Dbt & Airflow

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"} %}

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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"]  

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{{ "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.

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.

‍

dbt is wildly popular and has become a fundamental part of many data stacks. While it’s easy to spin up a project and get things running on a local machine, taking the next step and deploying dbt to production isn’t quite as simple.  

In this article we will discuss options for deploying dbt to production, comparing some high, medium, and low effort options so that you can find which works best for your business and team. You might be deploying dbt using one of these patterns already; if you are, hopefully this guide will help highlight some improvements you can make to your existing deployment process.  

We're going to assume you know how to run dbt on your own computer (aka your local dbt setup).  We’re also going to assume that you either want to or need to run dbt in a “production” environment – a place where other tools and systems make use of the data models that dbt creates in your warehouse.

Enhancing understanding of dbt deployment

The deployment process for dbt jobs extends beyond basic scheduling and involves a multifaceted approach. This includes establishing various dbt environments with distinct roles and requirements, ensuring the reliability and scalability of these environments, integrating dbt with other tools in the (EL)T stack, and implementing effective scheduling strategies for dbt tasks. By focusing on these aspects, a comprehensive and robust dbt deployment strategy can be developed. This strategy will not only address current data processing needs but also adapt to future challenges and changes in your data landscape, ensuring long-term success and reliability.

dbt environments

In deploying dbt you have the creation and management of certain dbt environments. The development environment is the initial testing ground for creating and refining dbt models. It allows for experimentation without impacting production data. Following this, the testing environment, including stages like UAT and regression testing, rigorously evaluates the models for accuracy and performance. Finally, the production environment is where these models are executed on actual data, demanding high stability and performance.

Data reliability and scalability

Reliability and scalability of data models are also important. Ensuring that the data models produce accurate and consistent results is essential for maintaining trust in your data. As your data grows, the dbt deployment should be capable of scaling, handling increased volumes, and maintaining performance.

End-to-End data pipeline integration

Integration with other data tools and systems is another key aspect. A seamless integration of dbt with EL tools, data visualization platforms, and data warehouses ensures efficient data flow and processing, making dbt a harmonious component of your broader data stack.

dbt job scheduling

Effective dbt scheduling goes beyond mere time-based scheduling. It involves context-aware execution, such as triggering jobs based on data availability or other external events. Managing dependencies within your data models is critical to ensure that transformations occur in the correct sequence. Additionally, adapting to varying data loads is necessary to scale resources effectively and maintain the efficiency of dbt job executions.

The main flavors for dbt deployment are:  

• Cron Job
• Cloud Runner Service (like dbt Cloud)
• Integrated Platform (like Datacoves)
• Fully Custom (like Airflow, Astronomer, etc)

They each have their place, and the trade-offs between setup costs and long-term maintainability is important to consider when you’re choosing one versus another.  

We can compare these dbt deployment options across the following criteria:

• Ease of Use / Implementation
• Required Technical Ability
• Configurability
• Customization
• Best for End-to End Deployment

Cron job

Cron jobs are scripts that run at a set schedule. They can be defined in any language. For instance, we can use a simple bash script to run dbt. It’s just like running the CLI commands, but instead of you running them by hand, a computer process would do it for you.  

Here’s a simple cron script:

In order to run on schedule, you’ll need to add this file to your system’s crontab.

As you can tell, this is a very basic dbt run script; we are doing the bare minimum to run the project. There is no consideration for tagged models, test, alerting, or more advanced checks.  

Even though Cron jobs are the most basic way to deploy dbt there is still a learning curve. It requires some technical skills to set up this deployment. Additionally, because of its simplicity, it is pretty limited. If you are thinking of using crons for multi-step deployments, you might want to look elsewhere.  

While it's relatively easy to set up a cron job to run on your laptop this defeats the purpose of using a cron altogether. Crons will only run when the daemon is running, so unless you plan on never turning off your laptop, you’ll want to set up the cron on an EC2 instance (or another server). Now you have infrastructure to support and added complexity to keep in mind when making changes. Running a cron on an EC2 instance is certainly doable, but likely not the best use of resources. Just because it can be done does not mean it should be done. At this point, you’re better off using a different deployment method.

The biggest downside, however, is that your cron script must handle any edge cases or errors gracefully.  If it doesn’t, you might wind up with silent failures – a data engineer’s worst enemy.  

Who should use Cron for dbt deployment?

Cron jobs might serve you well if you have some running servers you can use, have a strong handle on the types of problems your dbt runs and cron executions might run into, and you can get away with a simple deployment with limited dbt steps. It is also a solid choice if you are running a small side-project where missed deployments are probably not a big deal.  

Use crons for anything more complex, and you might be setting yourself up for future headaches.  

Ease of Use / Implementation – You need to know what you’re doing

Required Technical Ability – Medium/ High

Configurability – High, but with the added complexity of managing more complex code

Customization – High, but with a lot of overhead. Best to keep things very simple  

Best for End-to-End Deployment - Low.

Cloud Service Runners (like dbt Cloud)

Cloud Service Runners like dbt Cloud are probably the most obvious way to deploy your dbt project without writing code for those deployments, but they are not perfect.  

dbt Cloud is a product from dbt Labs, the creators of dbt. The platform has some out-of-the-box integrations, such as Github Actions and Webhooks, but anything more will have to be managed by your team. While there is an IDE (Integrated Developer Experience) that allows the user to write new dbt models, you are adding a layer of complexity by orchestrating your deployments in another tool. If you are only orchestrating dbt runs, dbt Cloud is a reasonable choice – it's designed for just that.  

