Datacoves blog

Since its inception, dbt has served the needs of small to mid-sized companies to transform data into actionable insights. Now as larger enterprises look to adopt dbt, they must consider the unique complexities of large enterprises. These include integrating with existing systems behind a corporate firewall, orchestrating end-to-end data pipelines, and implementing DataOps processes to transform how teams collaborate across the organization.

This article compares three popular dbt development and deployment options: leveraging dbt Cloud, using dbt Core alone, or using a dbt Core managed platform other than dbt Cloud. To learn more about specific dbt Cloud features and how to implement them using dbt Core using other open-source tools checkout dbt Core vs dbt Cloud - Key Differences.

dbt Cloud for your enterprise data management platform

dbt Cloud is a hosted environment to develop and deploy dbt Core projects. dbt Cloud leverages dbt Core, the open-source data transformation framework we all know as dbt. dbt Cloud pricing starts at $100 per developer for teams or if you are a single developer, they offer a free single seat with  limited features.

dbt Cloud IDE for dbt-core development

When companies are ramping up with dbt, one of the hardest parts is setting up and managing dbt IDE environments. Analytic Engineers coming to dbt may not be familiar with concepts like version control with git or using the command line. The dbt Cloud IDE simplifies developer onboarding by providing a web-based SQL IDE to team members so they can easily write, test, and refine data transformation code without having to install anything on their computers. Complexities like starting a git branch are tucked behind a big colorful button so users know that is the first step in their new feature journey.

Developers who are accustomed to more versatile local IDEs, such as VS Code, may find the dbt Cloud experience limiting as they cannot leverage extensions such as those from the VS Code Marketplace nor can they extend dbt Core using the vast array of Python libraries. An example of a common library employed in the dbt community is SQLFluff. With SQLFluff, teams can ensure a minimum level of code quality is achieved across developers. 

dbt Cloud for DataOps

DataOps is a set of practices that combines data management and software engineering to improve the quality, speed, and collaboration of data analytics teams. In an enterprise environment, implementing DataOps processes early on in a project's lifecycle is crucial.

dbt Cloud can be paired with Continuous Integration (CI) tools like GitHub Actions to validate data transformations before they are added to a production environment.

If CI integration checks include things like SQLFluff and dbt-checkpoint, there is currently no way for users to run these validations in dbt Cloud prior to executing the automated CI checks in GitHub Actions. This may lead to a lot of back and forth between the GitHub Actions run failures and the dbt Cloud IDE to fix and commit the changes. Aspects such as code reviews and approvals will occur in the CI/CD tool of choice such as GitHub and dbt Cloud can report job execution status back to GitHub Actions. This allows teams to know when it is safe to merge changes to their code.

Companies that have tools like Bitbucket, Jira, and Jenkins within their corporate firewall may find it challenging to integrate with dbt Cloud.

dbt Cloud for deployment 

dbt Cloud makes deploying a dbt Core project simple. It allows you to define custom environment variables and the specific dbt commands (seed, run, test) that you want to run during production runs. The dbt Cloud scheduler can be configured to trigger at specific intervals using an intuitive UI.

dbt Cloud is primarily focused on running dbt projects. Therefore if a data pipeline has more dependencies, an external orchestration tool may be required. Cases where this is necessary include when you need to connect the Extract and Load step to the Transformation happening in dbt. Without connecting these steps, you have no assurance that the load is complete before the transformation starts. This can be mitigated with freshness checks, but without an explicit connection between the load step and the transformation, you may be delaying transformation unnecessarily.

Fortunately, if you do use an external orchestrator, dbt Cloud offers an API to trigger dbt Cloud jobs from your orchestrator. Another reason an external orchestrator may be needed in an enterprise is when there are additional steps to run after the dbt transformation. These may include sending data to downstream BI tools, marketing campaign tools, MDM systems, or refreshing machine learning models. dbt Cloud recently announced a webhooks API that would allow for triggering steps after transformation. However, if these endpoints reside within a corporate firewall, this may not be an option and an orchestrator like Airflow may be necessary.

dbt Cloud extensibility

The dbt Cloud IDE does not allow for the installation of Python libraries and dbt Cloud cannot create and orchestrate Python scripts that leverage tools like SQLFluff (this is coming to dbt Cloud later this year), dbt-coves, dbt-fal, Snowpark, Permifrost, or Streamlit. Additionally, the dbt Cloud IDE cannot be extended with extensions such as the official Snowflake or Databricks VS Code extensions.

While dbt Cloud can send notifications via email and Slack, it does not support other popular enterprise systems such as MS Teams.

dbt Cloud security 

dbt Cloud offers industry-standard SaaS security appropriate for any enterprise. At the enterprise level, it offers Single Sign-On and Role Based Access Controls. Enterprises can also choose to have dbt Cloud provisioned on a dbt Labs managed private cloud account. dbt Cloud is only offered as a managed SaaS solution on AWS and thus cannot be deployed to a company's existing private cloud account.

Who should consider using dbt Cloud

dbt Cloud is especially attractive for organizations with limited in-house technical resources or those seeking to minimize the time and effort spent on initial setup, maintenance, and upgrades. dbt Cloud runs in the browser and thus removes the need for manual installation and setup of dbt components, allowing your team to focus on data transformation tasks. dbt Cloud handles updates and upgrades for you, so it is simple to stay up-to-date and secure. dbt Cloud includes built-in scheduling capabilities, allowing you to automate dbt runs and testing without relying on external schedulers or orchestration tools.

dbt Cloud may introduce additional complexities in large enterprises with more complex orchestration needs or those that need to host the environment within their private cloud. Finally, the dbt Cloud IDE may feel constraining for users who prefer a VS Code environment. 

All these features can in turn reduce the time to value in setting up an end-to-end enterprise data management platform. It is possible to start out with a simple setup, but inevitably not accounting for the breadth of the entire platform may create technical debt and cause rework in the future.

Pros and cons of using dbt Cloud

Pros:

- Simplified setup and management of dbt environment

- Enhanced collaboration capabilities for data teams

- Integrated dbt job scheduling and monitoring

- Managed upgrades and updates

- Web-based IDE for streamlined development

- Metadata API

Cons:

- Reliance on a third-party platform

- May be more expensive for larger teams or organizations with complex requirements

- Not able to deploy within the corporate's private cloud

- Unable to leverage VS Code extensions and other Python libraries

- May require additional components or additional SaaS solutions for complex orchestrations

dbt Core for your enterprise data management platform

dbt Core is the heart of dbt and since it is open source, anyone can use it freely. That being said, an enterprise data management platform must account for additional requirements such as the development experience, deployment and scheduling dbt runs, and creating a DataOps process. This will impact the total cost of ownership and the platform's time to market.

IDE for dbt Core development

Leveraging dbt Core allows for ultimate flexibility, but it involves setting up a development environment on each team member's local machine or on a virtual environment such as an AWS workspace. Installation includes installing a popular dbt IDE like VS Code, installing dbt Core, configuring a connection to the data warehouse, and managing additional dependencies such asa compatible version of Python.

While the overall installation process for dbt is similar for both Windows and Mac users, there are some differences and potential gotchas to be aware of. Additionally, most organizations provide users with pre-configured computers and these systems may be delivered with software that conflicts with dbt Core. This complexity can lead to frustration and delays and may require assistance from senior members of the team, pulling them away from value-added work.

Once everything is installed, configuring dbt to connect to a data warehouse can also introduce complexity before a new team member can begin familiarizing themselves with the organization's dbt project structure and coding practices.

An enterprise setup of dbt should also include components that improve developer code quality and productivity. Popular IDEs like VS Code include a vast array of extensions that also improve the development experience. These include dbt Power User, VS Code extension for SQLFluff, and the dbt Snowflake extension.

Configuring a development environment can take anywhere from a few days to as long as a week. Depending on the person’s technical aptitude and experience with dbt. To improve this process, companies spend weeks or months dialing in the installation process and documenting it in knowledge base tools like Confluence or Notion. 

A development environment is not evergreen, it must be upgraded as new versions of dbt Core are released for added functionality or to fix bugs. Upgrading can be as error-prone as new installations and some companies opt not to keep their enterprise data platform current. This, however, is ill-advised because this type of technical debt only gets harder to remediate over time.

dbt Core for DataOps

When using dbt Core for your enterprise data platform, you will need to not only define and configure the automation scripts, but you will also need to ensure that all the components, such as a git server, CI server, CI runners, etc. are all working harmoniously together.

Since dbt Core runs within the corporate firewall, it can be integrated with any CI tool and internal components such as Jira, Bitbucket, and Jenkins. This flexibility comes at a price. To do this well, all the project dependencies must be packed into reusable Docker containers. Notifications will also need to be defined across the various components and all of this will take time and money.

dbt Core for deployment

When setting up a deployment environment, companies using dbt Core can leverage any orchestration tool, such as Airflow. They can connect steps prior to or after the dbt transformations and they can trigger any tool that exists within or outside the corporate network.

