Datacoves blog

The world of data moves at a lightning-fast pace, and you may be looking to keep up by migrating your data to a modern infrastructure. As you plan your data migration, you’ll quickly see the many moving parts involved, from data compatibility and security to performance optimization. Choosing the right partner is critical—making the wrong choice can lead to data loss or corruption, compliance failures, project delays, hidden costs and more. At worst, you could end up with a costly new process that fails to gain user adoption! This article provides 10 key factors to consider in a partner to ensure these pitfalls don’t happen to you, guiding you toward a smooth and successful migration. Lets dive in!  

What is data migration?

Data migration is the process of moving data pipelines from one platform to another. This process can include upgrading or replacing legacy platforms, performing critical maintenance, or transitioning to new infrastructure such as a cloud platform. Whether it's moving data to a modern data center or migrating workloads to the cloud, data migration is a pivotal undertaking that demands meticulous planning and execution.

Organizations may embark on this complex journey for many reasons. A common driver is the need to modernize and adopt cutting-edge solutions like cloud platforms such as Snowflake, which offer unparalleled scalability, performance, and the flexibility of ephemeral resources. Data migration may also be necessitated by mergers and acquisitions, where consolidating and standardizing data across multiple systems becomes essential for unified operations. Additionally, organizations might pursue migration to improve security, streamline workflows, or boost analytics capabilities.

Done right, data migration can be transformative, enhancing data usage and enabling organizations to unlock new opportunities for efficiency, deeper insights, and strategic growth.

The complexity of data migration

Migrating data is a complex undertaking with many moving parts that vary based on your current system and the target system. Careful assessment of your current state and your desired future state is a critical step that should never be overlooked in this planning process. Key considerations include data security, optimizing configurations in the new environment, and transitioning existing pipelines seamlessly. Joe Reis and Matt Housley often emphasize that much of data engineering revolves around "plumbing"—the foundational connections and data flows—which must be meticulously managed for any successful migration.

A lift-and-shift approach, where pipelines are simply moved without modifications, should be avoided as much as possible. This method often undermines the purpose of migrating in the first place: to capitalize on modern features and enhancements offered by newer tools, such as dbt, to improve data quality, documentation, and impact analysis. Moving to dbt without re-thinking how data is cleansed and transformed can lead to outcomes that are worse than your current state such as increased compute costs and difficulty in debugging issues.

Given these complexities, detailed planning, skilled execution, prioritization, decommissioning unused assets, and effective risk management are crucial for a successful migration. Achieving this demands experienced professionals who can execute flawlessly while remaining adaptable to unexpected challenges.  

The risks of choosing the wrong partner

As we have seen above, there are many complexities when it comes to data migration, making the selection of the right partner paramount. Choosing the wrong partner can potentially lead to longer implementation times, hidden costs, project failure, compliance failures, data loss and corruption, and lost opportunity costs. Let’s discuss each of these in a little more detail.  

Longer time to implementation

Inexperienced partners can cause significant delays due to suboptimal choices in planning, technology selection, and execution. These inefficiencies can lead to frequent setbacks, resource mismanagement, and potential catastrophic roadblocks. Prolonged implementation timelines may also result in missed opportunities to capture market value and reduce time-to-insight, while eroding trust in a system that has yet to be fully implemented.

Hidden costs

Hiring the wrong partner often results in unforeseen costs due to extended project timelines as mentioned above, poor resource allocation, and the need for rework when initial efforts fall short. These hidden costs may include increased labor expenses, additional technology investments to rectify poor initial solutions, and higher costs associated with resolving data security or compliance issues.  Budget overruns and unexpected expenses from lack of foresight, poor risk management, and inefficiency can quickly erode ROI.

Project failure

A poorly executed data migration can lead to a new process that underperforms, costs more, or fails to gain user adoption. When users reject a poorly implemented system, organizations may be forced to maintain legacy systems, further compounding costs and delaying innovation. Worse still, critical data may be unusable or inconsistent, undermining trust in data-driven initiatives.

Compliance failures

Hiring the right partner is essential for ensuring compliance with data regulations, industry standards, and security best practices. Without expertise in these areas, there is a heightened risk of data breaches, non-compliance fines, and reputational damage due to mishandling sensitive information. Such failures can lead to costly legal ramifications, operational downtime, and diminished customer trust.

Data loss or corruption

Inadequate planning, testing, or execution can result in the loss or corruption of critical data during migration. Poor data management practices, such as insufficient backups, improper mapping of data fields, or inadequate validation procedures, can compromise data integrity and create gaps in your data sets. Data loss and corruption can disrupt business operations, degrade analytics capabilities, and require extensive rework to correct.

Missed optimization opportunities

Choosing the wrong partner can lead to missed opportunities for optimizing data processes, modernizing workflows, and unlocking valuable business insights. Every moment spent fixing issues or addressing inefficiencies due to poor implementation represents lost time that could have been invested in enhancing data quality, streamlining operations, and driving strategic initiatives. This opportunity cost is often overlooked but can be the difference between gaining a competitive edge and falling behind.

10 key factors to consider when choosing a data migration partner

Datacoves does not do data migrations, but we see companies hire companies to do this work as they implement our platform. Through our experience, we have compiled a list of 10 key factors to consider when selecting a data migration partner. Carefully evaluating these factors can significantly increase the likelihood of success for your data migration plan and ensure a smoother overall process.

1. Proven track record of success

When selecting a data migration partner, it’s crucial to thoroughly review their case studies, references, and client testimonials. Focus on case studies that feature companies with similar starting points and objectives to your own. Approach client testimonials with a discerning eye and validate their claims by contacting references. This is an excellent opportunity to determine whether the partner is merely focused on checking tasks off a to-do list or genuinely dedicated to setting things up correctly the first time, with a passion for leaving your organization in a strong position. While this may seem like a considerable effort, such diligence is essential for investing in your data’s success and ensuring the partner can deliver on their promises.

2. Deep technical expertise

Building on the importance of a proven track record from above, this factor emphasizes the need for technical depth. Verify that your potential partner is proficient in overarching data terminology and best practices, with deep familiarity in areas such as data architecture, data modeling, data governance, data integration, and security protocols. A qualified data partner must have the expertise necessary to successfully guide you through every phase of your data migration. Skipping this crucial step can lead to poorly structured data, compromised system performance, and numerous missed opportunities for optimization.

3. Effective project management communication and collaboration skills

This is often overlooked when selecting a data migration partner, yet it plays a critical role in ensuring a successful project. When evaluating potential partners, consider asking the following questions to assess their project management and communication capabilities:

• How do you structure the migration process?

• Will you provide regular sprint updates to keep us informed of progress?

• How transparent are you about the use of billable hours?

• Do you offer dashboards or tools that keep us updated and provide comprehensive data plans with clear, actionable timelines that we can follow and provide feedback on?

• How will you collaborate with our team to ensure a seamless workflow and maintain clear, consistent roadmaps?

• If deviations from the initial plan become necessary, how do you communicate and manage such changes?

This is by no means an exhaustive list of questions but rather a great starting point. The right partner should feel like a leader rather than a liability, demonstrating their expertise in a proactive manner. This ensures you don’t have to constantly direct their work but can trust them to drive the project forward effectively.