However, when you want to orchestrate more than just your dbt runs – for instance, kickoff multiple Extract / Load (EL) processes or trigger other jobs after dbt completes – you will need to look elsewhere.  

dbt Cloud will host your project documentation and provide access to its APIs. But that is the lion’s share of the offering. Unless you spring for the Enterprise Tier, you will not be able to do custom deployments or trigger dbt runs based on incoming data with ease.  

Deploying your dbt project with dbt Cloud is straightforward, though. And that is its best feature. All deployment commands use native dbt command line syntax, and you can create various "Jobs" through their UI to run specific models at different cadences.

Who should use dbt Cloud for dbt deployment?

If you are a data team with data pipelines that are not too complex and you are looking to handle dbt deployments without the need for standing up infrastructure or stringing together advanced deployment logic, then dbt Cloud will work for you. If you are interested in more complex triggers to kickoff your dbt runs - for instance, triggering a run immediately after your data is loaded – there are other options which natively support patterns like that. The most important factor is the complexity of the pieces you need to coordinate, not necessarily the size of your team or organization.  

Overall, it is a great choice if you’re okay working within its limitations and support a simple workflow. As soon as you reach any scale, however, the cost may be too high.  

Ease of Use / Implementation – Very easy

Required Technical Ability – Low

Configurability – Low / Medium

Customization – Low

Best for End-to-End Deployment - Low

Integrated platform (like Datacoves)

The Modern Data Stack is a composite of tools. Unfortunately, many of those tools are disconnected because they specialize in handling one of the steps in the ELT process. Only after working with them do you realize that there are implicit dependencies between these tools. Tools like Datacoves bridge the gaps between the tools in the Modern Data Stack and enable some more flexible dbt deployment patterns. Additionally, they cover the End-to-End solution, from Extraction to Visualization, meaning it can handle steps before and after Transformation.

Coordinating dbt runs with EL processes

If you are loading your data into Snowflake with Fivetran or Airbyte, your dbt runs need to be coordinated with those EL processes. Often, this is done by manually setting the ETL schedule and then defining your dbt run schedule to coincide with your ETL completion. It is not a hard dependency, though. If you’re processing a spike in data or running a historical re-sync, your ETL pipeline might take significantly longer than usual. Your normal dbt run won’t play nicely with this extended ETL process, and you’ll wind up using Snowflake credits for nothing.

This is a common issue for companies moving from early stage / MVP data warehouses into more advanced patterns. There are ways to connect your EL processes and dbt deployments with code, but Datacoves makes it much easier. Datacoves will trigger the right dbt job immediately after the load is complete. No need to engineer a solution yourself. The value of the Modern Data Stack is being able to mix and match tools that are fit for purpose.

Seamless integration and orchestration

Meeting established data freshness and quality SLAs is challenging enough, but with Datacoves, you’re able to skip building custom solutions for these problems. Every piece of your data stack is integrated and working together. If you are orchestrating with Airflow, then you’re likely running a Docker container which may or may not have added dependencies. That’s one common challenge teams managing their own instances of Airflow will meet, but with Datacoves, container / image management and synchronization between EL and dbt executions are all handled on the platform. The setup and maintenance of the scalable Kubernetes infrastructure necessary to run Airflow is handled entirely by the Datacoves platform, which gives you flexibility but with a lower learning curve. And, it goes without saying that this works across multiple environments like development, UAT, and production.

Streamlining the Datacoves experience

With the End-to-End Pipeline in mind, one of the convenient features is that Datacoves provides a singular place to access all the tools within your normal analytics workflow - extraction + load, transformation, orchestration, and security controls are in a single place. The implicit dependencies are now codified; it is clear how a change to your dbt model will flow through to the various pieces downstream.  

Who should use Datacoves for dbt deployment?

Datacoves is for teams who want to introduce a mature analytics workflow without the weight of adopting and integrating a new suite of tools on their own. This might mean you are a small team at a young company, or an established analytics team at an enterprise looking to simplify and reduce platform complexity and costs.

There are some prerequisites, though. To make use of Datacoves, you do need to write some code, but you’ll likely already be used to writing configuration files and dbt models that Datacoves expects. You won't be starting from scratch because best practices, accelerators, and expertise are already provided.

Ease of Use / Implementation – You can utilize YAML to generate DAGS for a simpler approach, but you also have the option to use Python DAGS for added flexibility and complexity in your pipelines.

Required Technical Ability – Medium

Configurability – High

Customization – High. Datacoves is modular, allowing you to embed the tools you already use

Best for End-to-End Deployment - High. Datacoves takes into account all of the factors of dbt Deployment

Fully custom (like Airflow, Dagster, etc)

What do you use to deploy your dbt project when you have a large, complex set of models and dependencies? An orchestrator like Airflow is a popular choice, with many companies opting to use managed deployments through services such as Astronomer.  

For many companies – especially in the enterprise – this is familiar territory. Adoption of these orchestrators is widespread. The tools are stable, but they are not without some downsides.  

