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The top dbt alternatives include Datacoves, SQLMesh, Bruin Data, Dataform, and visual ETL tools such as Alteryx, Matillion, and Informatica. Code-first engines offer stronger rigor, testing, and CI/CD, while GUI platforms emphasize ease of use and rapid prototyping. Teams choose these alternatives when they need more security, governance, or flexibility than dbt Core or dbt Cloud provide.
The top dbt alternatives include Datacoves, SQLMesh, Bruin Data, Dataform, and GUI-based ETL tools such as Alteryx, Matillion, and Informatica.
Teams explore dbt alternatives when they need stronger governance, private deployments, or support for Python and code-first workflows that go beyond SQL. Many also prefer GUI-based ETL tools for faster onboarding. Recent market consolidation, including Fivetran acquiring SQLMesh and merging with dbt Labs, has increased concerns about vendor lock-in, which makes tool neutrality and platform flexibility more important than ever.
Teams look for dbt alternatives when they need stronger orchestration, consistent development environments, Python support, or private cloud deployment options that dbt Cloud does not provide.
Organizations evaluating dbt alternatives typically compare tools across three categories. Each category reflects a different approach to data transformation, development preferences, and organizational maturity.
Organizations consider alternatives to dbt Cloud when they need more flexibility, stronger security, or support for development workflows that extend beyond dbt. Teams comparing platform options often begin by evaluating the differences between dbt Cloud vs dbt Core.
Running enterprise-scale ELT pipelines often requires a full orchestration layer, consistent development environments, and private deployment options that dbt Cloud does not provide. Costs can also increase at scale (see our breakdown of dbt pricing considerations), and some organizations prefer to avoid features that are not open source to reduce long-term vendor lock-in.
This category includes platforms that deliver the benefits of dbt Cloud while providing more control, extensibility, and alignment with enterprise data platform requirements.
Datacoves provides a secure, flexible platform that supports dbt, SQLMesh, and Bruin in a unified environment with private cloud or VPC deployment.
Datacoves is an enterprise data platform that serves as a secure, flexible alternative to dbt Cloud. It supports dbt Core, SQLMesh, and Bruin inside a unified development and orchestration environment, and it can be deployed in your private cloud or VPC for full control over data access and governance.
Benefits
Flexibility and Customization:
Datacoves provides a customizable in-browser VS Code IDE, Git workflows, and support for Python libraries and VS Code extensions. Teams can choose the transformation engine that fits their needs without being locked into a single vendor.
Handling Enterprise Complexity:
Datacoves includes managed Airflow for end-to-end orchestration, making it easy to run dbt and Airflow together without maintaining your own infrastructure. It standardizes development environments, manages secrets, and supports multi-team and multi-project workflows without platform drift.
Cost Efficiency:
Datacoves reduces operational overhead by eliminating the need to maintain separate systems for orchestration, environments, CI, logging, and deployment. Its pricing model is predictable and designed for enterprise scalability.
Data Security and Compliance:
Datacoves can be deployed fully inside your VPC or private cloud. This gives organizations complete control over identity, access, logging, network boundaries, and compliance with industry and internal standards.
Reduced Vendor Lock-In:
Datacoves supports dbt, SQLMesh, and Bruin Data, giving teams long-term optionality. This avoids being locked into a single transformation engine or vendor ecosystem.
Running dbt Core yourself is a flexible option that gives teams full control over how dbt executes. It is also the most resource-intensive approach. Teams choosing DIY dbt Core must manage orchestration, scheduling, CI, secrets, environment consistency, and long-term platform maintenance on their own.
Benefits
Full Control:
Teams can configure dbt Core exactly as they want and integrate it with internal tools or custom workflows.
Cost Flexibility:
There are no dbt Cloud platform fees, but total cost of ownership often increases as the system grows.
Considerations
High Maintenance Overhead:
Teams must maintain Airflow or another orchestrator, build CI pipelines, manage secrets, and keep development environments consistent across users.
Requires Platform Engineering Skills:
DIY dbt Core works best for teams with strong Kubernetes, CI, Python, and DevOps expertise. Without this expertise, the environment becomes fragile over time.
Slow to Scale:
As more engineers join the team, keeping dbt environments aligned becomes challenging. Onboarding, upgrades, and platform drift create operational friction.
Security and Compliance Responsibility:
Identity, permissions, logging, and network controls must be designed and maintained internally, which can be significant for regulated organizations.
Teams that prefer code-first tools often look for dbt alternatives that provide strong SQL modeling, Python support, and seamless integration with CI/CD workflows and automated testing. These are part of a broader set of data transformation tools. Code-based ETL tools give developers greater control over transformations, environments, and orchestration patterns than GUI platforms. Below are four code-first contenders that organizations should evaluate.
Code-first dbt alternatives like SQLMesh, Bruin Data, and Dataform provide stronger CI/CD integration, automated testing, and more control over complex transformation workflows.
SQLMesh is an open-source framework for SQL and Python-based data transformations. It provides strong visibility into how changes impact downstream models and uses virtual data environments to preview changes before they reach production. SQLMesh was originally developed by Tobiko Data, acquired by Fivetran in 2025, and donated to the Linux Foundation in March 2026.
Benefits
Efficient Development Environments:
Virtual environments reduce unnecessary recomputation and speed up iteration.
Community Governance Under the Linux Foundation:
In March 2026, Fivetran contributed SQLMesh to the Linux Foundation, establishing an open community governance model. Founding members including Benzinga, CloudKitchens, Harness, and others joined to support its ongoing development. The project remains publicly available on GitHub, which increases its neutrality and long-term independence from any single vendor.
Considerations
Governance Is New:
While Linux Foundation stewardship is a positive signal for openness, the community governance model is still in its early stages. It remains to be seen how active and independent the contributor community will become over time.
Dataform is a SQL-based transformation framework focused specifically for BigQuery. It enables teams to create table definitions, manage dependencies, document models, and configure data quality tests inside the Google Cloud ecosystem. It also provides version control and integrates with GitHub and GitLab.
Benefits
Centralized BigQuery Development:
Dataform keeps all modeling and testing within BigQuery, reducing context switching and making it easier for teams to collaborate using familiar SQL workflows.
Considerations
Focused Only on the GCP Ecosystem:
Because Dataform is geared toward BigQuery, it may not be suitable for organizations that use multiple cloud data warehouses.
AWS Glue is a serverless data integration service that supports Python-based ETL and transformation workflows. It works well for organizations operating primarily in AWS and provides native integration with services like S3, Lambda, and Athena.
Benefits
Python-First ETL in AWS:
Glue supports Python scripts and PySpark jobs, making it a good fit for engineering teams already invested in the AWS ecosystem.
Considerations
Requires Engineering Expertise:
Glue can be complex to configure and maintain, and its Python-centric approach may not be ideal for SQL-first analytics teams.
Bruin is a modern SQL-based data modeling framework designed to simplify development, testing, and environment-aware deployments. It offers a familiar SQL developer experience while adding guardrails and automation to help teams manage complex transformation logic.
Benefits
Modern SQL Modeling Experience:
Bruin provides a clean SQL-first workflow with strong dependency management and testing.
Considerations
Growing Ecosystem:
Bruin is newer than dbt and has a smaller community and fewer third-party integrations.