4. Industry-specific knowledge

A common theme for a successful partnership is deep expertise, and this is especially true for industry-specific knowledge. Every industry has its unique challenges and pitfalls when it comes to data. It is important to seek out partners who are experts in your industry and have a proven track record of successfully guiding similar organizations to their goals. For example, if your organization operates within the Health and Life Sciences sector, a partner with experience exclusively in Retail may lack the nuanced understanding required for your specific data needs, such as handling PII data, adhering to stringent regulatory compliance, or managing complex clinical trial data. While industry familiarity shouldn’t necessarily be a dealbreaker for every organization, it can be critical for sectors like Health and Life Sciences due to their high regulatory demands. Other industries may find it less restrictive, which is why it remains a key factor to consider when finding the right fit. See how Datacoves helped J&J achieve a 66% reduction in data processing with their Modern Data Platform, best practices, and accelerators.

5. Comprehensive risk mitigation strategy

A partner's ability to minimize downtime, prevent data loss, and mitigate security risks throughout the migration process is essential to avoiding catastrophic consequences such as prolonged system outages, data breaches, or compliance failures. A comprehensive risk mitigation strategy ensures that every aspect of your data migration is thoughtfully planned and executed with contingencies in place. Ask potential partners how they approach risk assessment, what protocols they follow to maintain data integrity, and how they handle unexpected issues. The right partner will proactively identify potential risks and implement measures to address them, providing you with peace of mind during what can be an otherwise complex and challenging process.

6. Flexibility and customization

A successful data migration partner should offer tailored solutions rather than relying on one-size-fits-all approaches. Every organization’s data needs are unique, and flexibility in meeting those needs is extremely important. Consider how a partner adapts their strategy and tools to align with your specific requirements, workflows, and constraints. Do they take the time to understand your goals and develop a plan accordingly, or do they push prepackaged solutions? The ability to customize their approach can be the difference between a migration that delivers optimal business value and one that merely "gets the job done."  

7. Long-term support and optimization capabilities

Data migration doesn’t end with the initial project. A strong partner should offer ongoing support, optimization, and strategic guidance post-migration to ensure continued value from your data infrastructure. Ask about their approach to post-migration support: Will they provide continued monitoring, performance optimization, and assistance—and for how long? The best partners view your success as an ongoing journey, bringing the expertise needed to continuously refine and enhance your data systems. Their commitment to getting things right the first time minimizes future issues and demonstrates a vested interest in your long-term success. By prioritizing a forward-thinking approach, they ensure your data systems are built to last, rather than quickly implemented and forgotten. This is why Datacoves goes beyond just providing tools; we offer accelerators and best practices designed to help you implement dbt successfully, ensuring a strong foundation for your data transformation journey. We work with strategic migration partners that will help you set things up the right way and are around for the long haul.

8. Time zone overlap

For many organizations, the geographic location of a data migration partner can impact communication and project efficiency. Consider whether the partner’s working hours overlap with yours. How will they handle urgent requests or collaboration across different time zones? Effective time zone alignment can enhance communication, reduce delays, and ensure faster resolution of issues. The last thing you want is to find an issue and not be able to get an answer until the next day.  

9. Change management focus

Successful data migration extends beyond the technical execution and tooling—it also requires effective change management. A capable partner will help your organization navigate the changes associated with data migration, including new processes, systems, and ways of working. How do they support employee training, communication, and adoption of new tools? Do they provide resources and strategies to ensure a smooth transition? Partners with a strong change management focus will work with you to minimize disruptions and maximize user adoption.

10.Certification

When evaluating potential partners, keep in mind that while their team lead may be highly technical, the team members you’ll work with day-to-day might not always match that level of expertise. Ensure that the team members working on your project possess relevant certifications for the key technologies you use. Certifications, such as dbt Certification, Snowflake Certification, or other relevant credentials, demonstrate expertise and a commitment to staying current with industry standards and best practices. Ask potential partners to provide proof of certification and inquire about how their team keeps pace with evolving technologies. While certifications alone don’t guarantee proficiency, they offer a solid starting point for assessing skill and commitment. This assurance of expertise can significantly impact the success of your project.

Don’t skimp on cost

Cost should not be the determining factor when hiring a migration partner. Cost is an essential consideration as it will directly impact project budget, but you must consider the total cost of ownership of your new platform.  In the long term, the initial migration cost will impact the long-term on-going costs. A low-cost partner will lack several of the items listed above and your migration team may be staffed with inexperienced team members. The migration will be done, but how much technical debt will you accumulate along the way?

Avoid simply searching for the lowest-cost vendor. Though this may lower upfront expenses, it often results in higher costs over time due to errors, inefficiencies, and the need for rework. Projects that are rushed or handled without proper expertise tend to exceed their budgets, take longer to complete, and are more challenging to maintain in the long run because they weren’t done correctly or optimized from the start. Experienced partners bring significant value by ensuring work is done right and to a high standard from the beginning. It is obvious that contracting a partner that meets most, if not all, of the key factors mentioned above most likely requires a monetary investment. This should be viewed as an investment in expertise that helps mitigate long-term costs and risks.

Conclusion

Choosing the right data migration partner is key to minimizing risks and ensuring optimal outcomes for your organization. The complexities and challenges of data migration demand a partner with proven expertise, industry-specific knowledge, effective communication, flexibility, and a commitment to long-term support. Each of the factors outlined above plays a vital role in determining the success of your migration project—potentially saving your organization from costly delays, hidden expenses, compliance pitfalls, and lost business opportunities.

Carefully evaluate potential partners using these key considerations to ensure you select a partner who will not only meet your immediate data migration needs but also support your organization’s continued success and growth. 📈

Can the right data migration platform cut costs and speed up delivery?

Datacoves has built-in best practices and accelerators built from our deep expertise in dbt, Airflow, and Snowflake. Our platform is designed to simplify your data transformation journey while providing excellent value by reducing your reliance on costly consultants. With our baked-in best practices, our customers have achieved faster implementations, enhanced efficiency, and long-term scalability.

The reason companies fail at leveraging analytics stems from the fact that people tend to focus on the destination instead of the journey that will lead to the solutions that will have the most impact on the business. Time and time again, I see people focus on the so-called shiny objects, like new tools, new techniques, or even new people, that appear to be the silver bullet everyone needs. The truth is, if you go back to the first principles and start with true alignment, good data processes, and user-centric experiences, project success and satisfaction are achievable.

Lack of Alignment Reduces Faith in the Solution

Every project I have been a part of started with a sense of optimism and excitement. The honeymoon phase was great. Everyone was united; we had gotten the funding, selected vendor partners, and purchased whatever technology was part of the solution. We all spoke the same language, everyone got to work, management started getting progress updates, and everyone thought we were off to a great start.

It wasn't until real decisions needed to be made that we realized the honeymoon was over. In every single instance, an excessive amount of time was spent in meetings  arguing and reaching some level of consensus until the next decision. The reason this happened was because we didn't really spend the time to get on the same page. People assumed that we were aligned because at a high level, we all talking about the key points of the given initiative: digital transformation, self-service analytics, customer mastering, data lakes, etc.

But we were not really thinking the same things. Everyone had different backgrounds and had expertise on different parts of the solution: regulatory requirement, technology limitations, end-user needs, etc. There were also things no one knew at the start, and we didn't have a north star to guide these decisions. We all appeared to be saying the same things, but we were thinking very differently.