These orchestrators require a lot of setup and maintenance. If you’re not using a managed service, you’ll need to deploy the orchestrator yourself, and handle the upkeep of the infrastructure running your orchestrator, not to mention manage the code your orchestrator is executing. It’s no small feat, and a large part of the reason that many large engineering groups have dedicated data engineering and infrastructure teams.  

Running your dbt deployment through Airflow or any other orchestrator is the most flexible option you can find, though. The increase in flexibility means more overhead in terms of setting up the systems you need to run and maintain this architecture. You might need to get DevOps involved, you’ll need to move your dbt project into a Docker image, you’ll want an airtight CI/CD process, and ultimately have well defined SLAs. This typically requires Docker images, container management, and some DevOps work. There can be a steep learning curve, especially if you’re unfamiliar with what’s needed to take an Airflow instance to a stable production release.

There are 3 ways to run Airflow, specifically – deploying on your own, using a managed service, or using an integrated platform like Datacoves. When using a managed service or an integrated platform like Datacoves, you need to consider a few factors:

1. Airflow use cases

2. Ownership and contributing teams

3. Integrations with rest of your stack

Airflow use cases

Airflow is a multi-purpose tool. It’s not just for dbt deployments. Many organizations run complex data engineering pipelines with Airflow, and by design, it is flexible. If your use of Airflow extends well beyond dbt deployments or ELT jobs oriented around your data warehouse, you may be better suited for a dedicated managed service.

Ownership and contributing teams

Similarly, if your organization has numerous teams dedicated to designing, building and maintaining  your data infrastructure, you may want to use a dedicated Airflow solution. However, not every organization is able to stand up platform engineering teams or DevOps squads dedicated to the data infrastructure. Regardless of the size of your team, you will need to make sure that your data infrastructure needs do not outmatch your team’s ability to support and maintain that infrastructure.

Integrations with the rest of your stack

Every part of the Modern Data Stack relies on other tools performing their jobs; data pipelines, transformations, data models, BI tools - they are all connected. Using Airflow for your dbt deployment adds another link in the dependency chain. Coordinating dbt deployments via Airflow can always be done through writing additional code, but this is an additional overhead you will need to design, implement, and maintain. With this approach, you begin to require strong software engineering and design principles. Your data models are only as useful as your data is fresh; meeting your required SLAs will require significant cross-tool integration and customization.  

Who should use a fully custom setup for dbt deployment?

If you are a small team looking to deploy dbt, there are likely better options. If you are a growing team, there are certainly simpler options with less infrastructure overhead. For Data teams with complex data workflows that combine multiple tools and transformation technologies such as Python, Scala, and dbt, however, Airflow and other orchestrators can be a good choice.  

Ease of Use / Implementation – Can be quite challenging starting from scratch

Required Technical Ability – High

Configurability – High

Customization – High, but build time and maintenance costs can be prohibitive ‍

Best for End-to-End Deployment - High, but requires a lot of resources to set up and maintain

dbt Deployment Options Overview

Final thoughts

The way you should deploy your dbt project depends on a handful of factors – how much time you’re willing to invest up front, your level of technical expertise, as well as how much configuration and customization you need.  

Small teams might have high technical acumen but not enough capacity to manage a deployment on their own. Enterprise teams might have enough resources but maintain disparate, interdependent projects for analytics. Thankfully, there are several options to move your project beyond your local and into a production environment with ease. And while specific tools like Airflow have their own pros and cons, it’s becoming increasingly important to evaluate your data stack vendor solution holistically. Ultimately, there are many ways to deploy dbt to production, and the decision comes down to spending time building a robust deployment pipeline or spending more time focusing on analytics.  

Jinja is the game changing feature of dbt Core that allows us to create dynamic SQL code. In addition to the standard Jinja library, dbt Core includes additional functions and variables to make working with dbt even more powerful out of the box.

See our original post, The Ultimate dbt Jinja Cheat Sheet, to get started with Jinja fundamentals like syntax, variable assignment, looping and more. Then dive into the information below which covers Jinja additions added by dbt Core.

This cheatsheet references the extra functions, macros, filters, variables and context methods that are specific to dbt Core.

Enjoy!

dbt Core: Functions

These pre-defined dbt Jinja functions are essential to the dbt workflow by allowing you to reference models and sources, write documentation, print, write to the log and more.

select * from {{ ref('model_name') }}
select * from {{ ref('model_name', version=1) }}
select * from {{ ref('project_or_package', 'model_name') }}

select * from {{ source('source_name', 'table_name') }} 

{{ log('Logging something', info=True) }}

{{ print("Print something: " ~ value1 ~ ", " ~ value2) }}

{% docs table_sales_description %}

# docs
- go
- here
 
{% enddocs %}

#schema.yml
version: 2

models:
- name: sales   
  description: '{{ doc("table_sales_description") }}'

{% macro get_list() %}
  {{ return(['column_1', 'column_2', 'column_2']) }}
{% endmacro %}

select 
-- getdata() returns a list!
{% for item in get_list() %}
  {{ item }}
  {% if not loop.last %},{% endif %}
{% endfor %}
from {{ ref('some_model') }}

"{{ env_var('DB_PORT') | as_number }}"

#dbt_project.yml
models:
  my_project:
    +materialized: "{{ env_var('DBT_MATERIALIZATION', 'table') }}"

#dbt_project.yml
vars:
  start_date: '2023-10-01'

select * from orders 
where start_date >'{{ var("start_date") }}'

dbt Core: Macros

These macros are provided in dbt Core to improve the dbt workflow.