While code-based transformation tools provide the most flexibility and long-term maintainability, some organizations prefer graphical user interface (GUI) tools. These platforms use visual, drag-and-drop components to build data integration and transformation workflows. Many of these platforms fall into the broader category of no-code ETL tools. GUI tools can accelerate onboarding for teams less comfortable with code editors and may simplify development in the short term. Below are several GUI-based options that organizations often consider as dbt alternatives.
GUI-based dbt alternatives such as Matillion, Informatica, and Alteryx use drag-and-drop interfaces that simplify development and accelerate onboarding for mixed-skill teams.
Matillion is a cloud-based data integration platform that enables teams to design ETL and transformation workflows through a visual, drag-and-drop interface. It is built for ease of use and supports major cloud data warehouses such as Amazon Redshift, Google BigQuery, and Snowflake.
Benefits
User-Friendly Visual Development:
Matillion simplifies pipeline building with a graphical interface, making it accessible for users who prefer low-code or no-code tooling.
Considerations
Limited Flexibility for Complex SQL Modeling:
Matillion’s visual approach can become restrictive for advanced transformation logic or engineering workflows that require version control and modular SQL development.
Informatica is an enterprise data integration platform with extensive ETL capabilities, hundreds of connectors, data quality tooling, metadata-driven workflows, and advanced security features. It is built for large and diverse data environments.
Benefits
Enterprise-Scale Data Management:
Informatica supports complex data integration, governance, and quality requirements, making it suitable for organizations with large data volumes and strict compliance needs.
Considerations
High Complexity and Cost:
Informatica’s power comes with a steep learning curve, and its licensing and operational costs can be significant compared to lighter-weight transformation tools.
Alteryx is a visual analytics and data preparation platform that combines data blending, predictive modeling, and spatial analysis in a single GUI-based environment. It is designed for analysts who want to build workflows without writing code and can be deployed on-premises or in the cloud.
Benefits
Powerful GUI Analytics Capabilities:
Alteryx allows users to prepare data, perform advanced analytics, and generate insights in one tool, enabling teams without strong coding skills to automate complex workflows.
Considerations
High Cost and Limited SQL Modeling Flexibility:
Alteryx is one of the more expensive platforms in this category and is less suited for SQL-first transformation teams who need modular modeling and version control.
Azure Data Factory (ADF) is a fully managed, serverless data integration service that provides a visual interface for building ETL and ELT pipelines. It integrates natively with Azure storage, compute, and analytics services, allowing teams to orchestrate and monitor pipelines without writing code.
Benefits
Strong Integration for Microsoft-Centric Teams:
ADF connects seamlessly with other Azure services and supports a pay-as-you-go model, making it ideal for organizations already invested in the Microsoft ecosystem.
Considerations
Limited Transformation Flexibility:
ADF excels at data movement and orchestration but offers limited capabilities for complex SQL modeling, making it less suitable as a primary transformation engine
Talend provides an end-to-end data management platform with support for batch and real-time data integration, data quality, governance, and metadata management. Talend Data Fabric combines these capabilities into a single low-code environment that can run in cloud, hybrid, or on-premises deployments.
Benefits
Comprehensive Data Quality and Governance:
Talend includes built-in tools for data cleansing, validation, and stewardship, helping organizations improve the reliability of their data assets.
Considerations
Broad Platform, Higher Operational Complexity:
Talend’s wide feature set can introduce complexity, and teams may need dedicated expertise to manage the platform effectively.
SQL Server Integration Services is part of the Microsoft SQL Server ecosystem and provides data integration and transformation workflows. It supports extracting, transforming, and loading data from a wide range of sources, and offers graphical tools and wizards for designing ETL pipelines.
Benefits
Strong Fit for SQL Server-Centric Teams:
SSIS integrates deeply with SQL Server and other Microsoft products, making it a natural choice for organizations with a Microsoft-first architecture.
Considerations
Not Designed for Modern Cloud Data Warehouses:
SSIS is optimized for on-premises SQL Server environments and is less suitable for cloud-native architectures or modern ELT workflows.
Recent consolidation, including Fivetran acquiring SQLMesh and merging with dbt Labs, has increased concerns about vendor lock-in and pushed organizations to evaluate more flexible transformation platforms.
Organizations explore dbt alternatives when dbt no longer meets their architectural, security, or workflow needs. As teams scale, they often require stronger orchestration, consistent development environments, mixed SQL and Python workflows, and private deployment options that dbt Cloud does not provide.
Some teams prefer code-first engines for deeper CI/CD integration, automated testing, and strong guardrails across developers. Others choose GUI-based tools for faster onboarding or broader integration capabilities. Recent market consolidation, including Fivetran acquiring SQLMesh and merging with dbt Labs, has also increased concerns about vendor lock-in.
These factors lead many organizations to evaluate tools that better align with their governance requirements, engineering preferences, and long-term strategy.
DIY dbt Core offers full control but requires significant engineering work to manage orchestration, CI/CD, security, and long-term platform maintenance.
Running dbt Core yourself can seem attractive because it offers full control and avoids platform subscription costs. However, building a stable, secure, and scalable dbt environment requires significantly more than executing dbt build on a server. It involves managing orchestration, CI/CD, and ensuring development environment consistency along with long-term platform maintenance, all of which require mature DataOps practices.
The true question for most organizations is not whether they can run dbt Core themselves, but whether it is the best use of engineering time. This is essentially a question of whether to build vs buy your data platform. DIY dbt platforms often start simple and gradually accumulate technical debt as teams grow, pipelines expand, and governance requirements increase.
For many organizations, DIY works in the early stages but becomes difficult to sustain as the platform matures.
The right dbt alternative depends on your team’s skills, governance requirements, pipeline complexity, and long-term data platform strategy.
Selecting the right dbt alternative depends on your team’s skills, security requirements, and long-term data platform strategy. Each category of tools solves different problems, so it is important to evaluate your priorities before committing to a solution.
If these are priorities, a platform with secure deployment options or multi-engine support may be a better fit than dbt Cloud.
Recent consolidation in the ecosystem has raised concerns about vendor dependency. Organizations that want long-term flexibility often look for:
Consider platform fees, engineering maintenance, onboarding time, and the cost of additional supporting tools such as orchestrators, IDEs, and environment management
dbt remains a strong choice for SQL-based transformations, but it is not the only option. As organizations scale, they often need stronger orchestration, consistent development environments, Python support, and private deployment capabilities that dbt Cloud or DIY dbt Core may not provide. Evaluating alternatives helps ensure that your transformation layer aligns with your long-term platform and governance strategy.
Code-first tools like SQLMesh, Bruin Data, and Dataform offer strong engineering workflows, while GUI-based tools such as Matillion, Informatica, and Alteryx support faster onboarding for mixed-skill teams. The right choice depends on the complexity of your pipelines, your team’s technical profile, and the level of security and control your organization requires.
Datacoves provides a flexible, secure alternative that supports dbt, SQLMesh, and Bruin in a unified environment. With private cloud or VPC deployment, managed Airflow, and a standardized development experience, Datacoves helps teams avoid vendor lock-in while gaining an enterprise-ready platform for analytics engineering.
Selecting the right dbt alternative is ultimately about aligning your transformation approach with your data architecture, governance needs, and long-term strategy. Taking the time to assess these factors will help ensure your platform remains scalable, secure, and flexible for your future needs.
Companies are investing heavily to become data-driven and to democratize data access. However, many are not achieving the transformative outcomes they expected.
The core issue? A lack of trust.
This mistrust stems from a lack of focus on core aspects that ensure a robust data-driven culture and critical mistakes in these areas.