I have seen the pressure to get started on a project and show progress lead to delays and ultimate dissatisfaction with the end result. On projects where we have spent a couple of weeks getting aligned using a structured approach to product discovery, we ended up with better estimates and better overall satisfaction.

In any analytics-related project, the same things apply: the team needs to understand the business objectives, the current state (so the new process isn't worse), the risks, and prioritize the high-level features. Most importantly, the team needs to align on what's NOT in the new solution and the prioritizing criteria such as quality, feature completeness, or usability that will be used when making decisions. Agile does not mean no planning.

Trust starts by listening to people and creating a shared vision that sets the right expectations from day one. You can create an achievable plan if everyone knows what you are trying to achieve.

People don't Trust the Data

Let's face it, your data processes get no love. This is usually because this is "too technical." Your users don't care about databases, schemas, tables, or columns, let alone the process of converting raw facts into business-ready insights. It's easy for management to see a fancy dashboard and get excited about the possibility of machine learning, but talk about data and people's eyes gloss over.

It kind of makes sense; most people don't understand how the power grid works. We all take it for granted. We flip a switch, the light turns on, and we move forward. No one cares about electricity until something goes wrong. In a lot of organizations, things go wrong with data more often than you would think. Sometimes people notice right away, but other times failures are silent. When something does go wrong, everyone goes into firefighting mode. Meetings are held, issues are discovered, and patches to "prevent" the failure are put in place. The time to think about the inevitable is not once things break; you need to anticipate failure and design for resilience.

The issue here is that we don't think of the process of going from raw data to insights as a single system. It is all interconnected and needs to be treated as such. When it comes to analytics, sometimes it feels like companies want to build a mansion on a foundation atop quicksand. Initially, all seems fine, and everyone is in the house decorating until someone notices that a corner of the house is sinking. Everyone goes outside, props up the corner, and they happily go back inside to decide what color to paint the next room.

You can't build a house on quicksand; you need to set up repeatable processes with quality built in from the start. If we want collaboration, we have to build it in. If you want to be able to do impact analysis, guess what? You can't retrofit that later if you didn't do it from the start. Having documented analytics is not magic; you need this to be part of the culture and part of the process. The good thing is that many smart people have faced the same issues, and there are examples we can see where people are doing things right.

If you want users to trust data analytics, they need to trust the data, and they need to believe in a solid process that is built on a solid foundation.  

Bad User Experiences Erode Confidence

When you try to please everyone, you please no one, and in many companies, technical teams try to do everything they are asked. They jump through hoops to deliver projects, but it is very common for people to be dissatisfied with the end results. I have also seen new tools used like old ones. Teams sometimes take the approach that the new process is just affecting some part of the current broken process, so they only incrementally change it. I have seen Tableau dashboards that are essentially Excel on the web with some automation.

Instead of asking users what they want, we need to understand what they need and why. What are they trying to accomplish? What's wrong with how they do things today? Is the new process / tool you are putting in place better than what they already have? Sometimes it makes more sense to leave a current process as-is until other parts of the system are improved.

When you understand the real need for an omni-channel dashboard or a sales dashboard, you design the solution to help you achieve that goal. If your users need to quickly get in and out of the tool, you can find ways to reduce the number of clicks it takes them to get there. You simplify access, and you surface the most important information first. You build the solution around them, and more importantly, you are able to justify your decisions and why certain things need to be de-prioritized. When users see that you empathize with them, they trust you. They don't push back on every choice because they know you have their best interests at heart because you have demonstrated time and again that you do care.

Conclusion

Getting decision-makers to trust data analytics is no different than getting anyone to trust anything. You need to start with alignment and set the right expectations; you need to build end-to-end processes that are robust; and you need to deliver the tools that facilitate the job users do.

Any experienced data engineer will tell you that efficiency and resource optimization are always top priorities. One powerful feature that can significantly optimize your dbt CI/CD workflow is dbt Slim CI. However, despite its benefits, some limitations have persisted. Fortunately, the recent addition of the --empty flag in dbt 1.8 addresses these issues. In this article, we will share a GitHub Action Workflow and demonstrate how the new --empty flag can save you time and resources.

What is dbt Slim CI?

dbt Slim CI is designed to make your continuous integration (CI) process more efficient by running only the models that have been changed and their dependencies, rather than running all models during every CI build. In large projects, this feature can lead to significant savings in both compute resources and time.

Key Benefits of dbt Slim CI

• Speed Up Your Workflows: Slim CI accelerates your CI/CD pipelines by skipping the full execution of all dbt models. Instead, it focuses on only the modified models and their dependencies and uses the defer flag to pull the unmodified models from production. So, if we have model A, B and C yet only make changes to C, then only model C will be run during the CI/CD process.

• Save Time, Snowflake Credits, and Money: By running only the necessary models, Slim CI helps you save valuable build time and Snowflake credits. This selective approach means fewer computational resources are used, leading to cost savings.

dbt Slim CI flags explained

dbt Slim CI is implemented efficiently using these flags:

--select state:modified:  The state:modified selector allows you to choose the models whose "state" has changed (modified) to be included in the run/build. This is done using the state:modified+ selector which tells dbt to run only the models that have been modified and their downstream dependencies.

--state <path to production manifest>: The --state flag specifies the directory where the artifacts from a previous dbt run are stored ie) the production dbt manifest. By comparing the current branch's manifest with the production manifest, dbt can identify which models have been modified.

--defer: The --defer flag tells dbt to pull upstream models that have not changed from a different environment (database). Why rebuild something that exists somewhere else? For this to work, dbt will need access to the dbt production manifest.

You may have noticed that there is an additional flag in the command above.  

--fail-fast: The --fail-fast flag is an example of an optimization flag that is not essential to a barebones Slim CI but can provide powerful cost savings. This flag stops the build as soon as an error is encountered instead of allowing dbt to continue building downstream models, therefore reducing wasted builds. To learn more about these arguments you can use have a look at our dbt cheatsheet.

dbt Slim CI with Github Actions before dbt 1.8

The following sample Github Actions workflow below is executed when a Pull Request is opened. ie) You have a feature branch that you want to merge into main.

Workflow Steps

Checkout Branch: The workflow begins by checking out the branch associated with the pull request to ensure that the latest code is being used.

Set Secure Directory: This step ensures the repository directory is marked as safe, preventing potential issues with Git operations.  

List of Files Changed: This command lists the files changed between the PR branch and the base branch, providing context for the changes and helpful for debugging.

Install dbt Packages: This step installs all required dbt packages, ensuring the environment is set up correctly for the dbt commands that follow. ‍

Create PR Database: This step creates a dedicated database for the PR, isolating the changes and tests from the production environment.

Get Production Manifest: Retrieves the production manifest file, which will be used for deferred runs and governance checks in the following steps.

Run dbt Build in Slim Mode or Run dbt Build Full Run: If a manifest is present in production, dbt will be run in slim mode with deferred models. This build includes only the modified models and their dependencies. If no manifest is present in production we will do a full refresh.

Grant Access to PR Database: Grants the necessary access to the new PR database for end user review.

Generate Docs Combining Production and Branch Catalog: If a dbt test is added to a YAML file, the model will not be run, meaning it will not be present in the PR database. However, governance checks (dbt-checkpoint) will need the model in the database for some checks and if not present this will cause a failure. To solve this, the generate docs step is added to merge the catalog.json from the current branch with the production catalog.json.