However, scaling tools like Airflow is not straightforward and may require knowledge of additional technologies like Kubernetes. By default, Airflow uses Python files and therefore, the team working with these orchestration scripts will need to also know Python on top of SQL.

A mature enterprise platform will also require multiple Airflow environments for development, testing, and production, adding complexity and increasing the total cost of ownership.

dbt Core extensibility 

Using dbt Core is inevitably the most flexible option, which is why many organizations choose to go this route. Companies can run dbt Core and leverage leading code editors like VS Code. They can install any additional Python dependencies, including running any proprietary code that is only available within the corporate firewall. They can also take advantage of the ever-growing VS Code extensions such as those for Snowflake or Databricks.

Teams using dbt Core can send notifications to Slack, MSTeams, or any other platform as long as they develop and maintain these solutions.

However, this extensibility will only increase the platform's total cost of ownership and the time it takes to deploy the end-to-end platform.

dbt Core security

One reason some enterprises choose to use dbt Core is that they are not able to leverage SaaS solutions due to compliance or data privacy requirements such as GDPR. By leveraging dbt Core, they can achieve ultimate security as dbt Core would run in and comply with corporate network policies.

Running dbt Core within the corporate firewall also eliminates the need for lengthy risk assessments. However, ensuring that security patches are applied is left to the organization. Managing Single Sign-on with tools like Airflow and setting up Role Based Access Controls are also things that will have to be solved when using dbt Core as a dbt enterprise data management platform.

Who should consider using dbt Core

A lot of the power and flexibility with using dbt Core comes from being able to customize the entire dbt experience. However, all of this comes with the expense of having to build a data management platform. It is not uncommon for organizations to take six months or up to a year to set up and test their custom dbt enterprise data management platform. They will also likely need to hire external consultants who have worked with a myriad of technologies and understand how to integrate them.

Building a platform is not for the faint of heart and since it is something custom to the organization, it will also increase the total cost of ownership. Some organizations get the basics in place just to make their teams productive, but eventually, they find that without proper design and implementation, they cannot take full advantage of the value of dbt and the rest of the modern data stack.

Using dbt Core as a dbt enterprise data platform can be a cost-effective option for smaller teams with limited requirements and budgets. It can also be good for those trying out dbt. Large organizations with stringent security requirements of sensitive data and strong technical platform teams may also leverage this option as it allows them to deploy in a way that is compliant with corporate policies.

As platform complexity grows, so does the cost and time needed to implement the end-to-end platform. Larger organizations should consider whether developing something custom is better than using a managed solution or taking shortcuts that can reduce the value of dbt and the modern data stack. 

Pros and cons of using dbt Core

Pros:

- Full control over the dbt environment and configurations

- No reliance on third-party services

- No subscription costs

- Able to meet corporate security requirements by installing within the corporate firewall

Cons:

- Requires technical expertise in defining the installation steps and documenting them

- May require hands-on involvement during setup and upgrades

- It can be time-consuming and challenging to maintain consistency across team members

- Upgrades and dependency management may be more complex compared to managed solutions

- Resolving installation issues can consume senior team members' time

- Total cost of ownership of the platform may be higher than using a managed solution

- Additional expertise will be needed to set up all the required technologies

- It will take longer to implement than a managed solution

Managed dbt Core for your enterprise data management platform

We have seen that dbt Cloud is a great dbt Core managed platform, but it has some limitations for large or complex deployments. On the other hand, using dbt Core alone gives organizations ultimate flexibility, but it comes at an added expense in terms of the time and cost that it takes to stand up and maintain a custom, one-of, enterprise data management platform.

What if you could combine the simplicity of dbt Cloud with the flexibility of dbt Core? A managed dbt Core platform, like Datacoves, can combine some of the best of both worlds. However, not all managed platforms are the same, below we discuss what to look for when considering a managed dbt Core enterprise data platform. Checkout our dbt pricing page for more pricing information.

Managed VS Code as a dbt IDE for development

By far, the tool most of the dbt enterprise users will interact with is the IDE. When considering a managed dbt Core platform, consider the developer experience. Will they have VS Code in the browser? Will you get a curated, best in class dbt IDE like VS Code with integrated libraries like SQLFluff and extensions that simplify the developer workflow? When required, will you be able to customize the developer environment with company specific Python libraries and extensions?

In a best-in-class developer setup, new users are onboarded in minutes with simple configuration screens that remove the need to edit text files such as profiles.yml and remove the complexity of creating and managing SSH keys. Version upgrades of dbt or any dependent library should be transparent to users.

Some platforms will also have added flexibility and let you leverage VS Code for non-dbt development tasks, such as opening a Jupyter notebook environment or a Python development environment with libraries such as Python Black.

Spinning up a pristine environment should be a matter of clicks. To test new dbt versions, you should be able to run your project in an isolated environment before switching all developers to the new version of dbt.

Managed dbt Core for DataOps

The advantage of a managed dbt Core platform when it comes to DataOps is that you do not have to worry about creating Docker images with all the dependencies you need as you perform your Continuous integration tests. Working with a partner that has experience with enterprise CI/CD platforms like Jenkins will also accelerate your time to value. A knowledgeable dbt Core managed platform partner will know best practices and guide you in setting up a world-class workflow that will scale with your enterprise. Developers should also be able to execute CI validations locally before pushing their changes to GitHub, thus reducing the back and forth to fix issues during the CI checks.

Some managed dbt Core platforms can run within your corporate firewall, allowing you to leverage existing tools like Jira, Bitbucket, Jenkins, or Gitlab. All this simplifies and accelerates the rollout of a dbt data management platform.

Managed dbt Core for deployment

Enterprises are complex, and as such, they must consider a flexible orchestration tool that has wide adoption, documentation and can scale to the needs of the company.

A mature enterprise dbt Core platform will include a full-fledged orchestrator such as Airflow. With Airflow, organizations can orchestrate pipelines that include Extract, Load, Transform, and Activation steps. Other aspects to consider is whether Airflow jobs can scale by leveraging technologies such as Kubernetes and whether it will be simple to send notifications to enterprise tools such as MS Teams.

Some dbt Core platforms that leverage dbt have also adopted mechanisms that simplify Airflow DAG creation by letting users create them using yml files vs creating traditional Python-based DAGs.

A complete enterprise data management platform will allow you to easily create multiple Airflow environments for development, testing, and production. It will also eliminate the burden of having to create Docker images with your dbt dependencies.

Managed dbt Core extensibility

An enterprise dbt Core platform will streamline the process of ramping up with dbt and it will offer the organization the flexibility it requires. This may include triggering AWS Glue jobs as well as Fivetran jobs. It may include triggering internal and external APIs or it may require a pre-configured development environment with Python libraries like Snowpark or Streamsets.

Managed dbt Core security

When it comes to security, different types of industries have different requirements. Regulated industries in Life Sciences or Finance may not want to or and unable to use SaaS solutions, so being able to deploy a dbt enterprise data management platform within the corporate firewall is a must.

Integrating with SSO solutions like Okta or PingID is also a must, and managing user's authorization using existing mechanisms like Active Directory simplifies compliance with corporate policies. 

Another area that may be overlooked when using open-source solutions is validating that libraries are up to date and there are no vulnerabilities introduced into the network. A robust dbt Core managed platform will be able to leverage internal registries like Artifactory, which are set up to scan for these issues. 

Finally, at enterprise scale, the dbt Core managed platform must have robust Role Based Access Controls to assure users have the appropriate level of access.

Who should consider using a managed dbt Core platform

Just like dbt Cloud, a reliable enterprise dbt Core data management platform will reduce the time to value. Teams will not have to figure out how to host and configure the multitude of tools and libraries and they will have a starting point for implementing best practices at the start of the project. 

By eliminating the need to stand up a platform, teams can focus on change management and new ways of working that will have the biggest impact on the organization. The total cost of ownership will be lower for the company since they do not have to build and constantly maintain their one-of platform. In contrast, they will benefit by working with a partner that works with other large organizations and looks for ways to improve and enhance the platform for everyone.

A managed dbt Core enterprise data platform is necessary for organizations that are not in the business of creating and managing complex technologies and those who need the flexibility that comes with complex enterprise tools and processes. When dbt Core must be deployed within the corporate firewall, an enterprise managed dbt data platform can also be a good option for companies with stringent security requirements to protect sensitive data.

Smaller organizations that want to leverage a managed VS Code environment or who want to implement DataOps from the outset can also realize the benefits of a SaaS managed dbt Core data platform. 

A managed dbt Core enterprise data platform gives companies the simplicity of dbt Cloud with the peace of mind of dbt Core. Best of all, if the platform leverages open-source technologies, there is no vendor lock-in.