Fortunately, these mistakes are self-inflicted which means they can be fixed, and this article aims to help highlight and address these pitfalls. By understanding and adhering to the core pillars of a data-driven culture and avoiding the common mistakes, organizations can develop and maintain a data-driven culture that people can trust.
It is no secret that there is power and opportunity in data, and data-driven culture is the approach which aims to take advantage of that.
A data-driven culture is not about hastily adopting the latest tools or technologies in the hope of resolving data challenges. This common mistake often leads to a focus on immediate results or 'shiny objects', such as acquiring cutting-edge technology or hiring new talent. Unfortunately, this approach tends to overlook essential priorities and gradually erodes the foundation of a data-driven culture: Trust in the data.
Many companies struggle with effectively using analytics because they overemphasize these immediate goals – the 'destination' – rather than appreciating the foundational journey necessary for impactful analytics. This journey involves more than just technology; it requires a shift in mindset and approach.
Data-driven culture represents an organizational approach where data is the cornerstone of decision-making processes. In such a culture, decisions are primarily informed by data analysis, rather than relying exclusively on intuition or past experiences. This approach involves strategically employing data at every level of the organization. It fosters an environment where data is not just an asset but the main driver of strategy, innovation, and operational choices. By harnessing the power and opportunities offered by data, a data-driven culture ensures that decisions across the organization are grounded in solid evidence and analytical insight, enhancing the overall decision-making quality and efficacy.
Empowered Decision Making: Decisions are based on data analysis, leading to objective and impactful outcomes.
Accessibility of Data: Data is accessible across the organization, breaking down silos and empowering all employees.
Investment in Technology: Adequate tools and technologies are provided for effective data collection and analysis.
Data Literacy: Continuous training is provided to enhance the workforce's understanding and use of data.
Quality and Governance: High standards of data accuracy and security are maintained.
Agility: The organization adapts quickly to insights derived from data.
Collaborative Integration: Data insights are shared and integrated across various functions.
Outcome-Focused: Emphasis on measurable results driven by data insights.
All of that sounds great, but how do we achieve a data-driven culture?
Like mentioned earlier in the article, true success in analytics comes not from merely chasing new tools or methodologies but from establishing three core pillars as part of a Data-Driven Culture:
By refocusing on these foundational elements, businesses can drive more meaningful and sustainable results from their analytic endeavors, leading to overall project success and satisfaction.
Let's dive deeper into the core pillars and examine the common pitfalls within each pillar that I have observed lead to challenges.
Fundamental alignment is about synchronizing analytics strategies with the organization's core business objectives. This ensures everyone involved, from executives to frontline employees, share a common vision and understanding of what analytics aims to achieve. This alignment is crucial for creating a unified direction in data-driven initiatives and ensuring that every analytics effort contributes meaningfully to the overall business strategy.
This sounds great right? So much so that every project I've participated in began with high hopes and enthusiasm. Initially, there was a sense of unity – funding secured, partnerships with vendors established, and the latest technology acquired. This honeymoon phase of the data driven transformation, filled with optimism, had everyone working diligently, with management receiving regular updates and a general belief that we were on the right track.
The real test emerged when critical decisions were required. This was the point where the honeymoon phase often faded, revealing a lack of true alignment. Meetings became prolonged discussions where the team struggled to reach consensus. This challenge stemmed from either not spending enough time initially to ensure everyone was on the same page or not conducting a discovery phase at the start of the project.
Although we agreed on high-level objectives like digital transformation and self-service analytics, there was a misalignment in our deeper understanding and perspectives. We were each influenced by our varied backgrounds and expertise in different aspects of the project.
This led me to a crucial fact: the importance of alignment before action. In projects where we dedicated time upfront for structured alignment and thorough product discovery, we not only achieved better estimations but also greater overall satisfaction. It became evident that successful analytics projects require a deep understanding of business objectives, the current state, potential risks, and a clear prioritization of features. This was because we developed a clear understanding and set up expectations that people could rely on throughout the course of implementation.
Crucially, alignment also involves clarity on what the project will not address, alongside the criteria for prioritization such as quality, completeness of features, and usability. Embracing agility does not mean forgoing thorough planning.
Ultimately, building trust in any project begins with listening, creating a shared vision, and setting the right expectations from the start. A well-defined and achievable plan, understood and agreed upon by all, is the foundation of success.
The end goal of analytics should be to serve the user's needs and involve designing practical solutions that add real value and enhance decision-making processes. This means creating analytics tools and processes that are intuitively aligned with how users work and make decisions, ensuring that these tools are not just technically proficient but also practically useful.
There are two pitfalls to avoid.
1. Trying to please everyone often leads to pleasing no one.
This is a common scenario in many companies where technical teams strive to meet all demands. Despite their efforts to deliver on projects, dissatisfaction with the end results is frequent.
2. Not addressing the actual user pain points.
This happens when the user does not actually get a good working solution out of the process.
During discovery it is important to discuss what is in scope, out of scope, essential, and nice to have. By categorizing this way you can better understand the needs of the group and use it to guide the process. With this process done, you can move forward with confidence that you are addressing the most important pain points.
Now that we have defined the pain points, the next step is to fully understand. The key is to not only understand their needs but the reasons behind them. What are the goals they're trying to achieve? What are the shortcomings of their current methods? Is the new process or tool genuinely an improvement over what they currently have? For example, if users need to navigate a tool quickly, finding ways to reduce unnecessary clicks and simplifying access becomes important. Sometimes, it's more practical to keep an existing process unchanged until other parts are enhanced. By bringing the most critical information to the forefront, the solution becomes more user centric.
It is important to have these needs in mind at the beginning of the project and strive to truly understand. If not, you risk investing time, money, and resources in a tool that users don't need, and this can have a detrimental effect on the overall culture.
More importantly, this approach allows you to justify your decisions and explain why certain aspects are prioritized over others. When users see that their needs and challenges are understood and addressed, they are more likely to trust and accept the solutions provided. This trust is built through consistently demonstrating that their best interests are at heart.
Efficient data management involves implementing robust processes to ensure data accuracy, accessibility, and understandability. This pillar is key to informed decision-making as it underpins the reliability of data-driven insights. Effective data management includes organizing, storing, and safeguarding data to make it readily available and useful for users across the organization.
Let's face it, your data processes get no love. This is usually because they are "too technical." Users often do not concern themselves with databases, schemas, tables, or columns, let alone the process that turns raw facts into business-ready insights. It is easy for management to get excited about a fancy dashboard and the potential of Machine Learning and Gen AI, but when it comes to the actual data, interest tends to wane.
It makes sense; most people don't understand how the power grid works. We take it for granted that we flip a switch and expect the lights to turn on. We move on without a second thought. No one really cares about electricity until something goes wrong. Similarly, in many organizations, data issues often go unnoticed until a failure occurs. Sometimes these issues are immediately apparent, but other times they are silent. When a failure does happen, there is a scramble to fix it. Meetings are held, issues are identified, and patches are implemented to "prevent" future failures. However, the best time to think about potential problems isn't after they happen, but before — building systems that anticipate and are designed for resilience.
The real issue is that the process from raw data to insights isn't often viewed as a single system. It is all interconnected and should be treated as such. In the world of analytics, it sometimes feels like companies are trying to build a mansion on a foundation of quicksand. Initially, everything seems fine, and everyone is busy with their tasks, but when the foundation starts to give way, the focus shifts to propping up the weak points. You can't effectively build on quicksand; you need solid, repeatable processes from the start.