Run Governance Checks: Executes governance checks such as SQLFluff and dbt-checkpoint.

Problems with the dbt CI/CD Workflow

As mentioned in the beginning of the article, there is a limitation to this setup. In the existing workflow, governance checks need to run after the dbt build step. This is because dbt-checkpoint relies on the manifest.json and catalog.json. However, if these governance checks fail, it means that the dbt build step will need to run again once the governance issues are fixed. As shown in the diagram below, after running our dbt build, we proceed with governance checks. If these checks fail, we need to resolve the issue and re-trigger the pipeline, leading to another dbt build. This cycle can lead to unnecessary model builds even when leveraging dbt Slim CI.

‍

Leveraging the --empty Flag for Efficient dbt CI/CD Workflows

The solution to this problem is the --empty flag in dbt 1.8. This flag allows dbt to perform schema-only dry runs without processing large datasets. It's like building the wooden frame of a house—it sets up the structure, including the metadata needed for governance checks, without filling it with data. The framework is there, but the data itself is left out, enabling you to perform governance checks without completing an actual build.

Let’s see how we can rework our Github Action:

Workflow Steps

Checkout Branch: The workflow begins by checking out the branch associated with the pull request to ensure that the latest code is being used.

Set Secure Directory: This step ensures the repository directory is marked as safe, preventing potential issues with Git operations.  

List of Files Changed: This step lists the files changed between the PR branch and the base branch, providing context for the changes and helpful for debugging.

Install dbt Packages: This step installs all required dbt packages, ensuring the environment is set up correctly for the dbt commands that follow. ‍

Create PR Database: This command creates a dedicated database for the PR, isolating the changes and tests from the production environment.

Get Production Manifest: Retrieves the production manifest file, which will be used for deferred runs and governance checks in the following steps.

*NEW* Governance Run of dbt (Slim or Full) with EMPTY Models: If there is a manifest in production, this step runs dbt with empty models using slim mode and using the empty flag. The models will be built in the PR database with no data inside and we can now use the catalog.json to run our governance checks since the models. Since the models are empty and we have everything we need to run our checks, we have saved on compute costs as well as run time.  

Generate Docs Combining Production and Branch Catalog: If a dbt test is added to a YAML file, the model will not be run, meaning it will not be present in the PR database. However, governance checks (dbt-checkpoint) will need the model in the database for some checks and if not present this will cause a failure. To solve this, the generate docs step is added to merge the catalog.json from the current branch with the production catalog.json.

Run Governance Checks: Executes governance checks such as SQLFluff and dbt-checkpoint.

‍Run dbt Build: Runs dbt build using either slim mode or full run after passing governance checks.

Grant Access to PR Database: Grants the necessary access to the new PR database for end user review.

By leveraging the dbt --empty flag, we can materialize models in the PR database without the computational overhead, as the actual data is left out. We can then use the metadata that was generated during the empty build. If any checks fail, we can repeat the process again but without the worry of wasting any computational resources doing an actual build. The cycle still exists but we have moved our real build outside of this cycle and replaced it with an empty or fake build. Once all governance checks have passed, we can proceed with the real dbt build of the dbt models as seen in the diagram below.

‍

Conclusion

dbt Slim CI is a powerful addition to the dbt toolkit, offering significant benefits in terms of speed, resource savings, and early error detection. However, we still faced an issue of wasted models when it came to failing governance checks. By incorporating dbt 1.8’s  --empty flag into your CI/CD workflows we can reduce wasted model builds to zero, improving the efficiency and reliability of your data engineering processes.

🔗 Watch the vide where Noel explains the  --empty flag implementation in Github Actions:

Jinja templating in dbt offers flexibility and expressiveness that can significantly improve SQL code organization and reusability. There is a learning curve, but this cheat sheet is designed to be a quick reference for data practitioners, helping to streamline the development process and reduce common pitfalls.

Whether you're troubleshooting a tricky macro or just brushing up on syntax, bookmark this page. Trust us, it will come in handy and help you unlock the full potential of Jinja in your dbt projects.

If you find this cheat sheet useful, be sure to check out our Ultimate dbt Jinja Functions Cheat Sheet. It covers the specialized Jinja functions created by dbt, designed to enhance versatility and expedite workflows.

dbt Jinja: Basic syntax

This is the foundational syntax of Jinja, from how to comment to the difference between statements and expressions.

{#  Example Comment  #}

{% … %} e.g.: for, if

{{ … }} e.g.: ref(), source(), 

dbt Jinja: Variable assignment 

Define and assign variables in different data types such as strings, lists, and dictionaries.

                {% set my_string = "example" %}

{% set my_list = ["apple", "lemon"] %}

{% set my_dict = {"fruit_1": "apple",
                   "fruit_2": "lemon"} %}

‍

dbt Jinja: White space control 

Jinja allows fine-grained control over white spaces in compiled output. Understand how to strategically strip or maintain spaces.

          {%- ... %}

          {% ... -%}

          {%- ... -%}

dbt Jinja: Control flow

In dbt, conditional structures guide the flow of transformations. Grasp how to integrate these structures seamlessly.

{%- if target.name == 'dev' -%}
{{ some code }}
{%- elif target.name == 'prod' -%}
{{ some other code }}
{%- else -%}
{{ some other code }}
{%- endif -%}

dbt Jinja: Looping

Discover how to iterate over lists and dictionaries. Understand  simple loop syntax or accessing loop properties.

{%- for item in my_iterable -%}
  --Do something with item
  {{ item }}
{%- endfor -%}

          {% for item in list %}
  {% if loop.last %}   
    --This is the last item
    {{ item }}
  {% endif %}
{% endfor %}

{% for item in list %}
  {% if loop.first %}
    --first item
    {{ item }}
  {% endif %}
{% endfor %}

{% for item in list %}
   --This is item number
   {{ loop.index }}
{% endfor %}

{% set rating_categories = ["quality_rating",
                            "design_rating",
                            "usability_rating"] %}

SELECT product_id,
 {%- for col_name in rating_categories -%}
   AVG({{ col_name }}) as {{ column_name }}_average
   {%- if not loop.last  -%} 
     , 
   {%- endif -%}
 {%- endfor -%}
 FROM product_reviews
 GROUP BY 1

SELECT product_id,
   AVG(quality_rating) as quality_rating_average,
   AVG(design_rating) as design_rating_average,
   AVG(usability_rating) as usability_rating_average
FROM product_reviews
GROUP BY 1

{% set delivery_type_dict = {"a": "digital_download",
                             "b": "physical_copy"} %}

SELECT order_id,
{%- for type, column_name in delivery_type_dict.items() -%}
COUNT(CASE 
      WHEN delivery_method = '{{ type }}' THEN order_id 
      END) as {{ column_name }}_count
      {%- if not loop.last  -%}
       , 
      {%- endif -%}
      {%- endfor -%}
FROM order_deliveries
GROUP BY 1

SELECT order_id,
COUNT(CASE 
      WHEN delivery_method = 'a' THEN order_id 
      END) as digital_download_count,
COUNT(CASE 
      WHEN delivery_method = 'b' THEN order_id 
      END) as physical_copy_count
FROM order_deliveries
GROUP BY 1

dbt Jinja: Operators 

These logical and comparison operators come in handy, especially when defining tests or setting up configurations in dbt.