Pros and Cons of using a managed dbt Core platform

Pros:

- Simplified developer onboarding

- Robust IDE such as VS Code

- Ability to configure a dbt environment

- Managed orchestrator such as Airflow

- Able to meet corporate security requirements by installing within the corporate firewall

- Requires a lot less technical expertise to set up and run

- Simplified dependency management across the development environment, CI/CD worker, and Airflow

- Lower total cost of ownership

Cons:

- Unable to have full control over the platform

- Reliance on third-party

- Licensing costs

Conclusion

Selecting between dbt Cloud, dbt Core, or a managed dbt Core enterprise platform comes down to the internal experience within an organization and their desire to focus on data vs developing and maintaining a custom dbt platform. The beauty of the dbt ecosystem is that the components are freely available and with enough time and money, anyone can do what they need. However, enterprises that want to move fast and leverage the full potential of dbt, should consider a managed dbt Core enterprise platform such as Datacoves to reduce the total cost of ownership and accelerate time to market. It is important to be aware of the dbt alternatives to make an informed decision.

Genesis of Datacoves and beyond

A few years ago, we identified a need to simplify how companies implement the Modern Data Stack, both from an infrastructure and process perspective. This vision led to the creation of Datacoves. Our efforts were recognized by Snowflake as we became a top-10 finalist in the 2022 Snowflake Startup Challenge. We have become trusted partners at Fortune 100 companies, and now we are ready for our next chapter.

Datacoves is thrilled to announce that we have been selected to be part of the Spring 2023 cohort at TinySeed. Their philosophy and culture align flawlessly with our values and aspirations. Our goal has always been to help our customers revolutionize the way their teams work with data, and this investment will enable us to continue delivering on that vision.

The TinySeed investment will also strengthen our commitment to supporting open-source projects within the dbt community, such as SQLFluff, dbt-checkpoint, dbt-expectations, and dbt-coves. Our contributions will not only benefit our customers but also the broader dbt community, as we believe open-source innovation enhances the tools and resources available to everyone.

We want to express our deep gratitude to Tristan Handy, CEO & Founder at dbt Labs and the entire dbt Labs team for partnering with us on the 2023 State of Analytics Engineering Community survey and for creating the amazing dbt framework. Without dbt, Datacoves would not be where it is today, and our customers would not enjoy the benefits that come with our solutions.

As we continue to grow and evolve, we look forward to finding new ways to collaborate with dbt Labs and the dbt community to further enhance how people use dbt and provide the best possible experience for everyone. Together, we can empower data teams around the world and revolutionize the way they work.

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In continuation of our previous blog discussing the importance of implementing DataOps, we now turn our attention to the tools that can efficiently streamline your processes. Additionally, we will explore real-life examples of successful implementations, illustrating the tangible benefits of adopting DataOps practices.

Which DataOps tools can help streamline your processes?

There are a lot of DataOps tools that can help you automate data processes, manage data pipelines, and ensure the quality of your data. These tools can help data teams work faster, make fewer mistakes, and deliver data products more quickly.

Here are some recommended tools needed for a robust DataOps process:

1. dbt (data build tool): dbt is an open-source data transformation tool that lets teams use SQL to change the data in their warehouse. dbt has a lightweight modeling layer and features like dependency management, testing, and the ability to create documentation. Since dbt uses version-controlled code, it is easy to see changes to data transformations (code reviews) before they are put into production.  dbt can dynamically change the target of a query's "FROM" statement on the fly and this allows us to run the same code against development, test, and production databases by just changing a configuration. During the CI process, dbt also lets us run only the changed transformations and their downstream dependencies.
2. Fivetran: Fivetran is an Extract and Load(EL) tool that has been gaining in popularity in recent years since it removes the complexity of writing and maintaining custom scripts to extract data from SaaS tools like Salesforce and Google Analytics.  By automating extracting data from hundred's of sources Fivetran removes complexity freeing data engineers to work on projects with a bigger impact. Finally, Fivetran has a robust API which allows you to save configurations done vie their UI for disaster recovery or to promote configurations form a development to a production environment.
3. Airbyte: Airbyte is another data-ingestion EL tool that is appealing because it is open source, requires little or no code, and is run by the community. It also makes it easier to extract and load data without having to do custom coding.  Airbyte also offers a connector development kit to help companies build custom connectors that may not be available. This allows companies to leverage most of the Airbyte functionality without too much work. There's also an API that can be used to retrieve configurations for disaster recovery.
4. SQLFluff: SQLFluff is an open-source SQL linter that helps teams make sure their SQL code is consistent and follows best practices. It gives you a set of rules that you can change to find and fix syntax errors, inconsistencies in style, and other common mistakes. Sqlfluff can be added to the CI/CD pipeline so that problems are automatically found before they are added to the codebase. By using a tool like SQLFluff, you can make sure your team follows a consistant coding style and this will help with long term project maintainability.
5. dbt-checkpoint: dbt-checkpoint provides validators to make sure your dbt projects are good. dbt is great, but when a project has a lot of models, sources, and macros, it gets hard for all the data analysts and analytics engineers to maintain the same level of quality. Users forget to update the columns in property (yml) files or add descriptions for the table and columns. Without automation, reviewers have to do more work and may miss mistakes that weren't made on purpose. Organizations can add automated validations with dbt-checkpoint, which makes the code review and release process better.
6. Hashboard: Hashboard is a business intelligence (BI) product built for data engineers to do their best work and easily spread the data love to their entire organizations. Hashboard has an interactive data exploration tool that enables anyone in an organization to discover actionable insights.
7. GitHub: GitHub offers a cloud-based Git repository hosting service. It makes it easier for people and teams to use Git for version control and to work together. GitHub can also run the workflows needed for CI/CD and it provides a simple UI for teams to perform code reviews and allows for approvals before code is moved to production.
8. Docker: Docker makes it easy for data teams to manage dependencies such as the versions of libraries such as dbt, dbt-checkpoint, SQLFluff, etc.. Docker makes development workflows more robust by integrating the development pipeline and combining dependencies simplifying reproducibility.

Examples of companies who have successfully implemented DataOps

DataOps has been successfully used in the real world by companies of all sizes, from small startups to large corporations. The DataOps methodology is based on collaboration, automation, and monitoring throughout the entire data lifecycle, from collecting data to using it. Organizations can get insights faster, be more productive, and improve the quality of their data. DataOps has been used successfully in many industries, including finance, healthcare, retail, and technology.

Here are a few examples of real-world organizations that have used DataOps well:

1. Optum: Optum is part of UnitedHealthcare. Optum prioritizes healthcare data management and analytics and when they wanted to implement new features and apps quickly, they turned to DataOps. DataOps helped Optum break down silos, saving millions of dollars annually by reducing compute usage. Optum managed data from dozens of sources via thousands of APIs, which was its biggest challenge. A massive standardization and modernization effort created a scalable, centralized data platform that seamlessly shared information across multiple consumers.
2. JetBlue: DataOps helped JetBlue make data-driven decisions. After struggling with an on-premises data warehouse, the airline migrated to the cloud to enable self-service reporting and machine learning. They've cleaned, organized, and standardized their data and leveraged DataOps to create robust processes. Their agility in data curation has enabled them to increase data science initiatives.
3. HubSpot: HubSpot is a leading company that makes software for inbound marketing and sales. It used DataOps to improve the use of its data. By using a DataOps approach, HubSpot was empowered to do data modeling the right way, to define model dependencies, and to update and troubleshoot models, which resulted in a highly scalable database and opened up new data application possibilities.
4. Nasdaq: Nasdaq, a global technology company that provides trading, clearing, and exchange technology, adopted DataOps to improve its data processing and analysis capabilities. They launched a data warehouse, products, and marketplaces quickly. After scalability issues, they moved to a data lake and optimized their data infrastructure 6x. The migration reduced maintenance costs and enabled analytics, ETL, reporting, and data visualization. This enabled better and faster business opportunity analysis.  
5. Monzo: Monzo is a UK-based digital bank that used DataOps to create a data-driven culture and improve its customer experience. By letting everyone make and look at the different data maps, they are helping teams figure out how their changes affect the different levels of their data warehouse. This gave the Monzo data team confidence that the data they give to end users is correct.

What is the future of DataOps adoption?

DataOps has a bright future because more and more businesses are realizing how important data is to their success. With the exponential growth of data, it is becoming more and more important for organizations to manage it well. DataOps will likely be used by more and more companies as they try to streamline their data management processes and cut costs. Cloud-based data management platforms have made it easier for organizations to manage their data well. Some of the main benefits of these platforms are that they are scalable, flexible, and cost-effective. With DataOps teams can improve collaboration, agility, and build trust in data by creating processes that test changes before they are rolled out to production.