{% set results = run_query('select order from orders_table') %}

{% macro my_macro() %}
  {% set result = my_macro() %}
  
  {{ debug() }}
  
{% endmacro %}

dbt Core: Filters

These dbt Jinja filters are used to modify data types.

{{ <my_value> | as_bool }}

{{ <my_value> | as_number }}

{{ <my_value> | as_native }}

dbt Core: Project context variables

These dbt core "variables" such as config, target, source, and others contain values that provide contextual information about your dbt project and configuration settings. They are typically used for accessing and customizing the behavior of your dbt models based on project-specific and environment-specific information.

{% macro ref(modelname) %}
    {% set db_name = builtins.ref(modelname).database | lower %}}
    {% if db_name.startswith('staging') or 
        db_name.endswith('staging')  %}
        {{ return(builtins.ref(modelname).include(database=false)) }}
    {% else %}
        {{ return(builtins.ref(modelname)) }}
    {% endif %}
{% endmacro %}

{%- set unique_key = config.get('unique_key', default='id') -%}

{%- set unique_key = config.require('unique_key') -%}

{% set payment_method_query %}
  SELECT DISTINCT
    payment_method
  FROM {{ ref('raw_payments') }}
  ORDER BY 1
{% endset %}

{% set payment_methods = run_query(payment_method_query) %}

{% if execute %}
  {# Extract the payment methods from the query results #}
  {% set payment_methods_list = payment_methods.columns[0].values() %}
{% else %}
  {% set payment_methods_list = [] %}
{% endif %}

{% if flags.STORE_FAILURES %}
--your logic
{% else %}
--other logic
{% endif %}

modules.<desired_module>.<desired_function> 

{% set now = modules.datetime.datetime.now() %}

dbt Core: Run context variables

These special variables provide information about the current context in which your dbt code is running, such as the model, schema, or project name.

select
    '{{ run_started_at.strftime("%Y-%m-%d") }}' as run_start_utc

dbt Core: Context methods

These methods allow you to retrieve information about models, columns, or other elements of your project.

{% set my_list = ['a', 'b', 'b', 'c'] %}
{% set my_set = set(my_list) %}
{% do print(my_set) %}


{# evaluates to {'a','b', 'c'} #}

{% set my_set = set_strict(my_list) %}

exceptions.raise_compiler_error("Custom Error message")

exceptions.warn("Custom Warning message") 

{%- set hashed_value = local_md5(“String to hash”) -%}
select 
     '{{ hashed_value }}' as my_hashed_value

{% set my_list_a = [1, 2] %}
{% set my_list_b = ['Data', 'Coves'] %}
{% set my_zip = zip(my_list_a, my_list_b) | list %}
{% do print(my_zip) %}  

{# Result [(1, 'Data'), (2, 'Coves')] #}

{% set my_list_a = 123 %}
{% set my_list_b = 'Datacoves' %}
{% set my_zip = zip_strict(my_list_a, my_list_b) | list %}
{# This will fail #}

Please contact us with any errors or suggestions.

If you've taken an interest in dbt (data build tool) and are on the fence about whether to opt for dbt Cloud or dbt Core, you're in the right place. Perhaps you're already using one of the dbt platforms and are considering a change. Regardless of your current position, understanding the differences of these options is crucial for making an informed decision. In this article, we'll delve deep into the key distinctions between dbt Cloud and dbt Core.

dbt, dbt Core, dbt Cloud: What do they mean?

For those new to the dbt community, navigating the terminology can be a tad confusing. "dbt," "dbt Core," and "dbt Cloud" may sound similar but each represents a different facet of the dbt ecosystem. Let's break it down.

What is dbt?

dbt is the generic name for the open-source tool and when people say dbt the features are mainly those in dbt Core. dbt allows users to write, document, and execute SQL-based transformations, making it easier to produce reliable and up-to-date analytics. By facilitating practices like version control, testing, and documentation, dbt enhances the analytics engineering workflow, turning raw data into actionable insights.

Once you decide dbt is right for your organization, the next step is to determine how you'll access dbt. The two most prevalent methods are dbt Core and dbt Cloud. While dbt Cloud offers an enhanced experience with additional features, its abstraction can sometimes limit the desired flexibility and control over the workflow especially when it comes to using dbt with the complexities of an enterprise.

Throughout this article we'll observe that by using dbt Core and incorporating other tools, you can achieve many of the same functionalities as dbt Cloud while maintaining flexibility and control. While this approach offers enhanced flexibility, it consequently introduces increased complexity, maintenance, and an added workload. When adopting a dbt platform it is important to understand the tradeoffs to truly know what will work best for your data team.

What is dbt Core?

dbt Core is an open-source data transformation tool that enables data analysts and engineers to transform and model data to derive business insights. dbt Core is the foundational, open-source version of dbt that provides users with the utmost flexibility. The term "flexible" implies that users have complete autonomy over its implementation, integration, and configuration within their projects.

Even though dbt Core is free, to meet or exceed the functionality of dbt Cloud, it will need to be paired with additional tooling as we will discuss below.These open source solutions may be leverage at no cost, but this increases the platform maintenance overhead and may impact the total cost of ownership and the platform's time to market. Alternatively, managed dbt Core platforms exist, like Datacoves, which simplify this process.