The focus should be on building systems that anticipate challenges and are designed for resilience. This involves integrating data management practices into the company's culture from the start, ensuring users trust the data and the processes that generate insights. If you want effective collaboration and impact analysis, these are difficult to retrofit later — they need to be part of the initial plan. Documented analytics isn't a magical solution; it needs to be ingrained in the culture and process from the beginning. The good news is that there are many examples and best practices from those who have navigated these challenges successfully.
For users to truly trust in analytics, they need to have faith in the data and the processes that generate it. They need to see and believe in a robust system built on a solid foundation.
To achieve a data-driven culture, companies must refocus on three core pillars: fundamental alignment, user-focused solutions, efficient data management, and avoid common mistakes in these areas. Success in analytics isn't about chasing new tools or methodologies but about building a robust system from the ground up, aligning everyone's vision, and creating practical, value-added solutions. Prioritizing foundational elements over immediate shiny objects will lead to more meaningful, sustainable results and will build trust in the analytics process.
As the world of data management continues to grow, terms and new concepts are constantly popping up. It's important for data professionals to stay up to date with terms such as Data Mesh and data observability. For those coming into the field from other areas, it’s also good to understand terminology to communicate more effectively with others.
In this blog post, we've put together an extensive table that breaks down and explains the essential terms in modern data engineering, analytics, and architecture. This resource is designed to help both experienced data professionals and newcomers alike to navigate and understand the ever-evolving language of data.
We've covered basic concepts like data warehouses and ETL pipelines and advanced ideas like Data Mesh. Each of these terms is crucial in shaping today's data ecosystems. Think about how these terms apply to your business and can enhance your understanding. Have we missed any terms that you were hoping to see defined, or do you think we could improve the definitions of some of the terms already defined? Please share your thoughts with us by providing feedback through our contact page.
Interested in modern data solutions? Accelerate your journey to a modern data stack with Datacoves' managed solution, designed to streamline your data processes and implement best practices efficiently. Discover how Datacoves can help you quickly add value and transform your data strategy, ensuring you make the most informed decisions for your specific needs, by scheduling a demo.
Data transformation tools turn raw data into reliable, analytics-ready datasets, but choosing the right one requires understanding how transformation fits into your full data pipeline. Modern teams do not just transform data. They also orchestrate jobs, enforce quality, manage deployments, and ensure reliability at scale.
This guide explains what data transformation tools actually do, how popular options compare, and how to evaluate them as part of an end-to-end data stack rather than in isolation.
Evaluating data transformation tools requires looking beyond features and understanding how each tool fits into a production data pipeline.
Data transformation is the process of converting data from one format or structure to another. It improves the performance of data processing systems and compliance with data governance regulations.
Data transformation is just one of the steps on the road to deriving value from data.
The end-to-end process includes the following steps:
It’s worth taking each of these steps into consideration when determining the best data transformation tool for your organization.
There is a common misconception that the tool alone will solve all the problems.
However, using the right tools without addressing the underlying processes can lead to a data mess that can exacerbate the underlying issue, costing more time and money. This data mess could easily be avoided in the first place, not just by having the right tools but by also having the modern best practices in place.
Both help businesses extract, load, and transform data, but the sequence of events is different with their pros and cons.
ELT is generally more effective than ETL processes because it removes the uncertainty of not having the necessary data for future use cases and offers more flexibility in the long term. Since storage is typically affordable, it makes more sense to simplify the ingestion process.
Here’s a list of the top data transformation tools to manage the ETL process:
Each of these tools falls into one of two categories: code-based or visual/drag-and-drop interface. Both have their own set of pros and cons, which we’ll go through below.
Code-based tools allow you to transform data by using SQL or Python to explicitly define the transformation steps. Although it requires knowledge and experience, visual tools don’t negate the need to know SQL. This approach gives users a high degree of flexibility and control, and simplifies the maintainability and validation of work before releasing it to production.
Moreover, it is simpler to trace each data transformation step without having a disconnected document explaining what the transformation “should” do.
After having multiple conversations with data teams at enterprise companies, the challenge of developing and orchestrating dbt pipelines is a topic that has come up on numerous occasions.
There are a lot of tools to figure out when it comes to implementing the best practices for digital transformations and custom applications. It’s not uncommon for companies to end up with more than one SaaS platform and tool than they had initially planned. We built Datacoves to eliminate this need by providing the following:
Datacoves focuses on helping companies accelerate growth by providing a complete ELT solution, including orchestration and visualization. Therefore, the learning curve for data transformation is minimized because of our best-practice accelerators and the available tool integrations to form an end-to-end platform.
Here is the extended version of the ELT process with Datacoves:
Develop modular code and track version changes that you and your team can view. You’re also able to validate the quality of data transformations with our built-in testing frameworks and generate documents to leave a record of how you’re transforming data.
You develop in a VS Code environment that can be configured with a vast array of VS Code extensions and Python libraries All the modern data tools you need are provided in a structured workspace:
It’s suitable for medium and large companies that lack the expertise or don’t want to create and manage complex data processes and need the flexibility that complex enterprise processes require.
Data teams can use all the components provided within the dbt ecosystem in a structured, methodical way with Datacoves. This means you’ll have a simplified dbt experience, yet you’ll still see the same results of dbt when used to its full potential.
Smaller companies also gain competitive advantages with Datacoves because they’ll be able to implement DataOps, follow best practices, and get a fully managed VS Code environment accelerating time to value.
If you would like to know more about how Datacoves can help, you can schedule a demo here.
dbt Cloud allows businesses to build and maintain data pipelines. It’s a cloud-based platform with a web-based IDE that allows you to transform data within a cloud data warehouse. They can help you reduce the time spent setting up an end-to-end solution.
dbt Cloud works well for organizations looking to reduce the time and effort required to transform data pipelines.
Since dbt Cloud is a web-based IDE, it may feel limited for data teams that would rather use a VS Code environment. Moreover, dbt is not deployable in a company’s private cloud. It also typically requires other SaaS tools for complicated data pipelines, making it more difficult to manage unless you have the necessary integration experience with each of those SaaS tools.
Most importantly, dbt Cloud is focused solely on the data transformation step of the ELT process. Hence, you are unable to load VS Code extensions nor additional Python libraries. An enterprise with any level of complexity will also need a full-featured orchestrator.
Apache Airflow is an open-source platform for workflow management. You can orchestrate and schedule data pipelines. It’s a scalable and flexible platform that’s based on Python. You can also define your own operators with Airflow.
Apache Airflow works well for those needing a scalable data transformation tool with an open-source platform. It’s particularly a good choice for businesses mainly using Python to manage their data.
However, Airflow is primarily an orchestrator. That means you may end up building complex code in your data pipelines. Therefore, developing and maintaining this complexity requires experience and technical expertise. Managing the infrastructure for Airflow is not trivial and also requires an understanding of tools like Docker and Kubernetes.
SAS is a solution that allows you to transform and prepare data for analysis. It offers a wide range of features for data transformation, including data cleaning, data integration, and data mining.
SAS is ideal for companies with complex datasets, such as those in financial services, healthcare, and retail industries. Additionally, it’s ideal for professionals with advanced skills and knowledge in data transformation.
With that in mind, there are better solutions than SAS for those less experienced in programming and data management, as SAS licensing can be quite expensive.
SQLMesh is a complete DataOps solution for data testing and transformation. Teams can use SQLMesh to collaborate on data pipelines when transforming data.