{% if condition1 and condition2 %}

{% if condition1 or condition2 %}

{{  not condition1 }}

‍

{% if 1 == 2 %}

{% if 1 != 2 %}

{% if 1 > 2 %}

{% if 1 < 2 %}

{% if 1 >= 2 %}

{% if 1 <= 2 %}

dbt Jinja: Variable tests

Within dbt, you may need to validate if a variable is defined or a if a value is odd or even. These Jinja Variable tests allow you to validate with ease.

                  {% if my_variable is defined %}
-- Handle conditions when variable exists
{% endif %}

{% if my_variable is none %}
-- Handle absence of my_variable
{% endif %}

{% if my_variable is even %}
-- Handle when my_variable is even
{% endif %}

{% if my_variable is odd %}
-- Handle when my_variable is odd
{% endif %}

{% if my_variable is string %}
-- Handle when my_variable is a string
{% endif %}

{% if my_variable is number %}
-- Handle when my_variable is a number
{% endif %}

dbt Jinja: Creating macros & tests

Macros are the backbone of advanced dbt workflows. Review how to craft these reusable code snippets and also how to enforce data quality with tests.

{% macro ms_to_sec(col_name, precision=3) %}   
  ( {{ col_name }} / 1000 )::numeric(16, {{ precision }})   
{% endmacro %}

SELECT order_id,       
  {{ ms_to_sec(col_name=time_ms, precision=3) }} as time_sec
FROM order_timings;

SELECT order_id,
(time_ms/ 1000 )::numeric(16, 3) AS time_sec
FROM order_timings;

{% macro create_schema(schema_name) %}
    CREATE SCHEMA IF NOT EXISTS {{ schema_name }};
{% endmacro %}

{% test over_10000(model, column_name) %}
  SELECT {{column_name}} 
  FROM {{ model }}   
  WHERE {{column_name}} > 10000     
{% endtest %}

version: 2

models:
  - name: my_model
    columns:
      - name: column_to_test
        tests:
          - over_10000
          - not_null

    
SELECT order_id, 
SUM(CASE
    WHEN amount < 0 THEN amount 
    ELSE 0 
    END) as total_refunded_amount,       
COUNT(CASE 
     WHEN amount < 0 THEN 1 
     END) as number_of_refunds  
FROM {{ ref('my_model') }}  
GROUP BY 1   
HAVING number_of_refunds > 5

dbt Jinja: Filters (aka Methods)

Fine-tune your dbt data models with these transformation and formatting utilities.

{{ "DATACOVES" | lower }} => "datacoves"

{{ "datacoves" | upper }} => "DATACOVES"

{{ variable_name | default("Default Value") }}    
If value exists => "Sample Value"
If value does not exist => "Default Value"

{{ "Datacoves   " | trim }} => "Datacoves"  

{{ "Datacoves" | replace("v", "d") }} => "Datacodes" 

{{ "Datacoves" | length }} => 9

{{ "datacoves" | capitalize }} => "Datacoves"  

{{ "datacoves managed platform" | capitalize }}
  => "Datacoves managed platform”

{{ print('-' * 20) }}

{{ "Datacoves"[0:4] }} => "Data"

{{ "Data coves".split(' ') }} => ["Data", "coves"]  

‍

{{ "20" | int }} => 20 

{{ 20 | float }} => 20.0 

{{ 20.1434 | round }} => 21

{{ 20.1434 | round(2) }} => 20.14

{{ 20.5 | round(method='floor') }} => 20 

{{ 20.5 | round(method='ceil') }} => 21

Please contact us with any errors or suggestions.

Implementing dbt (data build tool) can revolutionize your organization's data maturity, however, if your organization is not ready to take advantage of the benefits of dbt it might not be the right time to start. Why? Because the success of data initiatives often hinges on aspects beyond the tooling itself.  

Many companies rush into implementing dbt without assessing their organization’s maturity and this leads to poor implementation. The consequences that come from a poorly implemented dbt initiative can leave the organization frustrated, overwhelmed with technical debt, and wasted resources. To avoid these pitfalls and ensure your organization is truly ready for dbt, you should complete an assessment of your organization's readiness by answering the questions presented later in this article.

What is Data Maturity?

Before diving into the maturity assessment questions, it’s important to understand what data maturity means. Data maturity is the extent to which an organization can effectively leverage its data to drive business value. It encompasses multiple areas, including:

Data-Driven Culture: Fostering an environment where data is integral to decision-making processes.

Data Quality: Ensuring data is accurate, consistent, and reliable.

Data Governance: Implementing policies and procedures to manage data assets.

Data Integration: Seamlessly combining data from various sources for a unified view.

A mature data organization not only ensures data accuracy and consistency but also embeds data-driven decision-making into its core operations.  

How dbt Improves Data Maturity

By leveraging dbt's features, organizations can significantly enhance their data maturity, leading to better decision-making, improved data quality, robust governance, and seamless integration. For example:

Data-Driven Culture: By using dbt, you can improve many aspects that contribute to creating a data-driven culture within an organization. One way is by encouraging business users to be involved in providing or reviewing accurate model and column descriptions which are embedded in dbt. You can also involve them in defining what data to test with dbt. Better Data Quality will improve trust in the data. More trust in the data will always lead to more frequent use and reliance on it.

Data Quality and Observability: dbt enables automated testing and validation of data transformations. This ensures data quality by catching issues like schema changes or data anomalies early in the pipeline. As your data quality and data observability needs grow you can assess where you are on the data maturity curve.  For example, in a sales data model, we can write tests to ensure there are no negative order quantities and that each order has a valid customer ID. With dbt you can also understand data lineage and this can improve impact and root cause analysis when insights don’t seem quite right.

Data Governance: dbt facilitates version control and documentation for all transformations, enhancing transparency and accountability. Organizations can track changes to data models ensuring compliance with data governance policies.

Data Integration: dbt supports the integration of data from multiple sources by providing a framework for consistent and reusable transformations. This allows for the creation of unified data models that provide a holistic view of business operations.

dbt Readiness Assessment

Now that we understand what data maturity is and how dbt can help improve it, you might be ready to jump on the dbt bandwagon. But first, we encourage you to assess your organization’s readiness for dbt. The journey to data maturity involves not only choosing the right tools but also ensuring that your organization is philosophically and operationally prepared to take full advantage of these tools. It is important to recognize that dbt’s approach requires a shift in mindset towards modern data practices, emphasizing transparency, collaboration, and automation.

To determine if your organization is mature enough for dbt or if dbt is the right fit, consider the following assessment questions:

Are you philosophically aligned?

dbt requires a philosophical alignment with its principles, such as ELT (Extract, Load, Transform) instead of the traditional ETL (Extract, Transform, Load) approach. dbt is also based on idempotency meaning that given the same input, you will always get the same output. This is different than traditional ETL that may use incompatible constructs like Auto-Incrementing Primary Keys. If your organization prefers processes that are incompatible with dbt’s methodology, you will face challenges fighting the dbt framework to make it do something it was not intended to do.

Are you just going to do a lift and shift?

Simply migrating existing processes and code to dbt without rethinking them won’t leverage dbt’s full potential. Assess whether you’re ready to redesign your workflows to take advantage of dbt’s capabilities such as incremental tables, snapshots, seeds, etc.