Using and installing dbt Core is done manually. Depending on which data warehouse you are using, you select the appropriate dit adapter such as dit-snowflake, dbt-databricks, dt-redshift, etc. You can see all available dbt adapters on our dbt libraries. If you are using Snowflake you can check out our detailed Snowflake with dbt getting started guide.

Given that you have installed the pre-requisites, installing dbt is just a matter of installing dbt-snowflake.

What is dbt Cloud?

dbt Cloud is a hosted dbt platform to develop and deploy dbt projects. dbt Cloud leverages all the power of dbt Core with some extra features such as a proprietary Web-based UI, a dbt job scheduler, APIs, integration with Continuous Integrations platforms like Github Actions, and a proprietary Semantic layer. dbt Cloud's features are all intended to facilitate the dbt workflow.

dbt cloud pricing has three tiers: Enterprise, Team and Developer. Developer is a free tier meant for a single developer with a hard limit of 3000 model runs per month. The Team Plan pricing starts at $100 per developer for teams up to 8 with 15,000 successful models built per month; any additional models will cost $0.01.

dbt Core vs dbt Cloud: Development - IDE  

When it comes to the Integrated Development Environment (IDE), both dbt Cloud and dbt Core present distinct advantages and challenges. Whether you prioritize flexibility, ease of setup, or a blend of both, your choice will influence how your team develops, tests, and schedules your data transformations. Let's explore how each option handles the IDE aspect and the impact on developers and analytic engineers.

dbt Core

In the instance of IDEs, using dbt Core requires setting up a dev environment on each member's device or a virtual space like AWS workspace. This involves installing a popular dbt IDE such as VS Code, dbt Core, connecting to a data warehouse, and handling dependencies like Python versions.  

Enterprise dbt setups typically include additional dependencies to enhance productivity. Some notable VS Code extensions for this include dbt Power User, SQLFluff, and the official dbt Snowflake VS Code extension.

dbt Cloud

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

However, Developers who are accustomed to more versatile local IDEs, such as VS Code, may find the dbt Cloud experience limiting as they cannot leverage extensions such as those from the VS Code Marketplace nor can they extend dbt Core using the vast array of Python libraries.  

It is possible to get the best of both worlds - the flexibility of dbt Core in VS Code and the quick setup that dbt Cloud Offers  - with a Managed dbt Core Platform like Datacoves. In a best-in-class developer setup, new users are onboarded in minutes with simple configuration screens that remove the need to edit text files such as profiles.yml and remove the complexity of creating and managing SSH keys. Version upgrades of dbt or any dependent library should be transparent to users. Spinning up a pristine environment should be a matter of clicks.  

dbt Core vs dbt Cloud: Scheduling jobs  

Scheduling in a dbt project is crucial for ensuring timely and consistent data updates. It's the backbone of reliable and up-to-date analytics in a dbt-driven environment.  

dbt Core

While an orchestrator does not come out of the box with dbt Core, when setting up a deployment environment companies can leverage any orchestration tool, such as Airflow, Dagster, or Prefect. They can connect steps prior to or after the dbt transformations and they can trigger any tool that exists within or outside the corporate network.

dbt Cloud

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

dbt Cloud is primarily focused on running dbt projects. Therefore, if a data pipeline has more dependencies, an external orchestration tool may be required. Fortunately, if you do use an external orchestrator, dbt Cloud offers an API to trigger dbt Cloud jobs from your orchestrator.

dbt Core vs dbt Cloud: DataOps - Continuous Integration  

DataOps emphasizes automating the integration of code changes, ensuring that data transformations are consistently robust and reliable. Both platforms approach CI/CD differently. How seamless is the integration? How does each platform handle tool compatibility?

dbt Core  

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

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

dbt Cloud

dbt Cloud has built in CI capabilities which reduce the need for third party tools. dbt Cloud can also be paired with Continuous Integration (CI) tools like GitHub Actions to validate data transformations before they are added to a production environment. Aspects such as code reviews and approvals will occur in the CI/CD tool of choice such as GitHub and dbt Cloud can report job execution status back to GitHub Actions. This allows teams to know when it is safe to merge changes to their code. One item to note is that each successful model run in your CI run will count against the monthly model runs as outlined in the dbt Cloud pricing.

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

dbt Core vs dbt Cloud: Semantic layer  

A semantic layer helps businesses define important metrics like sales, customer churn, and customer activations with the flexibility to aggregate at run time. These metrics can be referenced by downstream tools as if they had been previously computed. End-users benefit from the flexibility to aggregate metrics at diverse grains without the company incurring the cost of pre-computing every permutation. These on-the-fly pivots ensure consistent and accurate insights across the organization.

dbt Core

dbt Core does not come with a built-in semantic layer, but there are open source and proprietary alternatives that allow you to achieve the same functionality. These include cube.dev, and Lightdash.

dbt Cloud

dbt Cloud has been rolling out a proprietary semantic layer which is currently in public preview. This feature is only available to dbt pricing plans Team and Enterprise. When using the dbt Cloud semantic layer your BI tool connects to a dbt Cloud proxy server which sits between the BI tool and your Data Warehouse.

dbt’s semantic layer offers a system where metrics are standardized as dbt metadata, visualized in your DAG, and integrated seamlessly with features like the Metadata API and the dbt proxy server.

dbt Core vs. dbt Cloud: Project Exploration and Lineage Tracking

Understanding your dbt project's structure and data flow is essential for effective data management and collaboration. While dbt Cloud offers dbt Explorer, a tool that visually maps model dependencies and metadata, it is exclusive to dbt Cloud users.

dbt Docs (dbt docs generate) is a built-in feature in dbt Core that generates a static documentation site, providing lineage graphs and detailed metadata for models, columns, and tests. However, for larger projects, dbt Docs can struggle with high memory usage and slow load times, making it less practical for extensive datasets. Also, dbt Docs lacks column-level lineage, which is crucial for impact analysis and debugging.