With the development of modern data tools, companies can now adopt software development best practices in analytics. In today’s fast-paced world, it's important to give teams the tools they need to respond quickly to changes in the market by using high-quality data. Companies should use DataOps if they want to manage data better and reduct the technical debt created from uncontrolled processes. Putting DataOps processes in place for the first time can be hard, and it's easier said than done. DataOps requires a change in attitude, a willingness to try out new technologies and ways of doing things, and a commitment to continuous improvement. If an organization is serious about using DataOps, it must invest in the training, infrastructure, and cultural changes that are needed to make it work. With the right approach, companies can get the most out of DataOps and help their businesses deliver better outcomes.

At Datacoves, we offer a suite of DataOps tools to help organizations implement DataOps quickly and efficiently. We enable organizations to start automating simple processes and gradually build out more complex ones as their needs evolve. Our team has extensive experience guiding organizations through the DataOps implementation process.

Schedule a call with us, and we'll explain how dbt and DataOps can help you mature your data processes.

The amount of data produced today is mind-boggling and is expanding at an astonishing rate. This explosion of data has led to the emergence of big data challenges that organizations must overcome to remain competitive. Organizations must effectively manage data, guarantee its accuracy and quality, in order to derive actionable insights from it. This is where DataOps comes in. In this first post we go through an overview of DataOps and in our next post we will discuss tooling that support DataOps as well as discuss companies that have seen the benefit of implementing DataOps Processes.

What is DataOps?

DataOps is a methodology that merges DevOps principles from software development with data management practices. This helps to improve the process of developing, deploying, and maintaining data-driven applications. The goal is to increase the delivery of new insights and the approach emphasizes collaboration between data scientists, data engineers, data analysts, and analytics engineers.

Automation and continuous delivery are leveraged for faster deployment cycles. Organizations can enhance their data management procedures, decrease errors, and increase the accuracy and timeliness of their data by implementing DataOps.

The overarching goal is to improve an organization's capacity for decision-making by facilitating quicker, more precise data product deliveries. DataOps can speed up task completion with fewer errors by encouraging effective team collaboration, ultimately enhancing the organization's success.

DataOps is now feasible thanks to modern data stack tools. Modern data tools like Snowflake and dbt can enable delivery of new features more quickly while upholding IT governance standards. This allows for employee empowerment and improves their agility all while helping to create a culture that values data-driven decision-making. Effective DataOps implementation enables businesses to maximize the value of their data and build a data-driven culture for greater success.

The importance of DataOps in solving big data challenges

It is impossible to overstate the role that DataOps plays in overcoming big data challenges. Organizations must learn how to manage data effectively as it continues to increase if they want to stay competitive.

DataOps addresses these challenges by providing a set of best practices and tools that enable data teams to work more efficiently and effectively. Some of the key benefits of DataOps include:

1. Improved agility and flexibility: DataOps empowers data teams to work in an agile manner, enabling them to quickly adapt to shifting requirements and business needs. By basing decisions on up-to-the-minute information, businesses can stay one step ahead of the competition.
2. Increased collaboration: DataOps encourages collaboration between different teams involved in the data management process, including data engineers, data scientists, and business analysts. This leads to better communication, faster feedback loops, and more efficient workflows.
3. Enhanced quality and accuracy: DataOps lowers the risk of errors and inconsistencies in the data by automating data deliverable processing and analysis pipelines. As a result, businesses are better able to make decisions based on accurate and trustworthy data
4. Reduced costs: DataOps helps businesses save money by reducing the time and resources needed to manage and perform data analytics. As a result, they can invest in other aspects of their business to maintain their competitiveness.

In a world without DataOps, data-related processes can be slow, error-prone, and documentation is often outdated. DataOps provides the opportunity to rethink how data products are delivered. This promotes a culture of federated delivery. Everyone in the organization with the necessary skills is empowered to participate in insight generation.

We can achieve comparable results while preserving governance over the delivery process by learning from large, distributed open-source software development projects like the Linux project. To ensure high-quality output, a good DataOps process places an emphasis on rules and procedures that are monitored and controlled by a core team.

What are the components for successful DataOps implementation?

To implement DataOps well, one needs to take a holistic approach that brings together people, processes, and technology. Here are the five most important parts of DataOps that organizations need to think about in order to implement it successfully:

1. Collaborative Culture: Organizations must foster a collaborative culture and they must encourage cross-functional teamwork and make it easy for data scientists, data engineers, data analysts, and analytics engineers to talk to each other and have visibility into each other's work. Collaboration breaks down silos and makes it easier for everyone to understand how data-driven applications work. A collaborative culture also helps team members feel like they own their work and are responsible for it, which can help improve the quality of the data and the applications that use it.
2. Automation: Automation is a key part of DataOps because it helps organizations streamline their feature deliveries and cut down on mistakes. Data ingestion, data integration, and data validation are all examples of tasks that are done over and over again. By automating these tasks, organizations can free up their resources to focus on more complex and valuable tasks, like analyzing data and deriving insights from it.
3. Continuous Integration and Delivery: Continuous integration and delivery (CI/CD) are important parts of DataOps. Together they enable the deployment of working code to production in a continuous and iterative way. CI/CD can help speed up the time it takes to get data-driven apps to market so companies can respond quickly to changing business needs and provide more value to their customers.
4. Monitoring and Feedback: Monitoring and feedback are important parts of DataOps because they help make sure that data-driven applications work as they should. By keeping an eye on how well data-driven applications work, organizations can catch problems early and fix them as needed. By asking users for feedback, the code can be made more reliable and maintainable leading to better better outcomes.
5. Data Governance: Data governance is a set of policies, procedures, and controls that make sure data is managed correctly throughout its lifecycle. Data governance includes data ownership, data quality, data security, data privacy, and data lineage. By putting in place strong data governance practices, organizations can make sure that their data is correct, consistent, and in line with company standards. Data governance can help make the data more trustworthy and lower the chance of a data breach.

Having identified what needs to be done to achieve a good DataOps implementation, you may be wondering how we turn these ideas into actionable steps.

What are the best ways to implement DataOps in organizations?

1. Start Small and Grow: When putting DataOps in place, it's important to start small and grow gradually. This method lets companies try out and improve their DataOps processes before putting them to use on bigger projects. By starting out small, organisations can find potential problems and solve them before rolling them out to a larger audience.
2. Align DataOps with Business Goals: For an organization's DataOps implementation to work, its DataOps processes need to be in line with its business goals. Organizations should figure out what their most important business goals are for their data-driven applications, and then design their DataOps processes to help them reach these goals. This approach makes sure that DataOps is focused on adding value to the business and not just on implementing new technology.
3. Foster collaboration between teams: One of the most important parts of DataOps is getting teams to work together. Organizations need to help data scientists, data engineers, data analysts, and analytics engineers work together. This method breaks down silos and makes it easier for everyone to understand how data-driven applications work. Organizations can make it easier for people to work together by putting them on cross-functional teams, encouraging open communication, fostering transparency, and where possible encouraging the same tools, e.g. using dbt for data pipelines.
4. Invest in tools for automation. Automation is a key part of DataOps. Organizations should invest in automation tools that can help them speed up their data management processes, such as data ingestion, data integration, and data validation. Automation can help cut down on mistakes, increase productivity, and free up resources to be used on more complicated tasks, like analyzing data.
5. Implement CI/CD processes: Creating a robust feature release process with automated validations and code review will encourage collaboration and build transparency in the process. Users will be encouraged to move faster by removing the fear that a new feature will break production.
6. Monitor Performance and Get Feedback: Two important parts of DataOps are monitoring performance and getting feedback. Organizations should use monitoring tools that can keep track of how well data-driven applications are working and get feedback from users. This method helps find problems early on and makes the data more accurate and reliable. It also gives organizations useful information about how users leverage tools and this can help them improve the user experience.

Looking to implement DataOps quickly and efficiently?  

At Datacoves, we offer a comprehensive suite of DataOps tools to help organizations implement robust processes quickly and efficiently. One of the key benefits of using Datacoves is the ability to implement DataOps incrementally using our dynamic Infrastructure. Organizations can start by automating simple processes, such as data collection and cleaning, and gradually build out more complex processes as their needs evolve.

Our agile methodology allows for iterative development and continuous deployment, ensuring that our tools and processes are tailored to meet the evolving needs of our clients. This approach allows organizations to see the benefits of DataOps quickly, without committing to a full-scale implementation upfront. We have helped teams implement mature DataOps processes in as little as six weeks.

The Datacoves platform is being used by top global organizations and our team has extensive experience in guiding organizations through the DataOps implementation process. We ensure that you benefit from best practices and avoid common pitfalls when implementing DataOps.

Set aside some time to speak with us to discuss how we can support you in maturing your data processes with dbt and DataOps.