Are you going to take advantage of features like data quality and documentation?

dbt offers excellent features for data quality and documentation. Evaluate if your team is prepared to prioritize the utilization of these features to enhance transparency and trust in your data. Tests and model descriptions will not write themselves. When it comes to good descriptions, they shouldn't come from a data engineering team that does not know how the data is used or the best data quality rules to implement. Good descriptions must involve business user review at a minimum.

Are you going to open up dbt to teams outside of IT?

The goal of dbt is to empower various teams including IT and business users by using the same tooling. Consider if your organization is ready to foster this cross-functional collaboration. When you implement dbt correctly, you will empower anyone who knows SQL to contribute. You can have multiple teams contribute to the insight delivery process and still ensure proper governance and testing before updating production.

Are you going to automate processes like deployment and testing?

Automation is key to achieving efficiency with dbt. Implementing automated deployment, testing, and CI/CD pipelines can significantly improve your workflows. If you aren’t ready to automate, the benefits of dbt may not be fully realized. If you simply put in dbt without thinking about the end-to-end process and the failure points, you will miss opportunities for errors. The spaghetti code you have today didn't happen just because you were not using dbt.

Are you changing more than just tooling?

dbt is a framework, not a silver bullet. Merely changing tools without altering your underlying processes will not solve existing issues. This is a huge issue with organizations that have not done the work to create a data-driven culture. Assess if your team is ready to adopt better naming conventions and more structured processes to make data more understandable.  

Conclusion

Data immaturity might manifest as a reliance on manual processes, lack of data quality controls, or poor documentation practices. These factors can derail the effective implementation of dbt since dbt thrives in environments where data practices are robust and standardized. In other words, dbt alone will not solve these problems.

Ensuring your organization is ready for the changes that come with implementing dbt is not just best practice, it is essential for success. By thoroughly assessing your readiness, you can avoid technical debt, optimize your workflows, and fully harness the power of dbt. Remember, dbt is a powerful tool, but its effectiveness depends on the readiness of your organization to improve data practices and its alignment with dbt’s philosophy.

Digital transformation is often seen through the lens of technological advancement and process optimization. Most blog posts and guides out there revolve around implementing new software, automating tasks, and digitizing operations. Yet, there's a pivotal element that's frequently overlooked in these discussions, especially when it comes to an enterprise: the mindset and culture within an organization. This article aims to shed light on why this is crucial in achieving true digital transformation. But first, let's investigate what digital transformation is and why it is important.

Digital transformation defined

Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how it operates and delivers value to customers. It is more than just a technological upgrade; it is a cultural shift that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure. This often means walking away from long-standing business processes that companies were built upon to embrace new ways of working. Most organizations find this part the most challenging.

Why is digital transformation important

• Keeping Up with the Digital Economy: In a world where technology evolves rapidly, businesses must adapt to stay relevant. Digital transformation allows companies to remain competitive in an increasingly digital economy.
• Enhanced Data Collection and Analysis: Digital transformation creates a system for gathering the right data and fully utilizing it for better business decisions, efficiencies, and customer insights.
• Customer Expectations: Today's customers expect a seamless digital experience. Businesses need to engage with customers on their terms, using digital tools and platforms that are convenient and user-friendly.
• Increased Agility and Innovation: Adopting digital solutions empowers organizations to be more agile and responsive to changes in the marketplace or industry. It fosters a culture of innovation, encouraging new ideas and approaches.
• Operational Efficiency: Automation and streamlining of processes reduce operational costs and improve efficiency. This allows employees to focus on more strategic tasks that add value to the business.
• Risk Management and Compliance: With the increasing importance of data security and privacy, digital transformation helps businesses keep up with changing regulations and protect sensitive information.
• Sustainability: Digital processes can reduce waste and improve energy efficiency, contributing to more sustainable business practices.

Enterprise digital transformation

To achieve digital transformation in an enterprise 9 times out of 10 there must be a change in company culture. However, changing a company's culture is a formidable task. It is rare to hear statements like, “We need to fundamentally change our problem-solving approach.” This realization became clear to me through my past experiences as I noticed that managers often lacked the influence to drive change at the highest organizational levels. Additionally, the pressure to deliver quick results within budget cycles frequently hindered genuine cultural transformation.

During my tenure at various companies, under numerous managers, the consistent message was the need for improvement. However, I have come to understand that organizations, much like fireflies, develop their own rhythms. It is this unique rhythm that sets apart innovative and transformative companies from those that merely follow without achieving similar success. What do I mean by this? Let’s turn to nature for an explanation.

Firefly phenomenon - Does it mean conformity or innovation in your organization?

Nature is fascinating, especially when observing how hundreds or thousands of fireflies can synchronize their flashes.

In organizations, a similar phenomenon occurs. People will sync up and follow the status quo, even if it is not what is best for the organization. This dramatically hinders digital transformation because the loudest are not always right and yet they cause others to sync up with them. This will cause innovation to be stopped in its tracks.

In addition to this firefly phenomenon, often action differs from ambition. I recall a staff meeting with a former CIO discussing a future less dependent on Microsoft and more open to non-Windows devices. It was clear that iPhones were going to change the corporate landscape. Despite this, every new tool implemented was still optimized for Internet Explorer. This discrepancy between ambition and action often drives analytical people like me to frustration. To effect change, persistence is key. I have had ideas initially dismissed as “not my job,” only to see one later turn into a patented invention.

This manifests itself in other ways as well; have you ever seen a company advocate for fewer meetings while simultaneously criticizing those who do not include “everyone” in decision-making? I have been in such situations and can attest that decision-making by committee is not inherently superior. In fact, the more people involved in an initiative, the less effective it tends to be. This, I believe, is due to the Dunning-Kruger effect.

The more people you involve in a transformation initiative, the more likely the discussions will deteriorate to bike shedding discussions. When there is a disconnect between what is said and what is done, people take notice, and it breeds discontent.

One firefly can only affect their neighbors

Even in my most successful transformation initiatives, the radius of transformation has been limited to my sphere of influence. Sure, some of my tools and processes got global and cross-functional acceptance, but the underlying principles never took hold because they were too radical for the organization at the time. I was not part of the IT organization so the things I did were typically seen as shadow IT. Instead of focusing on what I should not be doing, it would have been more progressive for them to see how I was practicing Agile principles. They could have inquired about how my project was doing DevOps before that was in style, or how it was that this non-sanctioned product was extremely well received and people sought me out to help them improve their processes.

This means if you want the organization to be more innovative, you need to find the obstacles that hold people back from being innovative. Often politics and bureaucracy impact an initiative more than the solution itself. If you force everyone to comply with existing tools and processes, then you are imposing a constraint on the team that will limit innovation.

A typical way this manifests itself is leadership pushing the idea that one platform or process can solve every need. This can come in the form of imposing that a particular group do data transformation, or a visualization tool be the way that everyone can do analytics. I have never seen one tool that is good at everything, and you end up balancing the single solution with an unmanageable array of tools and processes. A healthy organization is a learning organization that is always open to improvement.  When management encourages pushing boundaries and not taking anything as fact then the company can innovate.  

A great example of driving innovation is seen in the approach of Steve Jobs, co-founder of Apple Inc. Jobs was known for his ability to challenge conventional wisdom and existing standards in the technology industry. He emphasized the importance of understanding the fundamental principles underlying a problem to innovate and create groundbreaking solutions. One notable instance was the development of the iPhone, which revolutionized the smartphone industry. Jobs and his team did not just improve on existing phones; they rethought what a phone could be, focusing on user experience and simplicity. This approach led to a product that dramatically altered how people interact with technology.  