But no worries—dbt Core users can achieve similar, and even better, functionalities through alternative methods. The answer: a data catalog like DataHub. A Data Catalog can significantly enhance not just your dbt exploration, but your entire data project discovery experience! 

DataHub Offers:

• Searchable Metadata with Comprehensive Integration – DataHub can ingest metadata from various sources, including Apache Airflow and dbt, consolidating everything into a single repository. This creates a unified view of your data ecosystem, surpassing the capabilities of dbt Explorer.
• Column-Level Lineage – See how data moves and transforms at the column level, enabling more precise impact analysis and debugging.
• Collaborative Features – Facilitate teamwork with tools designed for sharing and documenting data knowledge across your organization such as a data glossary.

There is an obvious caveat. Implementing and maintaining an open-source data catalog like DataHub introduces additional complexity. Organizations need to allocate resources to manage, update, and scale the platform effectively. Fortunately, a managed solution like Datacoves simplifies this by providing an integrated offering that includes DataHub, streamlining deployment and reducing maintenance overhead.

dbt Core vs dbt Cloud: API

APIs play a crucial role in streamlining dbt operations and enhancing extensibility.  

dbt Core

With dbt Core, users often rely on external solutions to integrate specific API functionalities.

Administrative API Alternative: There is currently no feature-to-feature alternative for the dbt Cloud administrative API. However, the Airflow API can be leveraged to enqueue runs for jobs which is a primary feature of the dbt Cloud Administrative API.

Discovery API Alternative: This API was formerly known as the dbt Cloud Metadata API. Tools such as Datahub can provide similar functionality. Datahub can consume dbt Core artifacts such as the manifest.json and expose an API for dbt metadata consumption.

Semantic Layer API Alternative: When it comes to establishing and managing the semantic layer, Cube.dev provides a mature, robust, and comprehensive alternative to the dbt Cloud Semantic layer. Cube also has an API tailored for this purpose.

dbt Cloud

dbt Cloud offers three APIs. These APIs are available to Team and Enterprise customers.  

Administrative API: The dbt Cloud Administrative API is designed primarily for tasks like initiating runs from a job, monitoring the progress of these runs, and retrieving artifacts once the jobs have been executed. dbt Cloud is working on additional functionality for this API, such as operational functions within dbt Cloud.

Discovery API: Whenever you run a project in dbt Cloud, it saves details about that project, such as information about your data models, sources, and how they connect. The Discovery API lets you access and understand this saved information. Use cases include: performance, quality, discovery, governance and development.

Semantic Layer API: The dbt Semantic Layer API provides a way for users to interact with their data using a JDBC driver. By using this API, you can easily query metrics values from your data and get insights.

Conclusion

Examining the differences between dbt Core and dbt Cloud reveals that both can lead organizations to similar results. Much of what dbt Cloud offers can be replicated with dbt Core when combined with appropriate additional tools. While this might introduce some complexities, the increased control and flexibility might justify the trade-offs for certain organizations. Thus, when deciding between the two, it's a matter of prioritizing simplicity versus adaptability for the team. This article only covers dbt core vs dbt cloud but you can read more about dbt alternatives in our blog..

As a managed dbt Core solution, the Datacoves platform simplifies the dbt Core experience and retains its inherent flexibility. It effectively bridges the gap, capturing many benefits of dbt Cloud while mitigating the challenges tied to a pure dbt Core setup. See if Datacoves dbt pricing is right for your organization or visit our product page.

The dbt-utils package enhances the dbt experience by offering a suite of utility macros. Designed to tackle common SQL modeling patterns, it streamlines complex operations, allowing users to focus on data transformation rather than the intricacies of SQL. dbt-utils is a must-have tool for dbt aficionados!

dbt-utils Cheat sheet

The dbt-utils package is a gem in the world of data transformations. Let this cheat sheet guide you swiftly through its features, ensuring you get the most out of dbt-utils. Enjoy!

SQL Generators

The SQL generators in the dbt-utils package streamline your modeling tasks. By automating common SQL patterns, they minimize manual coding and guarantee consistent, high-quality queries. Think of it as a handy toolkit for every dbt user's SQL endeavors!

Generic tests

^Within the dbt-utils package lies a set of generic tests, designed to validate your data effortlessly. These tests ensure consistency and quality, checking for common issues without the need to craft custom validation rules. It's data integrity made simple for dbt users.

Introspective macros

The introspective macros within the dbt-utils package are a window into your data's metadata. They empower you to dynamically interact with and understand the underlying structure of your datasets. It's like having a magnifying glass for the intricacies of your dbt projects!

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Please contact us with any errors or suggestions.

dbt and Airflow are not competing tools. They solve different problems in a modern data stack.

dbt focuses on transforming data inside the warehouse using analytics engineering best practices. Airflow focuses on orchestrating workflows across systems, schedules, and dependencies. Most mature data teams use both.