SQLMesh is well-suited for businesses with SQL and Python expertise that need to collaborate on complex data transformations and pipelines. Although other open-source tools are available, teams can use SQLMesh to maintain data quality and perform unit testing of their transformations.
SQLMesh may not be ideal when you only need to perform simple data transformations. In this case, there are other more straightforward tools available. Moreover, SQLMesh may not be for you when your primary focus is on real-time data processing.
Visual tools make the ELT process more straightforward by removing the need to manually write code. It works by dragging and dropping pre-built components into a canvas. This makes them ideal for data teams who aren’t as experienced in programming.
The biggest advantage of graphical tools for ETL is that people who are less comfortable with code can use them. Conversely, drag-and-drop tools typically don’t offer the same level of flexibility and control as code-based tools, which can complicate the process of debugging data pipelines and long-term maintenance.
Informatica helps you turn your data into an asset. It’s a cloud-based or on-prem solution for data management with numerous data transformation libraries and APIs available.
Informatica can be a good choice for large enterprises and data professionals looking to quickly transform large volumes of complex data using an on-premise solution. It can also be a good choice for companies that need to comply with industry-specific data standards.
However, it may be too complicated to use for some organizations. Informatica requires a team of experienced data engineers with the necessary skills and experience. DataOps can also be a challenge. Since you’ll be dealing with multiple things simultaneously, it’s easy to get lost in the process when you don’t have the full technical expertise.
Moreover, it’s an expensive solution. There are other more affordable alternatives.
Talend is a cloud-native platform deployable on public cloud solutions such as AWS, Azure, and GCP. They also offer an on-prem solution and provide a variety of components and custom connectors for data transformation.
Talend works for most businesses and data professionals. It’s particularly well-suited for those who need to:
Still, you may want to consider other options when prioritizing DataOps and performing highly specialized data transformations such as machine learning or NLP. Talend enterprise licenses may also be costly.
Azure Data Factory helps you simplify the data transformation process at scale. You’re provided with a code-free and code-centric experience for orchestrating data transformation pipelines.
Azure Data Factory could be the right option for data professionals working within the Azure ecosystem. Azure may be worth considering when you’re looking into data warehousing using Azure Synapse and Azure DataOps and not just ELT.
However, Azure Data Factory might not be the best option when you’re on a budget. As with any visual ELT tooling, DataOps and pipeline maintainability may be more complex leading to an increased total cost of ownership.
Matillion is a cloud-based data transformation tool that provides you with on-premises databases, cloud applications, and SaaS platform integrations.
Matillion’s pre-built connectors and visual interface makes it an ideal solution for less experienced data professionals. The disadvantage is that it can be costly for businesses on a budget. Moreover, you must ensure that Matillion supports your specific requirements and how you intend to perform the data transformations. Care must be given to the long-term maintainability of pipelines that are both visual and code-based.
Getting started with Matillion is simple because they use a drag-and-drop interface for building data pipelines. But like with any other visual tool, there is still a learning curve and it’s typical to have a mix of code and visual components in a production data pipeline.
Alteryx simplifies the data transformation process. You can automate advanced analytics and prepare data through self-service. It’s an effective solution that makes it easier for teams to collaborate. Unlike the other visual tools above which are typically used by Data Engineers in IT, Alteryx is more widely adopted in less technical departments of an organization. It’s also typically paired with visualization tools like Tableau.
Alteryx is a good option to help ensure teams are on the same page throughout the data workflow. Data transformation projects can be shared and feedback provided seamlessly, making collaboration easier.
The downside is that Alteryx is costly compared to the other tools on this list. Moreover, there is still a bit of a learning curve, even if you’re experienced in data analytics. You should also check that Alteryx aligns with teams for effective collaboration.
Data transformation is a process that’s prone to multiple errors along the way. While many tools listed can help you reduce friction, they must be carefully evaluated. With Datacoves, you’ll be able to implement best data practices and DataOps so that you have a smooth process with a minimized learning curve.
If you’d like to learn more about how Datacoves helps you accelerate time to value, you can schedule a free demo here.
dbt transforms data inside your warehouse using SQL. Airflow orchestrates workflows across your entire data pipeline. They are not competing tools -- they solve different problems. dbt handles the "T" in ELT: modeling, data testing, unit testing, documentation, and lineage. Airflow handles the scheduling, sequencing, retries, failure notifications, observability, and coordination of every step in the pipeline, including ingesting data, triggering dbt, and activation steps. Most production data teams use both. The real decision is not which tool to pick, but how to run them together without drowning in infrastructure overhead.
Airflow is a workflow scheduler, but it is only one part of a broader data orchestration problem that includes dependencies, retries, visibility, and ownership across the entire data lifecycle.
Imagine a scenario where you have a series of tasks: Task A, Task B, and Task C. These tasks need to be executed in sequence every day at a specific time. Airflow enables you to programmatically define the sequence of steps as well as what each step does. With Airflow you can also monitor the execution of each step and get alerts when something fails.
Airflow's core strength is flexibility. You define the code behind each task, not just the sequence and schedule. Whether it's triggering an extraction job, calling a Python script, or integrating with an external platform, Airflow lets you control exactly how each step executes.
But that flexibility is a double-edged sword. Just because you can code everything inside Airflow doesn't mean you should. Overly complex task logic inside Airflow makes workflows harder to debug and maintain. The best practice: use Airflow as the orchestrator, not the execution engine. Let specialized tools handle the heavy lifting. Use tools like dbt for transformation and tools like Azure Data Factory, dlt, Fivetran or Airbyte for extraction. Let Airflow handle the scheduling, coordination, observability, and failure notification.
dbt (data build tool) is an open-source framework that uses SQL and Jinja templating to transform data inside the warehouse. Developed by dbt Labs, dbt handles the "T" in ELT: transforming, testing, documenting, and tracking lineage of data models. It brings software engineering practices like version control, CI/CD, and modular code to analytics workflows.
dbt lets you write transformation logic as SQL select statements, enhanced with Jinja templating for dynamic execution. Each transformation is called a "model." When you run dbt, it compiles those models into raw SQL and executes them against your warehouse, creating or replacing tables and views. dbt resolves dependencies between models automatically, running them sequentially or in parallel as needed.
Unlike traditional ETL tools that abstract SQL behind drag-and-drop interfaces, dbt treats SQL as the primary language for transformation. This makes it immediately accessible to anyone who already knows SQL. But dbt goes further by adding Jinja templating for dynamic scripting, conditional logic, and reusable macros.
A few things set dbt apart from alternatives:
dbt excels at the "T" in ELT but requires complementary tools for extraction, loading, and orchestration.
A common misunderstanding in the data community is equating "dbt" with "dbt Cloud." When people say dbt, they typically mean dbt Core -- the open-source framework. dbt Cloud is a commercial product from dbt Labs built on top of dbt Core that adds a scheduler, hosted IDE, monitoring, and managed infrastructure.
The key distinction: you can use dbt Core without paying for dbt Cloud, but you won't get a scheduler, hosted environment, or managed CI/CD. That means you'll need to solve orchestration, deployment, and infrastructure yourself. This is typically done with a tool like Airflow and a platform to tie it all together.
For a deeper comparison, see our article on dbt Cloud vs. dbt Core.
dbt Cloud includes a scheduler that automates dbt runs on a defined cadence. This keeps your transformation models fresh and your data reliable. But it only schedules dbt jobs, nothing else.