As a leader, you need to look for the fireflies who are using first principles like Steve Jobs to deliver innovative solutions and nurture, or create, a corporate culture that truly challenges what has been done without artificial constraints.

Reasoning by first principles removes the impurity of assumptions and conventions. What remains is the essentials. It’s one of the best mental models you can use to improve your thinking because the essentials allow you to see where reasoning by analogy might lead you astray.

Most fireflies eventually comply, or fly away – Loss of innovators

The transformative and innovative thinkers will either comply or leave, both of which are undesirable. In my case I tended to leave. In every organization where I have worked, I have managed to make a significant impact, often through sheer determination. During my time at one such company, our goal was to introduce a data catalog. By analyzing the problem I was able to discern what was essential for our organization vs an elaborate and idealistic vision which was capable of doing everything. While the IT organization felt it would be better to create a home-grown catalog I understood that our biggest obstacle was getting people to use a catalog in the first place, so time to market was critical. I found that Alation met the needs we had and IT kept to their vision to build an all encompassing catalog, In 3 months I had deployed Alation and 1.5 years later, the home grown solution was a tenth as good. This approach of breaking down the problem to its basic elements and building up from there was critical. It is often underestimated how challenging it is to develop and maintain custom software.  This experience highlights the effectiveness of first principles thinking in deploying practical and efficient solutions.

The reality is that not everyone possesses the tenacity to advocate for change, especially in the face of substantial resistance. Not only that, but I have also witnessed people being ostracized for thinking differently, while others were promoted for fitting in. It is crucial to seek out divergent thinkers and consider the validity of their perspectives, instead of forcing them to conform. This is why true digital transformation necessitates a shift in culture.  

When an individual, much like a firefly that does not flash in unison with the rest, finds themselves out of sync with the collective rhythm, they face a decision: conform and synchronize with the group or venture out to find a new collective that resonates with their unique spark.

How do we change the flash for all? Aligning mindsets for transformation

True transformational change must come from the top. Achieving enterprise digital transformation requires a deep and bold questioning of the status quo. We must critically assess our processes: Is a particular task truly necessary for a certain group? Can we identify and eliminate inefficiencies? Will adding another layer of approval or inspection genuinely enhance outcomes? It is essential to remember that human behavior often has a more profound impact than any technology or process we implement. When decision-making is centralized within one group, solutions are inevitably skewed to reflect their viewpoint. Too often, I have witnessed decisions justified by cost considerations that, upon closer inspection, proved detrimental in the broader context. An effective strategy involves analyzing the entire system, recognizing that optimizing the whole may require accepting lower efficiency in some areas.

The key is to align with the needs of users and the organization and engage leadership in this journey. With a united front, tackling the 'corporate dragons' becomes a more manageable endeavor. One practical approach is employing methodologies like the 'Job to be Done' framework.

Conclusion

Company culture and change management are frequently overlooked in the pursuit of process improvement. Employees operate within their limitations, while management ponders the lack of innovation and agility compared to other companies. The simpler path might seem to be increasing staff or updating technology, but the heart of transformation lies in the mindset of the organization. Leaders aiming for a lasting impact must embrace first principles thinking, ready to scrutinize and challenge established norms. Transformational change rarely stems from incremental improvements; truly innovative companies are those that dare to think and act differently. The organization thus faces a pivotal choice: will it adapt to a new rhythm, or compel its 'new fireflies' to fall in line with the existing order?

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.

The top dbt alternatives we will cover are:

Why Teams Look for dbt Alternatives

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.

Categories of dbt Alternatives

Organizations evaluating dbt alternatives typically compare tools across three categories. Each category reflects a different approach to data transformation, development preferences, and organizational maturity.

dbt Cloud Alternatives

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

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.

DIY dbt Core

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.

dbt alternatives – Code based ETL tools

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

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.

Benefits

Efficient Development Environments:
Virtual environments reduce unnecessary recomputation and speed up iteration.

Considerations

Part of the Fivetran Ecosystem:
SQLMesh was acquired by Fivetran, which may influence its future roadmap and level of independence.

Dataform

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

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 Data

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.

dbt alternatives – Graphical ETL tools

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

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

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

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)

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

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.

SSIS (SQL Server Integration Services)

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.

Why These dbt Alternatives Exist: The Full Context

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.

Should You DIY a dbt Data Platform?

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.

When DIY Makes Sense

• The team has strong platform engineering expertise
• Pipelines are relatively simple
• Security and compliance needs are minimal
• The organization prefers to own and operate every part of the stack

When DIY Becomes a Liability

• Multiple analytics engineers need consistent development environments
• Governance, auditing, or private deployment become required
• Pipelines need enterprise-grade orchestration
• Upgrades and maintenance begin consuming valuable engineering time

For many organizations, DIY works in the early stages but becomes difficult to sustain as the platform matures.

How to Choose the Right dbt Alternative

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.

1. Team Skills and Workflow Preferences

• SQL-first teams: Tools like dbt and Dataform work well for analysts and analytics engineers.
• Engineering-first teams: SQLMesh, and AWS Glue offer deeper CI integration, testing, and Python support.‍
• Mixed-skill teams: GUI tools like Matillion, Informatica, and Alteryx provide visual development.

2. Governance and Security Requirements

• Need for private cloud or VPC deployment
• Centralized identity and access management
• Audit logging and compliance standards
• Ability to control data movement and network boundaries

If these are priorities, a platform with secure deployment options or multi-engine support may be a better fit than dbt Cloud.

3. Complexity of Pipelines

• Simple pipelines may work with lightweight tools
• Complex, multi-team pipelines benefit from strong orchestration, consistent environments, and guardrails
• SQL-only tools may fall short when pipelines require Python-based logic or mixed-language workflows

4. Integration and Ecosystem Compatibility

• Choose a tool that integrates cleanly with your cloud environment and data warehouse
• Engineering-forward teams may prioritize CI/CD and Git workflows
• Analytics-focused or traditional Data Engineering teams may value GUI tools

5. Vendor Lock-In and Long-Term Flexibility

Recent consolidation in the ecosystem has raised concerns about vendor dependency. Organizations that want long-term flexibility often look for:

• Multi-engine support
• Open-source components
• Tooling that can be run in their cloud environment

6. Total Cost of Ownership

Consider platform fees, engineering maintenance, onboarding time, and the cost of additional supporting tools such as orchestrators, IDEs, and environment management

Final dbt alternative Recommendation

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.

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SQL databases are great for organizing, storing, and retrieving structured data essential to modern business operations. These databases use Structured Query Language (SQL), a gold standard tool for managing and manipulating data, which is universally recognized for its reliability and robustness in handling complex queries and vast datasets.

SQL is so instrumental to database management that databases are often categorized based on their use of SQL. This has led to the distinction between SQL databases, which use Structured Query Language for managing data, and NoSQL databases, which do not rely on SQL and are designed for handling unstructured data and different data storage models. If you are looking to compare SQL databases or just want to deepen your understanding of these essential tools, this article is just for you.

What is an Open Source Database?  