Don't miss out on our latest blog, where we showcase the best tools and provide real-life examples of successful implementations.

Get ready to take your data operations to the next level!

Big Data Analytics - Making information tasty and accessible

No one bakes for the sake of baking alone; cakes are meant to be shared.  If no one bought, ate, or gifted someone with their delicious chocolate cake, then there would be no bakers. The same is true for data analytics. Our goal as data practitioners is to feed our organization with the information needed to make decisions.  

If our cake doesn’t taste good or isn’t available when people want dessert, then it doesn’t matter that we made it from scratch. When it comes to big data, your goal should be to have the equivalent of a delicious cake - usable data - available when someone needs it.

Big Data Analytics - The ever-changing tastes of data consumers

Life would be simple if everyone were happy with a single flavor of cake. Metrics play a crucial role in our organization, and two of the most fundamental ones are ARR (Annual Recurring Revenue) and NRR (Net Recurring Revenue). These metrics are like chocolate and vanilla - they remain popular and relevant. Yet, these flavors alone are not enough. Just as with ice cream flavors, we eventually need to try something new, the same goes for insights. It's important to experiment and explore different perspectives.

When something is novel, we love it. When we first start baking, we are not consistent. But, over time, quality improves. We go from ok, to good, and to great. Even with our newfound expertise, the chocolate cake will become boring. We all want something new.  

With data, you often start with a simple metric. Having something is better than nothing, but that only lasts so long. We discover something new about our business and we translate that information into action. This is good for a while, but we will eventually see diminishing returns. While a LTV (Life Time Value) analysis may have a significant impact today, its usefulness is likely to diminish within a short period of time. Your stakeholders will crave something new, something more innovative and updated.

Someone is going to ask for a deep dive into how CAC (Customer Acquisition Cost) impacts LTV, or they might ask for a new kind of icing on that cake you just made. Either way, the point remains – as those around you start making use (or eating) of what you’re providing, they will inevitably ask for something more.  

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Big Data Analytics - How much expertise do you need to bake successfully?

It depends. Some organizations can do more than enough with the reports and dashboards built into their CRM, web analytics, or e-commerce system. This works for many companies. There is no need to spend extra time and money on more complex data systems when something simple will suffice. There is a reason that chocolate and vanilla are popular flavors; many people like their taste and know what they are getting with their order. The same can be said for your data infrastructure.

However, flexibility is the challenge. You are limited by your reporting options and the data analytics you can access. You can have a cake you can eat, but it’s going to look a certain way, having only one type of icing, and it certainly will not have any premium fillings.  

If you want those things, you need to look for something a bit more nuanced.  

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Big Data Analytics - Start with an Easy Bake Oven

How do you start to accommodate your organization’s new demand for analysis or your family’s newly refined palate? By using new tools and techniques.  

The Easy Bake Oven is the simplest first step – you get a pouch of ingredients, mix them, and within a few minutes, you have a cake. You might think it’s a children’s toy, but there are very creative recipes out there for the adventurous types. Unfortunately, you can only go so far; you’re limited by the size of the oven and the speed you can bake each item.  

The corollary in the data world is Excel – a fabulous tool, but something that has its limitations. While it is possible to extract data from your tools and manipulate it in a spreadsheet to make it more manageable, you are still limited by the pre-designed extracts offered by the vendor tools. Excel is flexible enough for many, but it is not a perfect end-state reporting solution for everyone.  

Eventually, you’ll need to address inconsistencies between systems and automate the process of data prep before it gets into your spreadsheet.

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Big Data Analytics - Using more advanced techniques to elevating your baking

We have a feel for the basics, but now we want to improve our process. There are certain things we do repeatedly, regardless of the recipes we’re making. We need to create an assembly line. Whether we’re calculating metrics or mixing the wet ingredients and the dry ingredients, there are steps we need to complete in a specific order and to a certain level of quality.  

We’ve moved on from the Easy Bake Oven and are now baking using a full-size oven. We need to be more careful about our measurements, ensure the oven is at the correct temperature, and write down notes about the various steps in our more complicated recipes. We need to make sure that different cakes, fillings, and decorations are ready at the same time, even if they have wildly different prep times. We need consistent results every time we make the cake, and we need others to be able to make the same recipe and achieve the same results by following our instructions.

Documenting and transferring this knowledge to others is difficult. Sure, you can write things down, but it is easy to skip a step that you take for granted. Perhaps your handwriting is hard to read, or someone is having trouble with the oven. You may be aware that using a hair dryer while baking can cause the circuit breaker to trip, but unless you document this information, others will not know this quirk. If your recipe has changed, such as using individual ingredients instead of a pre-made cake mix, it is important to clearly specify these adjustments, otherwise your friends and family may struggle to replicate your delicious cake.

There are plenty of data analytics tools out there that assist at this stage – Alteryx, Tableau Prep, and Datameer are just a few of many. In large enterprise organizations, you might find Informatica Power Center, Talend, or Matillion. These types of tools have graphical user interfaces (GUI); they give you the flexibility to extract data, load data, inspect data and transform it. Many enable you to define and calculate metrics. But they require you to work within each tool’s set of rules and constraints. This works well if you are starting and need something less complex.

The process that was once simple now is not; there are hidden assumptions, configurations, and requirements. You’re not using a pen-and-paper recipe anymore; now you’re working within a new system.  

GUI-based tools are great for companies whose workflows fit the way the systems work. But between the way some tools are licensed, and the skill needed to use them, they are only available to the IT organization. This leaves users to find shortcuts, develop workarounds, and become dependent on the business’ shadow IT. Inevitably, you’re going to run into maintainability issues.  

Recipes often have plenty of steps and ingredients; “data recipes” are no different. There are dependencies between operations, different run times for different transformations, different release cadences, and data availability SLAs. Your team might be able to manage your entire workflow quite well with one of these tools, but once your team starts introducing custom SQL logic or additional overlapping tools into the ecosystem, you introduce another layer of complexity. The result is an increase in the total cost of ownership.  

Often, this complexity is opaque, too. It is not obvious what that GUI component is doing, but they are strung together to build something usable. Over time, the complexity continues to grow; custom SQL logic is introduced, and more steps in the chain. Eventually, abstractions begin to form. The data engineers decide that these processing pipelines look quite similar and can be customized based on some basic configurations. Less overhead, more output.  

You’re now on the path of building custom ELT (Extract, Transform, and Load) pipelines, stitched together within the constraints of a GUI-based system. For some companies, this is okay, and it works. But there is a hidden cost – it is harder to maintain high quality inputs and outputs. The layers are tightly coupled and a mistake in one step is not caught until the whole pipeline is complete.  

IT may not be aware of downstream issues because they occur in other tools outside of their domain; one change here breaks something else there. This is like buying a ready-made cake mix and “enhancing it” with your custom ingredients. It works until it does not. One day, Duncan Hines changes their ingredients, and without you realizing, there is a bad reaction between your “enhancements” and the new mix.  Your once great recipe is not so great anymore, but there was no way for Duncan Hines to know. They expected you to follow their instructions; everything has been going according to plan until now.  

Even if your tool has strong version control built in, it’s often difficult to reverse the changes before it is too late. If your recipe calls for 1 cup of sugar but you accidentally add in 11, we don’t want to wait until the cake is baked to discover the error. We want to catch that mistake as soon as it happens.  

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Big Data Analytics - Building consistence into the baking process

Everything up to this point serves a specific profile of a business, but what happens when the business matures beyond what these tools offer? What happens when you know how to bake a cake, but struggle to consistently produce hundreds or thousands of the same quality cakes?

When we aim to expand our baking operations, it's crucial to maintain consistency among bakers, minimize accidental mistakes, and have the ability to swiftly recover from any errors that occur. We need enough mixers and ovens to support the demand for our cakes, and we need an organized pantry with the correct measuring cups and spoons. We need to know which ingredients are running low, which are delayed, and which have common allergens.  

In data analytics, we have our own supply chain, often called ELT. You will find tools like Airbyte and Fivetran are common choices for bringing in our data “ingredient delivery”. They manage data extraction and ingestion so you can skip the manual CSV downloads that once served you so well.

We want to ensure quality, have traceability, document our process, and successfully produce and deliver our cakes. To do this, we need a repeatable process, with clear sequential steps. In the world of baking, we use recipes to achieve this.  

All baking recipes have a series of steps, some of which are common across different recipes. For example, creaming eggs and sugar, combining the wet and dry ingredients, and whipping the icing are repeatable steps that, when performed in the correct order, result in a delicious dessert. Recipes also follow a standard format: preheat the oven to the specified temperature, list the ingredients in the order in which they are used, and provide the preparation steps last. The sequence is intentional and provides a clear understanding of what to expect during the baking process.