Your Extract and Load steps, downstream processes like BI refreshes, ML model retraining, marketing automation, and any cross-system coordination still need an orchestrator. dbt Cloud's scheduler does not replace Airflow. It replaces the need to cron-schedule dbt runs manually, but leaves the rest of the pipeline unmanaged.
Yes, unless your entire pipeline is just dbt transformations with nothing before or after them.
dbt Cloud schedules and runs your dbt jobs. That covers transformation, testing, and documentation. But most production pipelines involve more than that: extracting data from source systems, loading it into the warehouse, refreshing BI dashboards, triggering ML model retraining, sending alerts, and coordinating dependencies across all of these steps.
dbt Cloud does not orchestrate any of those. If your pipeline looks like this:
Extract → Load → Transform → Activate
Then dbt Cloud only covers one step. You still need an orchestrator like Airflow to tie the full pipeline together, manage dependencies between stages, handle retries, and provide end-to-end visibility when something breaks.
For small teams with a simple pipeline (one source, one warehouse, one dashboard), dbt Cloud's scheduler may be enough to start. But as soon as you add sources, downstream consumers, or cross-system dependencies, you'll need Airflow or a platform that includes it.
The hidden cost of choosing open-source dbt Core and Airflow is the platform you have to build around them. CI/CD pipelines, secrets management, environment provisioning, Git workflows, Kubernetes tuning, Airflow upgrades -- this is real engineering work that pulls your team away from delivering data products.
Datacoves eliminates that overhead. It provides managed dbt Core and managed Airflow in a unified environment deployed in your private cloud. Developers get an in-browser VS Code editor with dbt, Python, extensions, and Git pre-configured. Orchestration is handled through Datacoves' managed Airflow, which includes a simplified YAML-based DAG configuration that connects Extract, Load, and Transform steps without writing boilerplate Python.
Best practices, governance guardrails, and accelerators are built in, so teams get a production-grade data platform in minutes instead of months. For a deeper look at the tradeoffs, see our guide on building vs. buying your analytics platform.
To learn more about Datacoves, check out our product page.
This is not an either-or decision. dbt and Airflow solve different problems, and trying to force one tool to do the other's job creates fragile, hard-to-maintain pipelines.
Use dbt for transformation, testing, documentation, and lineage. Use Airflow for scheduling, dependency management, retries, and end-to-end pipeline coordination. The comparison table below shows where each tool fits.
If you need to pick one tool to start with, here's the decision framework:
Start with dbt if your immediate priority is transforming raw data into reliable, tested models in the warehouse. dbt gives you version control, testing, documentation, and lineage from day one. For small teams with simple pipelines, a cron job or dbt Cloud's scheduler can handle automation in the short term.
Start with Airflow if you already have multiple systems that need coordination; extraction from SaaS sources, loading into the warehouse, transformation, and downstream activation. Airflow gives you end-to-end visibility and control across the full pipeline.
The reality: most teams outgrow a single-tool approach quickly. Once your pipeline spans more than one step, you need both. The question isn't which tool to pick, it's how soon you'll need the other one. If you're still evaluating transformation tools, check out our dbt alternatives comparison.
In a production pipeline, the tools fit together like this:
Airflow owns the "when" and "what order." dbt owns the "how" for transformation. Neither tool steps on the other's responsibilities.
The challenge is running them together. Deploying Airflow and dbt Core in a shared, production-grade environment requires Kubernetes, CI/CD, secrets management, environment provisioning, and ongoing maintenance. For teams that don't want to build and maintain that platform layer themselves, a managed solution like Datacoves provides both tools pre-integrated in your private cloud -- so your team focuses on data, not infrastructure.
dbt and Airflow are not competing tools. dbt transforms your data. Airflow orchestrates your pipeline. Trying to use one without the other leaves gaps that show up as fragile pipelines, manual workarounds, and constant firefighting. The teams that struggle most are the ones who pick the right tools but never invest in the platform layer that makes them work together.
The real question is not "dbt vs Airflow," it's how you run them together without building and maintaining a platform from scratch. That infrastructure layer (Kubernetes, CI/CD, secrets, environments, Git workflows, upgrades) is where most teams burn time and budget.
Datacoves gives you managed dbt Core and managed Airflow in a unified environment, deployed in your private cloud, with best practices and governance built in. Your team focuses on delivering data products, not maintaining the platform underneath them.
dbt testing gives data teams a way to validate data quality, transformation logic, and governance standards directly inside their development workflow. Out of the box, dbt Core includes generic tests (unique, not_null, accepted_values, relationships), singular tests for custom SQL assertions, and unit tests for verifying transformation logic before it reaches production. Community packages like dbt-utils and dbt-expectations add dozens more. Combined with CI/CD tools like dbt-checkpoint, teams can enforce testing and documentation standards on every pull request.
This guide covers every layer: what ships with dbt Core, which packages to add, how to store and review results, and how to integrate testing into your CI/CD pipeline.
dbt has two main categories of tests: data tests and unit tests.
Data tests validate the integrity of actual warehouse data and run with every pipeline execution. They come in two forms: generic tests, defined in YAML and applied across models, and singular tests, written as standalone SQL assertions for specific conditions. Under the hood, dbt compiles each data test to a SQL SELECT statement and executes it against your database. If any rows are returned, dbt marks the test as failed.
Unit tests, introduced in dbt 1.8, validate transformation logic using static, predefined inputs. Unlike data tests, they are designed to run during development and CI only, not in production.
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dbt Core ships with four built-in generic tests that cover the most common data quality checks.
unique verifies that every value in a column contains no duplicates. Use this on identifiers like customer_id or order_id to catch accidental duplication in your models.not_null checks that a column contains no null values. This is especially useful for catching silent upstream failures where a field stops being populated.accepted_values validates that a column only contains values from a defined list. For example, a payment_status column might only allow pending, failed, accepted, or rejected. This test will catch it if a new value like approved appears unexpectedly.relationships checks referential integrity between two tables. If an orders table references customer_id, this test verifies that every customer_id in orders has a matching record in the customers table.You apply generic tests by adding them to the model's property YAML file.
Generic tests also support additional configurations that give you more control over how and when they fail.
where limits the test to a subset of rows, useful on large tables where you want to test only recent data or exclude specific values.severity controls whether a test failure blocks execution. Set to warn to flag issues without stopping the pipeline, or keep the default error for critical checks.name overrides the auto-generated test name. Since dbt generates long default names, a custom name makes logs and audit tables much easier to read.
Singular tests are custom SQL assertions saved in your tests/ directory. Each file contains a query that returns the rows failing the test. If the query returns any rows, dbt marks the test as failed.
Use singular tests when built-in generic tests or package tests do not cover your specific business logic. A good example: verifying that sales for one product stay within +/- 10% of another product over a given period. That kind of assertion is too specific for a reusable generic test but straightforward to express in SQL.
When you find yourself writing similar singular tests repeatedly across models, that is a signal to convert the logic into a custom generic test instead.
Custom generic tests work like dbt macros. They can be stored in tests/generic/ or in your macros/ directory. Datacoves recommends tests/generic/ as the default location, but macros/ makes more sense when the test depends on complex macro logic. At minimum, a custom generic test accepts a model parameter, with an optional column_name if the test applies to a specific column. Additional parameters can be passed to make the test more flexible.
Once defined, a custom generic test can be applied across any model or column in your project, just like the built-in tests. This makes them the right choice when you have business logic that repeats across multiple models but is too specific to find in a community package.