The real challenge is understanding where each tool fits, where responsibilities overlap, and how poor orchestration decisions lead to fragile pipelines and constant firefighting.

What is Apache Airflow?

Airflow is a workflow scheduler, but it is only one part of a broader data orchestration problem that includes dependencies, retries, visibility, and ownership across the entire data lifecycle.

How does Airflow work?

Imagine a scenario where you have a series of tasks: Task A, Task B, and Task C. These tasks need to be executed in sequence every day at a specific time. Airflow enables you to programmatically define the sequence of steps as well as what each step does. With Airflow you can also monitor the execution of each step and get alerts when something fails.

What sets Airflow apart?

Airflow provides flexibility, which means you can script the logic of each task directly within the tool. However, this flexibility might be both a blessing and a curse. Just because you can code everything within Airflow, it doesn't mean that you should. Overly complicated workflows and incorporating too much logic within Airflow can make it difficult to manage and debug. Ensure that when you're using Airflow, it's the right tool for the specific task you're tackling. For example, it is far more efficient to transform data within a data warehouse than to move data to the Airflow server, perform the transformation, and write the data back to the warehouse. 

At the heart of Apache Airflow's appeal is its flexibility when it comes to customizing each step in a workflow. Unlike other tools that may only let you schedule and order tasks, Airflow offers users the ability to define the code behind each task. This means you aren't just deciding the "what" and the "when" of your tasks, but also the"how". Whether it's extracting and loading data from sources, defining transformations, or integrating with other platforms, Airflow lets you tailor each step to your exact requirements. This granularity makes it a powerful ally for those looking to have granular control over their data workflows, ensuring that each step is executed precisely as intended.

While Airflow is powerful, it's important to strike a balance. You should use Airflow primarily as an orchestrator. If mature tools exist for specific tasks, consider integrating them into your workflow and allow Airflow to handle scheduling and coordination. Let specialized tools abstract away complexity. One example is leveraging a tool like Fivetran or Airbyte to perform data extraction from SaaS applications rather than building all the logic within Airflow.

Preferred Airflow use cases

As stated above, Airflow can be used for many things, but we suggest these use cases.

• Data Extraction and Loading: Airflow can be used to trigger an external tool for data extraction and loading. In cases where tools don't exist, Airflow can be used to call Python scripts or frameworks like dlt to perform data loading.
• Data Transformation Coordination: Organize, execute, and monitor data pipelines across the entire ELT process. Once data is loaded, trigger a tool like dbt Core to handle the transformation steps in the right sequence and with the appropriate parallelism directly in the data warehouse.
• ML Workflow Coordination: Trigger and orchestrate machine learning pipelines, ensuring each component from data preprocessing to model refreshes run in sequence after data transformation.
• Automated Reporting: Initiate generation of various data reports or data extraction to reporting tools, ensuring stakeholders always have access to the latest insights.
• System Maintenance and Monitoring: Schedule regular backups, send alerts in case of system anomalies, and manage the storage of logs, to ensure your data-driven applications run smoothly.

Airflow benefits

• Job Management: Set up of workflows and dependencies between tasks in a simple way with a built-in scheduler that handles synchronization of tasks
• Retry mechanism: It is common to retry some parts, or the whole data pipeline when a task fails. Airflow provides a robust retry mechanism in case of failures that ensure resilience and fault tolerance.
• Alerting: When something goes wrong, you should know. Airflow helps you by sending alerts to other tools such as email, Slack, or MS Teams
• Monitoring: The Airflow UI enables you to monitor your workflows
• Scalability: Airflow can be deployed on scalable infrastructure like Kubernetes. This allows the platform to scale up when more resources are needed and scale down when they are not needed. Combined this helps companies process many tasks quickly without having to pay for a lot of infrastructure that is typically idle
• Community: Airflow has an active open-source project and there is robust support both on GitHub and Slack. There are also many resources to learn and maintain Airflow.

Airflow challenges

• Programming Language: Airflow is a Python tool and as such the out of the box experience requires knowing Python to create the Airflow jobs
• Learning curve: creating workflows as code in Python can be complex and understanding Airflow's concepts like DAGs, operators, and tasks can require time and effort to master
• Production Deployment: Airflow offers scalability, but setting up a robust infrastructure requires an understanding of technologies like Kubernetes and the host of challenges that come with tuning them
• Maintenance: Upgrading Airflow and Kubernetes comes with a cost and, like the initial setup, can be challenging for those who don’t work on complex platforms on a regular basis
• Debugging: identifying root issues in a complex Airflow environment can be challenging. Just knowing where to start requires experience
• Cost of Ownership: While Airflow is open-source and therefore “free” to use, the complexity of initial setup and ongoing support can be very costly and time consuming.
• Development experience: To test jobs, developers will need to create a local Airflow environment using tools like Docker which adds complexity and delays to the process

What is dbt?

dbt Core is an open-source framework that leverages templated SQL to perform data transformations. Developed by dbt Labs, it specializes in transforming, testing, and documenting data. While it's firmly grounded in SQL, it infuses software engineering principles into the realm of analytics, promoting best practices like version control and DataOps.

How does dbt work?