We can apply this model to our data infrastructure by using a tool called dbt (data build tool). Instead of repeating miscellaneous transformation steps in various places, we can centralize our transformation logic into reusable components. Then we can reference those components throughout our project. We can also identify which data is stale, review the chain of dependencies between transformations, and capture the documentation alongside that logic.

We no longer need a GUI-driven tool to review our data; instead, we use the process as defined in the code to inform our logic and documentation. Our new teammate can now confidently create her cake to the same standard as everyone else; she can be confident that she is avoiding common allergens, too.

Better yet, we have a history of changes to our process and “data recipe”. Version control and code reviews are an expectation, so we know when modifications to our ingredient list will cause a complete change in the final product. Our recipes are no longer scattered, but part of a structured system of reusable, composable steps.  

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Big Data Analytics - Are you ready to bake well, consistently?

Maturing the way we build data processes comes down to our readiness. When we started with our Easy Bake Oven, little could go wrong, but little could be tweaked. As we build a more robust system, we can take advantage of its increased flexibility, but we also need to maintain more pieces and ensure quality throughout a more complex process.

We need to know the difference between baking soda and baking powder. Which utensils are best, and which oven cooks most evenly? How do we best organize our new suite of recipes? How do you set up the kitchen and install the appliances? There are many decisions to make, and not every organization is ready to make them. All this can be daunting for even large organizations.

But you don’t have to do things all at once. Many organizations choose to make gradual improvements, transforming their big data process from disorganized to consistent.

You can subscribe to multiple SaaS services like Fivetran or Airbyte for data loading and use providers like dbt Cloud for dbt development. If your work grows increasingly complex, you can make use of another set of tools (such as Astronomer or Dagster) to orchestrate your end-to-end process.  You will still need to develop the end-to-end flow, so what you gained in flexibility you have lost in simplicity.

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Big Data Analytics - Becoming a master baker quickly

This is what we focus on at Datacoves. We aim to help organizations create mature processes, even when they have neither the time nor resources to figure everything out.

We give you “a fully stocked kitchen” - all the appliances, recipes, and best practices to make them work cohesively. You can take the guess work out of your data infrastructure, and instead, use a suite of tools designed to help your team perform timely and efficient analytics.

Whether your company is early in its data analytics journey or ready to take your processes to the next level, we are here to help. If your organization has strong info-sec or compliance requirements, we can also deploy within your private cloud. Datacoves is designed to get you “baking delicious cakes” as soon as possible.

Set aside some time to speak with me and learn how Datacoves has helped both small and large companies deploy mature analytics processes from the start. Also check out our case studies to see some of our customer's journeys.

Imagine yourself baking the next great cake at your organization. You can do it quickly with our help.

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When you are learning to use a new tool or technology, one of the hardest things is learning all the new terminology. As we pick up language throughout our lives, we develop an association between words and our mental model of what they represent. The next time we see the word again that picture pops up in our head and if the word is now being used to mean something new, we must create a new mental model. . In this post, we introduce some core dbt (data building tool)terminology and how it all connects.

Language understanding is interesting in that once we have a mental model of a term, we have a hard time grasping the new association. I still remember the first time I spoke to someone about the Snowflake Data Warehouse, and they used the term warehouse. To me, the term had two mental models. One was a place where we store a lot of physical goods, type Costco Warehouse into Google and the first result is Costco Wholesale, a large retailer in the US that is so big it is literally a warehouse full of goods.

I have also worked in manufacturing, so I also associated a warehouse as the place where raw materials and finished goods are stored.

In programming, we would say we are overloading the term warehouse to mean different things.

In some programming languages, function overloading or method overloading is the ability to create multiple functions of the same name with different implementations – Wikipedia

 We do this type of thing all the time and don’t think twice about it. However, if I say “I need a bass” do you know what I am talking about?

In my Snowflake example, I knew the context was technology and more specifically something to do with databases, so I already had a mental model for a warehouse. It’s even in Wikipedia's description of the company.

Snowflake Inc. is a cloud computing-based data warehousing company - Wikipedia

I knew of data warehouses from Teradata and Amazon (Redshift), so it was natural for me to think of a warehouse as a technology and a place where lots of data is stored. In my mind, I quickly thought of

• The Redshift Warehouse
• The Teradata Warehouse
• The Snowflake Warehouse

For those new to the term warehouse, I may have lost you already.  Maybe you are new to dbt and you come from the world of tools like Microsoft Excel, Alterix, Tableau, and PowerBI. If you know all this, grant me a few minutes to bring everyone up to speed.

Let’s step back and first define a database.

A database is an organized collection of structured information, or data, typically stored electronically in a computer system - Oracle

Ok, you probably know Excel. You have probably also seen an Excel Workbook with many sheets. If you organize your data neatly in Excel like the image below, we could consider that workbook a database.

Going back to the definition above “organized collection of structured information” you can see that we have structured information, a list of orders with a Date, Order Quantity, and Order Amount. We also have a collection of these, namely Orders and Invoices.

In database terms, we call each Excel sheet a table and each of the columns an attribute.

Now back to a warehouse. This was my mental model of a warehouse.

A data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise - Wikipedia

Again, if you are new to all this jargon, the above definition might not make much sense to you. Going back to our Excel Example. In an organization, you have many people with their own “databases” like the example above. Jane has one, Mario has another, Elena has a third. All have some valuable information we want to combine in order to make better decisions. So instead of keeping these Excel workbooks separately, we put them all together into a database and now we call that a warehouse. We use this central repository for our “business intelligence”

So, knowing all of this, when I heard of a Snowflake warehouse the above is what I thought. It is the place where we have all the data, duh. Just like Redshift and Teradata.  But look at what the people at Snowflake did, they changed the meaning on me.

A virtual warehouse, often referred to simply as a “warehouse”, is a cluster of compute resources in Snowflake. - Snowflake

The term warehouse here is no longer about the storage of things it now means “cluster of compute” A what of what?

Ok, let’s break this down. You are probably reading this on a laptop or some other mobile device. That device stores all your documents and when you perform some actions it “computes” certain things.  Well, in Snowflake the storage of the information is separate and independent of the computation on the things that are stored.  So, you can store things once and connect different “computers” to it. Imagine you were performing a task on your laptop, and it was slow. What if you could reach in your desk drawer, pull out a faster computer, and speed up the task that was slow, well, in Snowflake you can. Also, instead of just having one computer doing the work, they have a cluster of computers working together to get the job done even faster.

As you can see, language is tricky, and creating a shared understanding of it is crucial to advancing your understanding and mastery of the technology. Every Snowflake user develops the new mental model for a warehouse and using it is second nature, but we forget that these terms that are now natural to us may still be confusing to newcomers.

Understanding dbt (data build tool) terminology

Let’s start with dbt. When you join the dbt Slack community you will inevitably learn that the preferred way to write dbt is all lower case. Not DBT, not Dbt, just dbt.  I still don’t know why exactly, but you may have noticed that everyone in this space always puts “dbt (Data Build Tool)”

If you have some knowledge of Behavioral Therapy you may already know that DBT has a different meaning. Dialectical behavior therapy (DBT)

Dialectical behavioral therapy (DBT) is a type of cognitive-behavioral therapy. Cognitive-behavioral therapy tries to identify and change negative thinking patterns and pushes for positive behavioral changes.

Did you notice how they do the inverse? They spell out Dialectical behavior therapy and put DBT in parenthesis. So, maybe the folks at Fishtown Analytics, now dbt Labs came across this other meaning for DBT and chose to differentiate by using lowercase, or maybe it was to mess with all of the newbies lol. 

So update your auto-correct and don’t let dbt become DBT or Dbt or you will hear from someone in the community, haha.

Now let’s do a quick rundown of terms you will hear in dbt land which may confuse you as you start your dbt journey. I will link to the documentation with more information. My job here is to hopefully create a good mental model for you, not to teach you all the ins and outs of all of these things.

Seed or dbt seed

This is simply some data that you put into a file and make it part of your project. You put it in the seeds folder within your dbt project, but don’t use this as your source to populate your data warehouse, these are typically small files you may use as lookup tables. If you are using an older version of dbt, the folder would be data instead of seeds. That was another source of confusion, so now the term seed and the directory seed are more tightly connected. The format of these files must be CSV, more information can be found via the link above.  

Jinja

Jinja is a templating engine with syntax similar to the Python programming language that allows you to use special placeholders in your SQL code to make it dynamic. The stuff you see with {{ }} is Jinja.

Without Jinja, there is no dbt. I mean it is the combination of Jinja with SQL that gives us the power to do things that would otherwise be very difficult. So, when you see the lineage you get in the dbt documentation, you can thank Jinja for that.

dbt macro

I knew you would have this question. Well, a macro is simply a reusable piece of code. This too adds to the power of dbt. Every newcomer to dbt will quickly learn about the ref and source macros. These are the cornerstone of dbt. They help capture the relationship and sequence of all your data transformations. Sometimes you are using macros and you may not even realize it. Like the not_null test in your yml file, that’s a macro.