Open source databases are software systems whose source code is publicly available for anyone to view, modify, and enhance. This article covers strictly open source SQL databases. Why? Because we believe that they bring additional advantages that are reshaping the data management space. Unlike proprietary databases that can be expensive and restrictive, open source databases are developed through collaboration and innovation at their core. This not only eliminates licensing fees but also creates a rich environment of community-driven enhancements. Contributors from around the globe work to refine and evolve these databases, ensuring they are equipped to meet the evolving demands of the data landscape.  

Why Use Open Source Databases?

Cost-effectiveness: Most open source databases are free to use, which can significantly reduce the total cost of ownership.

Flexibility and Customization: Users can modify the database software to meet their specific needs, a benefit not always available with proprietary software.

Community Support: Robust communities contribute to the development and security of these databases, often releasing updates and security patches faster than traditional software vendors.

OLTP vs OLAP

When selecting a database, it is important to determine your primary use case. Are you frequently creating, updating, or deleting data? Or do you need to analyze large volumes of archived data that doesn't change often? The answer should guide the type of database system you choose to implement.  

In this article we will be touching on OLTP and OLAP open source SQL databases. These databases are structured in different ways depending on the action they wish to prioritize analytics, transactions, or a hybrid of the two.  

What is OLTP?

OLTP or Online Transaction Processing databases are designed to manage and handle high volumes of small transactions such as inserting, updating, and/or deleting small amounts of data in a database. OLTP databases can handle real-time transactional tasks due to their emphasis on speed and reliability. The design of OLTP databases is highly normalized to reduce redundancy and optimizes update/insert/delete performance. OLTP databases can be used for analytics but this is not recommended since better databases suited for analytics exist.

Characteristics of OLTP:

• Handles large numbers of transactions by many users.
• Operations are typically simple (e.g., updating a record or retrieving specific record details).
• Focus on quick query processing and maintaining data integrity in multi-access environments.
• Data is highly normalized.

When to use OLTP?

Use OLTP if you are developing applications that require fast, reliable, and secure transaction processing. Common use cases include but are not limited to:  

E-commerce: Order placement, payment processing, customer profile management, and shopping cart updates.

Banking: Account transactions, loan processing, ATM operations, and fraud detection.

Customer Relationship Management (CRM): Tracking customer interactions, updating sales pipelines, managing customer support tickets, and monitoring marketing campaigns.

What is OLAP?

OLAP or Online Analytical Processing databases are designed to perform complex analyses and queries on large volumes of data. They are optimized for read-heavy scenarios where queries are often complicated and involve aggregations such as sums and averages across many datasets. OLAP databases are typically denormalized, which improves query performance but come with the added expense of storage space and slower update speeds.

Characteristics of OLAP:

• Designed for analysis and reporting functions.
• Queries are complex and involve large volumes of data.
• Focus on maximizing query speed across large datasets.
• Data may be denormalized to expedite query processing.

When to use OLAP?

Use OLAP if you need to perform complex analysis on large datasets to gather insights and support decision making. Common use cases include but are not limited to:

Retail Sales Data Analysis: A retail chain consolidates nationwide sales data to analyze trends, product performance, and customer preferences.

Corporate Performance Monitoring: A multinational uses dashboards to track financial, human resources, and operational metrics for strategic decision-making.

Financial Analysis and Risk Management: A bank leverages an OLAP system for financial forecasting and risk analysis using complex data-driven calculations.

In practice, many businesses will use both types of systems: OLTP systems to handle day-to-day transactions and OLAP systems to analyze data accumulated from these transactions for business intelligence and reporting purposes.

Now that we are well versed in OLTP vs OLAP, let's dive into our open source databases!  

Open Source OLTP Databases

PostgreSQL

A row-oriented database, often considered the world’s most advanced open source database. PostgreSQL offers extensive features designed to handle a range of workloads from single machines to data warehouses or web services with many concurrent users.

Best Uses: Enterprise applications, complex queries, handling large volumes of data.

SQLite

SQLite is a popular choice for embedded database applications, being a self-contained, high-reliability, and full-featured SQL database engine. This database is a File-based database which means that they store data in a file (or set of files) on disk, rather than requiring a server-based backend. This approach has several key characteristics and advantages such as being lightweight, portable, easy to use, and self-contained.

Best Uses: Mobile applications, small to medium-sized websites, and desktop applications.

MariaDB

A columnar database and offshoot of MySQL. MariaDB was created by the original developers of MySQL after concerns over its acquisition by Oracle. It is widely respected for its performance and robustness.

Best Uses: Web-based applications, cloud environments, or as a replacement for MySQL.

Firebird

Firebird is a flexible relational database offering many ANSI SQL standard features that run on Linux, Windows, and a variety of Unix platforms. This database can handle a hybrid approach of OLTP and OLAP due to its multi-generational architecture and because readers do not block writers when accessing the same data.

Best Uses: Small to medium enterprise applications, particularly where complex, customizable database systems are required.

Open Source OLAP Databases

ClickHouse

Known for its speed, ClickHouse is an open-source column-oriented, File-based database management system that is great at real-time query processing over large datasets.  As mentioned earlier in the article, File-based databases bring many benefits They make use of data compression, disk storage of data, parallel processing on multiple cores, distributed processing on multiple servers and more.

Best Uses: Real-time analytics and managing large volumes of data.

DuckDB

Similar to SQLite, DuckDB is an embedded file-based database, however, DuckDB is a column-oriented database that is designed to execute analytical SQL queries fast and efficiently. This database has no dependencies making it a simple, efficient, and portable database. Since it is file-based this means DuckDB runs embedded within the host process, which allows for high-speed data transfer during analytics.

Best Uses: Analytical applications that require fast, in-process SQL querying capabilities.

StarRocks

StarRocks is a performance-oriented, columnar distributed data warehouse designed to handle real-time analytics. StarRocks also supports hybrid row-column storage. It is known for its blazing-fast massively parallel processing (MPP) abilities. Data can be ingested at a high speed and updated and deleted in real time making it perfect for real-time analytics on fresh data.

Best Uses: Real-time analytical processing on large-scale datasets.

Doris

Doris is an MPP-based, column-oriented data warehouse, aimed at providing high performance and real-time analytical processing. Doris can support highly concurrent point query scenarios and high-throughput complex analytic scenarios. Its high speed and ease of use despite working with large amounts of data make it a great option.

Best Uses: Real-time OLAP applications and scenarios demanding fast data processing and complex aggregation.

Trino

Even though Trino is not a database, but rather a query engine that allows you to query your databases, we felt it is a powerful addition to this open source list. Originally developed by Facebook and known as PrestoSQL, Trino is designed to query large data warehouses and big data systems rapidly. Since it is great for working with terabytes or petabytes of data it is an alternative to tools such as Hive or Pig. However, it can also operate on traditional relational databases and other data sources such as Cassandra. One major benefit is that Trino allows you to perform queries across different databases and data sources. This is known as query federation.  

Best Uses: Distributed SQL querying for big data solutions.

Citus

While this is not a separate open source database, we felt it was a good addition to the list because Citus is an extension to PostgreSQL that that transforms your Postgres database into a distributed database. This enables it to scale horizontally.

Best Uses: Scalable PostgreSQL applications, especially those needing to handle multi-tenant applications and real-time analytics over large datasets.

Conclusion  

Open source SQL databases provide a variety of options for organizations and developers seeking flexible, cost-effective solutions for data management. Whether your needs are for handling large data sets, real-time analytics, or robust enterprise applications, there is likely an open source database out there for you.