Unit tests, available natively since dbt 1.8, validate that your SQL transformation logic produces the expected output before data reaches production.
Unlike data tests that run against live warehouse data on every pipeline execution, unit tests use static, predefined inputs defined as inline values, seeds, or SQL queries in your YAML config. Because the expected outputs don’t change between runs, there’s no value in running unit tests in production. Run them locally during development and in your CI pipeline when new transformation code is introduced.
If your project is on a version older than dbt 1.8, upgrading is the recommended path. Community packages that previously filled this gap (dbt-unit-testing, dbt_datamocktool, dbt-unittest) are no longer actively maintained and are not recommended for new projects.
Source freshness checks aren’t technically dbt tests, but they solve one of the most common silent failure modes in data pipelines: a source stops updating, but the pipeline keeps running without any errors.
Freshness checks are configured in your sources.yml file with warn_after and error_after thresholds. When dbt detects that a source has not been updated within the defined window, it raises a warning or error before your models run. This is especially critical for time-sensitive reporting, where stale data can be worse than no data at all.
dbt Core's built-in tests cover the fundamentals, but the community has built a rich ecosystem of packages that extend testing well beyond what ships out of the box. Packages are installed via your packages.yml file and sourced from dbt Hub.
dbt-utils, maintained by dbt Labs, is the most widely used dbt testing package. It adds 16 generic tests alongside SQL generators and helper macros that complement dbt Core's built-in capabilities.
not_accepted_values is the inverse of accepted_values. Use it to assert that specific values are never present in a column.equal_rowcount confirms that two tables have the same number of rows. This is particularly useful after transformation steps where row counts should be preserved.fewer_rows_than validates that a target table has fewer rows than a source table, which is the expected result after any aggregation step.For the full list of all 16 generic tests with usage examples, see the Datacoves dbt-utils cheatsheet.
dbt-expectations, maintained by Metaplane, ports the Python library Great Expectations into the dbt ecosystem. It gives analytics engineers 62 reusable generic tests without adding a separate tool to the stack.
The package covers seven categories:
The string matching and aggregate function categories in particular cover validations that would otherwise require custom singular tests. Full documentation is available on the dbt-expectations GitHub repository.
dbt_constraints, created by Snowflake, generates database-level primary key, unique key, and foreign key constraints directly from your existing dbt tests. It is primarily designed for Snowflake, with limited support on other platforms.
When added to a project, it automatically creates three types of constraints.
dbt_utils.unique_combination_of_columns.relationships tests.This package is most valuable for teams that want database constraints alongside dbt's test-based validation, not instead of it. Snowflake doesn’t enforce most constraints at write time, but the query optimizer uses them for better execution plans. Some tools also read constraints to reverse-engineer data model diagrams or add joins automatically between tables.
For most dbt projects, dbt-utils and dbt-expectations are the two packages worth adding first. Layer in dbt_constraints when your use case specifically calls for database-level constraints on Snowflake.
By default, dbt doesn’t persist test results between runs. You can change this by setting store_failures: true in your dbt_project.yml or at the individual test level.
This is useful for spot-checking failures after a run, but each execution overwrites the previous results, so it does not give you historical visibility into data quality trends over time. The tools below address that gap with lightweight reporting options.
When store_failures: true is enabled at the project or test level, dbt writes the rows that fail each test into tables in your warehouse under a schema named [your_schema]_dbt_test__audit. This creates one table per test, containing the actual records that triggered the failure.
This is a practical first step for teams that want to inspect failures without setting up a dedicated observability platform. The main limitation: each run overwrites the previous results, so you cannot track failure trends over time without additional tooling.
Datacoves recommends dbt audit tables in a separate database to keep your production environment clean. On platforms like Snowflake, this also reduces overhead for operations like database cloning.
dq-tools stores dbt test results and makes them available for visualization in a BI dashboard. If your team already has a BI layer in place, this is a lightweight way to surface data quality metrics alongside existing reporting without adding a separate observability tool.
datacompy is a Python library for migration validation. It performs detailed table comparisons, reporting on row-level differences, column mismatches, and statistical summaries. It’s not a dbt package, but it integrates naturally into migration workflows that use dbt for transformation.
Standard dbt tests aren’t designed for row-by-row comparison across systems, which makes datacompy a useful complement when you’re migrating from legacy platforms.
The tests covered so far validate data. The tools below validate your dbt project itself, catching governance and compliance issues at the development and PR stage rather than in production.
dbt-checkpoint, maintained by Datacoves, enforces project governance standards automatically so teams don’t rely on manual code review to catch missing documentation, unnamed columns, or hardcoded table references. It runs as a pre-commit hook, blocking non-compliant code before it’s pushed to the main branch, and can also run in CI/CD pipelines to enforce the same checks on every pull request.
Out of the box it validates things like whether models and columns have descriptions, whether all columns defined in SQL are present in the corresponding YAML property file, and whether required tags or metadata are in place.
It's a natural fit for dbt Core teams that want Git-native governance without additional infrastructure. The tradeoff: it requires familiarity with pre-commit configuration to set up and extend. Like all Python-based governance tools, it doesn't run inside the dbt Cloud IDE.
dbt-bouncer, maintained by Xebia, takes an artifact-based approach, validating against manifest.json, catalog.json, and run_results.json rather than running as a pre-commit hook. It requires no direct database connection and can run in any CI/CD pipeline that has access to dbt artifacts. Checks cover naming patterns, directory structure, and description coverage, and it can run as a GitHub Action or standalone Python executable.
dbt-project-evaluator, maintained by dbt Labs, takes a different approach. Rather than running as an external tool, it is a dbt package: it materializes your project's DAG structure into your warehouse and runs dbt tests against it.
It checks for DAG issues (model fanout, direct joins to sources, unused sources), testing and documentation coverage, naming convention violations, and performance problems like models that should be materialized differently.
The main limitation is adapter support: it works on BigQuery, Databricks, PostgreSQL, Redshift, and Snowflake, but not on Fabric or Synapse. Because it materializes models into your warehouse, it has a slightly higher execution cost than artifact-based tools.
dbt-score, maintained by Picnic Technologies, is a Python-based CLI linter that reads your manifest.json and assigns each model a score from 0 to 10 based on metadata quality: missing descriptions, absent owners, undocumented columns, models without tests.
The scoring approach makes it easy to track improvement over time and prioritize which models need attention, without enforcing hard pass/fail gates on every PR. Custom rules are fully supported. It requires no database connection and no dbt run, just a manifest.json.
These tools aren’t mutually exclusive, and many teams combine them. For dbt Core teams, dbt-checkpoint is the recommended starting point: it enforces governance at the PR stage with minimal setup and can run both locally and in CI/CD pipelines. dbt-bouncer is worth evaluating if your team wants artifact-based validation running in an external CI/CD pipeline. Add dbt-project-evaluator when you want DAG-level structural checks alongside documentation and testing coverage. Layer in dbt-score for ongoing visibility into metadata quality across your project without hard enforcement gates.
dbt testing isn’t an all-or-nothing investment. The most effective approach is to start with what ships in dbt Core and expand coverage as your team builds confidence.
A practical progression looks like this: start with unique, not_null, and relationships tests on source tables and mart models. Add dbt-utils and dbt-expectations as your testing needs grow beyond the basics. When your team is ready to enforce governance and metadata standards, dbt-checkpoint is the simplest starting point for dbt Core teams, with dbt-project-evaluator and dbt-score as complementary layers.