Behind the scenes, dbt is taking information in your yml file and sending parameters to this macro. In my example, the parameter model gets replaced with base_cases (along with the database name and schema name) and colum_name gets replaced with cases. The compiled version of this test looks like this:

There are dbt packages like dbt-expectations that extend the core dbt tests by adding a bunch of test macros, so check it out.

dbt package

What do you do when you have a lot of great macros that you want to share with others in the community? You create a dbt package of course.

But what is a dbt package? A package is simply a mini dbt project that can be incorporated into your dbt project via the packages.yml file.  There are a ton of great packages and the first one you will likely run into is dbt-utils. These are handy utilities that will make your life easier. Trust me, go see all the great things in the dbt-utils package.

Packages don’t just have macros though. Remember, they are mini dbt projects, so some packages incorporate some data transformations to help you do your analytics faster. If you and I both need to analyze the performance of our Google Ads, why should we both have to start from scratch?  Well, the fine folks over at Fivetran thought the same thing and created a Google Ads package to help.

When you run the command dbt deps, dbt will look at your packages.yml file and download the specified packages to the dbt_packages directory of your dbt project. If you are on an older version of dbt, packages will be downloaded to the dbt_modules directory instead, but again you can see how this could be confusing hence the updated directory name.

There are many packages and new ones arrive regularly. You can see a full listing on dbt hub.

dbt hub

This is the website maintained by dbt Labs with a listing of dbt packages.  

As a side note, we at Datacoves also maintain a similar listing of Python libraries that enhance the dbt experience in our dbt Libraries page. Check out all the libraries that exist. From additional database adapters to tools that can extract data from your BI tool and connect it with dbt, there’s a wealth of great open-source projects that take dbt to another level. Keep in mind that you cannot install Python libraries on dbt Cloud.

dbt models

These are the SQL files you find in the models directory. These files specify how you want to transform your data. By default, each of these files creates a view in the database, but you can change the materialization of a model to something else and for example, have dbt create a table instead.

Materialization

 Materializations define what dbt will do when it runs your models.  Basically, when you execute dbt run this is what happens.

1. dbt reads all your files
2. dbt then compiles the models by replacing the jinja code with the “real” code the database will run e.g. {{ ref(“my_model”) }} becomes my_database.my_schema.my_model
3. Finally, it wraps the compiled code in the specified materialization, which by default is a view

All the code that dbt compiles and runs can be found in the dbt target directory

Target

This term can be ambiguous to a new dbt user. This is because in dbt we use it interchangeably to mean two different things. As I used it above, I meant the directory within your dbt project where dbt commands write their output. If you look in this directory, you will see the compiled and run directories where I found the code I showed above.

Now that you know what dbt is doing under the hood, you can look in this directory to see what will be executed in the database. When you need to do some debugging, you should be able to take code directly from the compiled directory and run it on your database.

dbt target

This is the other meaning for target. It refers to where dbt will create/materialize the objects in your database.

Again, dbt first compiles your model code and creates the files in the compiled directory. It then wraps the compiled code with the specified materialization and saves the resulting code in the run directory. Finally, it executes that code in your database target. It is the final file in the run directory that is executed in your database.

The image above is the code that runs in my Snowflake instance.

But how does dbt know which database target to use? You told it when you set up your dbt profile which is normally stored in a folder called .dbt in your computer's home folder (dbt Cloud and Datacoves both abstract this complexity for you).

dbt Profile

When you start using dbt, you learn of a file called profiles.yml This file has your connection information to the database and should be kept secret as it typically contains your username and password.

This file is called profiles, plural, because you can have more than one profile which you eventually realize is where the target database is defined.  Here is a case where you can argue that a better name for this file is targets.yml, but you will learn later why the name profiles.yml was probably chosen and why this name makes sense.

Notice above that I have two different dbt targets defined below the word outputs, dev and prd.  dbt can only work on one target at a time so if you want to run dbt against two different databases you can specify them here. Just copy the dev target, give it a new name, and change some of the parameters.

Think of the word outputs on line 3 above as targets.  Notice in line 2 the line target: dev this tells dbt which target it should use as your default. In my case, unless I specify otherwise, dbt will use the dev target as my default connection. Hence it will replace the Jinja ref macro with my development database.

How would you use the other target? You simply pass the target parameter to the dbt command like

dbt run --target prd or dbt run -t prd

What is that default: thing on the first line of my profiles.yml file?

Well you see, that’s the name given to your dbt profile, which by default is well, default.

dbt project

The dbt project is what is created when you create a project via the dbt init command. It includes all of the folders you typically associate with a dbt project and includes a configuration file called dbt_project.yml. If you look at your dbt_project.yml file, you will find something similar to this.

In line 10 you can see which profile dbt will look for in your profiles.yml file. If I change that line and try to run dbt, I will get an error.

NOTE: For those paying close attention, you may have seen I used-s and not -m when selecting a specific model to run.  This is the new/preferred way to select what dbt will run.

So now you see why profiles.yml is called profiles.yml and not targets.yml, because you can have multiple profiles in the file. In practice, I think people normally only have one profile, but nothing is preventing you from creating more and it might be handy if you have multiple dbt projects each with different connection information.

Those smart folks at Fishtown Analytics build in this flexibility for a very specific use case. You see, they were originally an analytics consulting company and developed dbt to help them do their work more efficiently. You can imagine that they were working with multiple clients whose project timelines overlapped so by having multiple profiles they could point each independent dbt project to a different profile in the profiles.yml file with each client's database connection information. Something like this.

Now that I have a profile called company_a in my profiles.yml that matches what I defined in my dbt_project.yml dbt will run correctly.

Conclusion

There is a ton of stuff to learn in your dbt journey and starting out with a solid foundation can help you better communicate and quickly progress through the learning curve.

Fishtown Analytics, now dbt Labs, created dbt to meet a real need they had and some of their shared vocabularies made it into the names we now use in the community. Those of us who have made it past the initial learning curve sometimes forget how daunting all the terminology can be for a newcomer.

There is a wealth of information you can find in the dbt documentation and our own dbt cheat sheet, but it takes some time to get used to all the new terms and understand how it's all connected. So next time you come across a newbie, think about the term that you are about to use and the mental model they will have when you tell them to update the seed. We need to take our new dbt seeds (people) and mature them into strong trees.

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In 3 Core Pillars to a Data-Driven Culture, I discussed the reasons why decision makers don’t trust analytics. I then outlined the alignment and change management aspect to any solution. Once you know what you want, how do you deliver it? The cloud revolution has brought in a new set of challenges for organizations which have nothing to do with delivering solutions. The main problem is that people are faced with a Cheesecake Factory menu and most people would be better served with Omakase.

For those who may not be aware, The Cheesecake Factory menu has 23 pages and over 250 items to choose from. There are obviously people who want the variety and there is certainly nothing wrong with that, but my best meals have been where I have left the decision to the chef.

Omakase, in a Japanese restaurant is a meal consisting of dishes selected by the chef, it literally means “I'll leave it up to you”

How does this relate to the analytics landscape? Well, there is a gold rush in the analytics space. There is a lot of investment and there are literally hundreds of tools to choose from. I have been following this development over the last five years and if anything, the introduction of tools has accelerated.

Most people are where I was back in 2016. While I have been doing work in this space for many years the cloud and big data space was all new to me. There was a lot I needed to learn and I was always questioning whether I was making the right decision. I know many people today who do POC after POC to see which tool will work the best, I know, I did the same thing.

Contrast this process with my experience learning a web development framework called Ruby on Rails. When I started learning Rails in 2009 I was focused on what I was trying to build, not the set of tools and libraries that are needed to create a modern web application. That’s because Rails is Omakase.

When you select Omakase in Rails you are trusting many people with years of experience and training to share that knowledge with you. Not only does this help you get going faster, but it also brings you into a community of like-minded people. So that when you run into problems, there are people ready to help. Below I present my opinionated view of a three-course meal data stack that can serve most people and the rationale behind it. This solution may not be perfect for everyone, but neither is Rails. 

Appetizer: Loading data

You are hungry to get going and start doing analysis, but we need to start off slowly. You want to get the data, but where do you start. Well, there are a few things to consider.

·     Where is the data coming from?

·     Is it structured into columns and rows or is it semi-structured(JSON)?

·     Is it coming in at high velocity?

·     How much data are you expecting?