If your organization is still running transformations in legacy tools like Talend, Informatica, or Python, you do not need to wait for a full migration to start benefiting from dbt testing. dbt sources can point to any table in your warehouse, whether or not dbt created it, so you can layer dbt tests and documentation on top of your existing pipeline today. See Using dbt to Document and Test Data Transformed with Other Tools for a step-by-step walkthrough.
Where teams hit friction isn’t usually the tests themselves. It’s managing the infrastructure, environments, CI/CD pipelines, and orchestration around them. Datacoves handles that layer, giving your team a managed dbt and Airflow environment so they can focus on building and testing models rather than maintaining tooling. Learn more about how Datacoves compares to other dbt solutions.
You now know what dbt (data build tool) is all about. You are being productive, but you forgot what `dbt build` does or you forgot what the @ dbt graph operator does. This handy dbt cheat sheet has it all in one place.
With the advent of dbt 1.6, we updated the awesome dbt cheat sheet created originally by Bruno de Lima
We have also moved the dbt jinja sheet to a dedicated post.
This reference summarizes all the dbt commands you may need as you run your dbt jobs or study for your dbt certification.
If you ever wanted to know what the difference between +model and @model is in your dbt run, you will find the answer. Whether you are trying to understand dbt graph operators or what the dbt retry command does, but this cheat sheet has you covered. Check it out below.
These are the principal commands you will use most frequently with dbt. Not all of these will be available on dbt Cloud
The dbt commands above have options that allow you to select and exclude models as well as deferring to another environment like production instead of building dependent models for a given run. This table shows which options are available for each dbt command
By combining the arguments above like "-s" with the options below, you can tell dbt which items you want to select or exclude. This can be a specific dbt model, everything in a specific folder, or now with the latest versions of dbt, the specific version of a model you are interested in.
dbt Graph Operator provide a powerful syntax that allow you to hone in on the specific items you want dbt to process.
The following commands are used less frequently and perform actions like initializing a dbt project, installing dependencies, or validating that you can connect to your database.
The flags below immediately follow the dbt command and go before the subcommand e.g. dbt <FLAG> run
Read the official dbt documentation
As a managed dbt Core solution, the Datacoves platform simplifies the dbt Core experience and retains its inherent flexibility. It effectively bridges the gap, capturing many benefits of dbt Cloud while mitigating the challenges tied to a pure dbt Core setup. See if Datacoves dbt pricing is right for your organization or visit our product page.
Please contact us with any errors or suggestions.
I read an article on Anchor Data Modeling, more specifically, Atomic modeling where the author proposes a different way of Data Modeling. The rationale for this change is that there is a lack of skills to model data well. We are giving powerful tools to novices, and that is bound to lead to problems.
From the article:
"we are in a distressful situation both concerning the art as a whole but also its place in modern architectures"
Is this the case? Do we have a big problem on the horizon that requires us to make this big shift?
I'd say I am open-minded and expose myself to different ways of thinking so I can broaden my views. A few years ago, I learned a bit about COBOL, not because I had any real use for it but because I was curious. I found it very interesting and even saw its similarities with SQL. I approached the topic with no preconceived ideas; this is the first time I heard of Atomic Modeling.
The issues I see with ideas like Atomic data modeling are not in their goal. I am 100% aligned with the goal; the problem is the technology, process, and people needed to get there.
What we see in the market is a direct result of a backlash against doing things "perfectly." But why is this the case? I believe it is because we haven't communicated how we will achieve this vision of ideas like atomic data. The author even says a key phrase in the first paragraph:
"practitioners shying away from its complexity"
If doing anchor data modeling is "complex" how are we going to up-skill people? Is this feasible? I am happy if I can get more people to use SQL vs a GUI tool like Alteryx 😁
I am by no means an expert. Yet, I am fairly technical, and if I am not convinced, how will we convince decision-makers?
As I read this article, here's what I am thinking:
1. First, I will need to deconstruct the data I get from some source like material master data form SAP. That will be a bunch of tables, and who is going to do all this data modeling? It sounds expensive and time-consuming.
2. I am going to need some tooling for this, and I am either going to build it or use something a few others are using. Will my company want to take a chance on something this early? This sounds risky.
3. After I deconstruct all this data, I need to catalog all these atoms. I now have a lot of metadata, and that's good, but is the metadata business-friendly? We can't get people to add table descriptions how is this going to happen with this explosion of objects? Who will maintain it? How will we expose this? Is there a catalog for it already? Does that catalog have the other features people need? It sounds like I need to figure out a bunch of things, the biggest one being the change management aspect.
4. What sort of database will I use to store this? This is a great use case for a graph database. But graph databases are not widely adopted, and I have seen graph databases choke at scale. We can use a relational database, but these joins are going to be complex. Someone may have figured all this out, but there's more tech and learning needed. It sounds like this will also add time and expense.
5. When I have managed to do all the above, I will need to construct what people can actually use. We need objects that work with tools that are available. I need to make relational tables I can query with SQL and viz tools, which are more user-friendly. This sounds like more work, more time, and more money.
I may have missed some steps and oversimplified what's needed for this type of change. I am also aware that I may not know what exists to solve all the above. However, if I don't know it, then there are a ton of other people who also don't know it and this is where we need to start. We need to understand how we will tactically achieve this "better" world.
I've had conversations on metadata-driven automation, and like atomic modeling, I am not clear on who we are helping and how. What are we improving and in what timeframe? In the end, it feels like we have optimized for something only a few companies can do. To do anchor modeling well would be a huge expense, and when things go wrong, there are several points of failure. When we look at business problems, we need to be sure to optimize the end-to-end system. We can't locally optimize one area because we are likely moving the problem somewhere else. This can be in terms of time, money, or usability.
Decision-makers are not interested in data modeling. They are expecting results and a faster time to market. It's hard enough getting people to do things "better." This is why I find it hard to imagine that we can get to this level of maturity any time soon.
There are incremental steps we can take to incorporate best practices into the modern data stack. We need to help people mature their data practice faster, and we should not let perfection get in the way of good. Most companies are not large enterprises with millions of dollars to spend on initiatives like atomic modeling. That being said, I have yet to see anchor modeling in practice, so I welcome the opportunity to learn. I remember years ago the debates about how Ruby on Rials was teaching people "bad practices." The other side of that argument is that Rails helped companies like Twitter and Github launch businesses faster. Rails was also better than the alternative at the time, which included messy PHP code. Others advocated for well-crafted "scalable" and expensive Java applications. Rails may not be the powerhouse it once was, but it has had a huge influence on how we build software. I even see its influence in dbt even if it might not have been intentional or direct.
Tools like Snowflake and dbt allow us to build processes that are much better than what most people have. Should we focus on all the "bad" things that may come with the modern data stack? Should we focus on how practitioners are not well educated, and so we need to throw all they are doing out?
I don't think so; I believe that we can help companies mature their data practices faster. Will we have the best data models? Maybe not. Will users do things perfectly? Nope. But can we help them move faster and guide them along their journey to avoid big pitfalls? I think we can. Getting people to use git, automating testing, and creating DataOps processes is a huge step forward for many organizations. Let's start there.
There's a reason Data Mesh and the Modern Data Stack resonate with so many people. There's a desire to do things faster with more autonomy at many companies, not just the ones with multi-million-dollar budgets. Let's focus on what is achievable, do the best we can, and help people mature along the way. We don't need more complexity; we need less.