What I find is that many people want to over engineer a solution or focus on optimizing for one dimension which is usually cost since that is simple to grasp. The problem is that if you focus only on cost, you are giving up something else, usually a better user experience. You don’t have a lot of time to evaluate solutions and build extract and load scripts, so let me make this simple. If you start with Snowflake as your database and Fivetran as your Extract and Load solution, you’ll be fine. Yes, there are reasons why not to choose those solutions, but you probably don’t need to worry about them, especially if you are starting out and you are not Apple.

Why Snowflake you ask? Well, I have used Redshift, MS SQLServer, Databricks, Hadoop, Teradata, and others, but when I started using Snowflake I felt like a weight was lifted. It “just worked.” Do you think you will need to mask some data at some point? They have dynamic data masking. Do you want to be able to scale compute or storage independently? They have separate compute and storage too. Do you like waiting for data vendors to extract data from their system and then having to import it on your side? Or do you need to collaborate with partners and send them data? Well,Snowflake has a way for companies to share data securely, gone are the days of moving data around, now you can securely grant access to groups within or outside your organization, simple, elegant. What about enriching your data with external data sources? Well, they have a data marketplace too and this is bound to grow. Security is well thought out too and you can tell they are focused on the user experience because they do things to improve analyst happiness like MATCH_RECOGNIZE. Oh, and they also handle structured and semi-structured data amazingly well and all without having to tweak endless knobs. With one solution I have been able to eliminate the need to answer the questions above because Snowflake can very likely handle your use case regardless of the answer. I can go on and on, but trust me, you’ll be satisfied with your Snowflake appetizer. If it’s good enough for Warren Buffett, it’s good enough for me.

But what about Fivetran you say? Well, because you have better things to do than to replicate data from Google Analytics, Salesforce, Square, Concur, Workday, Google Ads, etc. etc. Here’s the full list of current connectors Fivetran supports. Just set it and forget it. No one will give you a metal for mapping data from standard data sources to Snowflake. So just do the simple thing and let’s get to the main dish. 

Main dish: Transforming data

Now that we have all our data sources in Snowflake, what do we do? Well, I haven’t met anyone who doesn’t want to do some level of data quality, documentation, lineage for impact analysis, and do this in a collaborative way that builds trust in the process.

I’ve got you covered. Just use dbt. Yup, that’s it, simple, a single tool that can do documentation, lineage, data quality, and more. dbt is a key component in our DataOps process because it, like Snowflake, just works. It was developed by people who were analysts themselves and appreciated software development best practices like DRY. They knew that SQL is the great common denominator and all it needed was some tooling around it. It’s hard enough finding good analytics engineers let alone finding ones that know Python. Leave the Python to Data Science and first build a solid foundation for your transformation process. Don’t worry, I didn’t forget about your ambition to create great machine learning models, Snowflake has you covered there as well, check out Snowpark.

You will need a little more than dbt in order to schedule your runs and bring some order to what otherwise would become chaos, but dbt will get you a long way there and if you want to know how we solve this with our Datacoves, reach out, we’ll share our knowledge in our 1-hour free consultation.

Dessert: Reporting on data

This three-course meal is quickly coming to an end, but I couldn’t let you go home before you have dessert. You need dashboards, but you also want self-service, then you can’t go wrong with Looker. I am not the only chef saying this, have a look at this.

One big reason for choosing Looker in addition to the above is the fact that version control is part of the process. If you want things that are documented, reused, and follow software development best practices, then you need to have everything in version control. You can no longer depend on the secret recipe that one of your colleagues has on their laptops. People get promoted, move to other companies, forget… and you need to have a data stack that is not brittle. So choose your dessert wisely.

Conclusion 

There are a lot of decisions to be made when creating a great meal. You need to know your guests dietary needs, what you have available, and how to turn raw ingredients into a delicious plate. When it comes to data the options and permutations are endless and most people need to get to delivering solutions so decision makers can improve business results. While no solution is perfect, in my experience there are certain ingredients that when put together well enable users to get to building quickly. If you want to deliver analytics your decision makers can trust, just go Omakase.

In our previous article we wrote about the various dbt tests, we talked about the importance of testing data and how dbt, a tool developed by dbt Labs, helps data practitioners validate the integrity of their data. In that article we covered the various packages in the dbt ecosystem that can be used to run a variety of tests on data. Many people have legacy ETL processes and are unable to make the move to dbt quickly, but they can still leverage the power of dbt and by doing so slowly begin the transition to this tool. In this article, I’ll discuss how you can use dbt to test and document your data even if you are not using dbt for transformation.

Why dbt?

Ideally, we can prevent erroneous data from ever reaching our decision makers and this is what dbt was created to do. dbt allows us to embed software engineering best practices into data transformation. It is the “T” in ELT (Extract, Load, and Transform) and it also helps capture documentation, testing, and lineage. Since dbt uses SQL as the transformation language, we can also add governance and collaboration via DataOps, but that’s a topic for another post.

I often talk to people who find dbt very appealing, but they have a lot of investment in existing tools like Talend, Informatica, SSIS, Python, etc. They often have gaps in their processes around documentation and data quality and while other tools exist, I believe dbt is a good alternative and by leveraging dbt to fill the gaps in your current data processes, you open the door to incrementally moving your transformations to dbt,   

Eventually dbt can be fully leveraged as part of the modern data workflow to produce value from data in an agile way. The automated and flexible nature of dbt allows data experts to focus more on exploring data to find insights.

Why ELT?

The term ELT can be confusing, some people hear ELT and ETL and think they are fundamentally the same thing. This is muddied by marketers who try to appeal to potential customers by suggesting their tool can do it all. The way I define ELT is by making sure that data is loaded from the source without any filters or transformation. This is EL (Extract and Load). We keep all rows and all columns. Data is replicated even if there is no current need. While this may seem wasteful at first, it allows Analytic and Data Engineers to quickly react to business needs. Have you ever faced the need to answer a question only to find that the field you need was never imported into the data warehouse? This is common especially in traditional thinking where it was costly to store data or when companies had limited resources due to data warehouses that coupled compute with storage. Today, warehouses like Snowflake have removed this constraint so we can load all the data and keep it synchronized with the sources. Another aspect of modern EL solutions is making the process to load and synchronize data simple. Tools like Fivetran and Airbyte allow users to easily load data by simply selecting pre-build connectors for a variety of sources and selecting the destination where the data should land.  Gone are the days of creating tables in target data warehouses and dealing with changes when sources add or remove columns. The new way of working is helping users set it and forget it.

Plugging in dbt for testing

In an environment where other transformation tools are used, you can still leverage dbt to address gaps in testing. There are over 70 pre-built tests that can be leveraged, and custom tests can be created by just using SQL. dbt can test data anywhere in the transformation lifecycle. It can be used at the beginning of the workflow to test or verify assumptions about data sources and the best part is that these data sources or models do not need to be a part of any ongoing project within dbt. Imagine you have a raw customer table you are loading into Snowflake. We can connect this table to dbt by creating a source yml file where we tell dbt where to find the table by providing the name of the database, schema, and table. We can then add the columns to the table and while we are at it, we can add descriptions. 

The image below illustrates how test would be added for a CUSTOMER table in the SNOWFLAKE_SAMPLE_DATA database in the TPCH_SF100 schema.‍

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Testing non-source tables

So far we have done what you would learn on a standard dbt tutorial, you start with some source, connect it to dbt, and add some tests. But the reality is, dbt doesn’t really care if the table that we are pointing to is a true "source" table or not.  To dbt, any table can be a source, even an aggregation, reporting table, or view.  The process is the same. You create a yml file, specify the “source” and add tests.

Let’s say we have a table that is an aggregate for the number of customers by market segment. We can add a source that points to this table and check for the existence of specific market segments and a range of customers by segment.

Using this approach, we can leverage the tests available in dbt anywhere in the data transformation pipeline. We can use dbt_utils.equal_rowcount to validate that two relations have the same number of rows to assure that a transformation step does not inadvertently drop some rows. 

When we are aggregating, we can also check that the resulting table has fewer rows than the table we are aggregating by using the dbt_utils.fewer_rows_than test.

Notice that you can use the source macro when referring to another model outside of dbt. As long as you register both models as sources, you can refer to them. So when you see documentation that refers to the ref() macro, just substitute with the source macro as I did above.

Also, note that even though documentation may say this is a model test, you can use this in your source: definition as I have done above.

Documenting tables

In dbt sources, we can also add documentation like so:

These descriptions will then show up in the dbt docs.

Conclusion

dbt is a great tool for transforming data, capturing documentation, and lineage, but if your company has a lot of transformation scripts using legacy tools, the migration to dbt may seem daunting and you may think you cannot leverage the benefits of dbt. 

By leveraging source definitions you can take advantage of dbt’s ecosystem of tests and ability to document even if transformations are done using other tools.

Gradually the organization will realize the power of dbt and you can gradually migrate to dbt.  For the data to be trusted, it needs to be documented and tested and dbt can help you in this journey.