Imagine you have a raw data set and you need to transform it for analytical purposes. dbt allows you to create transformation scripts using SQL which is enhanced with Jinja templating for dynamic execution. Once created, these scripts, called "models" in dbt, can be run to create or replace tables and views in your data warehouse. Each transformation can be executed sequentially and when possible, in parallel, ensuring your data is processed properly.

What sets It apart?

Unlike some traditional ETL tools which might abstract SQL into drag-and-drop interfaces, dbt embraces SQL as the lingua franca of data transformation. This makes it exceptionally powerful for those well-acquainted with SQL. But dbt goes a step further: by infusing Jinja, it introduces dynamic scripting, conditional logic, and reusable macros. Moreover, dbt's commitment to idempotency ensures that your data transformations are consistent and repeatable, promoting reliability.

Lastly, dbt emphasizes the importance of testing and documentation for data transformations. dbt facilitates the capture of data descriptions, data lineage, data quality tests, and other metadata about the data and it can generate a rich web-based documentation site. dbt's metadata can also be pushed to other tools such a specialized data catalog or data observability tools. While dbt is a transformative tool, it's essential to understand its position in the data stack. It excels at the "T" in ELT (Extract, Load, Transform) but requires complementary tools for extraction and loading.

Preferred dbt Core use cases

• Data Transformation for Analytics: Utilize dbt to transform and aggregate data in a manner that's ready for analytical tools, BI platforms, and ML models.
• SQL-based Workflow Creation: Design and execute SQL workflows with modular components, using the Jinja templating engine for dynamic script generation.
• Data Validation and Testing: Employ dbt's pre-defined data tests for ensuring data quality and reliability in transformation tasks.
• Documentation and Lineage: Use dbt's built-in capabilities for auto-generating documentation and establishing clear data lineage, simplifying impact analysis and debugging.
• Version Control and DataOps: Promote best practices in data operations by utilizing dbt's version control and environment management features, ensuring transformations are consistent and properly tested before deploying to production.

dbt benefits

• Common knowledge: Since dbt uses SQL and Jinja for data transformation. Many data analysts and data engineers can leverage their existing SQL knowledge
• Modularity: In dbt each transformation is composed of small steps that are SQL select statements which transform raw data into an analysis-friendly structure. This modularity simplifies understanding, debugging and enable reusability
• Tests: The ability to add tests to transformation scripts ensure that data is accurate and reliable without needing to introduce additional tools into the data platform
• Documentation: dbt has an auto-generated website that exposes descriptions, lineage, and other information about a transformation workflow. This makes it easier to maintain the project over time. It also allows consumers of the data to find and understand data before use
• Debugging: dbt’s data lineage simplifies debugging when issues occur
• Impact Analysis: By leveraging dbt’s lineage information, we can pinpoint the effects when there's a delay in a data source. This also provides insight into which sources contribute to a specific data product, like an executive dashboard.
• Open-source: dbt Core is open-source so there is no vendor lock-in
• Packages and libraries: Thanks to a large community there are many dbt packages, which are pre-defined pieces of reusable functionality, that anyone can reuse in their dbt project. There are also many dbt Python libraries available which extend the functionality of dbt
• Metadata consumption: dbt produces metadata files that can be consumed by downstream tools like data catalogs and observability tools. This open format is quickly becoming the de-facto standard for sharing information about data across data platforms
• Community: dbt has a strong and growing community that has over 50k members on Slack alone. There are also a lot of resources making it simpler to find help and there are many examples from others solving similar data problems

dbt challenges

• Data transformation only: As mentioned above, it is the “T” in the ELT pipeline. This means that other parts of the data value chain such as extracting from other applications, loading a data warehouse, or sending the data to downstream processes like marketing automation are not part of dbt and need to be solved with the help of other tools
• Macros: dbt macros are reusable dynamic code that are a powerful feature, but may be hard to read, debug, and test for analysts who are only accustomed to SQL

Common misconception: dbt means dbt Cloud

A common misunderstanding within the data community is that dbt = dbt Cloud. When people say dbt they are referring to dbt Core. dbt Cloud is a commercial offering by dbt Labs and it is built upon dbt Core. It provides additional functionalities to the open source framework; these include a scheduler for automating dbt runs, alongside hosting, monitoring, and an integrated development environment (IDE). This means that you can use the open source dbt Core framework without paying for dbt Cloud, however, you will not get the added features dbt Cloud offers such as the scheduler. If you are using dbt Core you will eventually need an orchestrator such as Airflow to get the job done. For more information, check out our article where we cover the differences between dbt cloud vs dbt core.

The scheduler: Automating dbt runs

As mentioned above, one of the key features of dbt Cloud is its scheduler which allows teams to automate their dbt runs at specified intervals. This functionality ensures that data transformations are executed regularly, maintaining the freshness and reliability of data models. However, it's important to note that dbt Cloud's scheduler only handles the scheduling of dbt jobs, i.e., your transformation jobs. You will still need an orchestrator to manage your Extract and Load (EL) processes and anything after Transform (T), such as visualization.

Managed dbt Core paired with managed Airflow  

At Datacoves we solve the deployment and infrastructure problems for you so you can focus on data, not infrastructure. A managed Visual Studio Code editor gives developers the best dbt experience with bundled libraries and extensions that improve efficiency. Orchestration of the whole data pipeline is done with Datacoves’ managed Airflow that also offers a simplified YAML based Airflow job configuration to integrate Extract and Load with Transform. Datacoves has best practices and accelerators built in so companies can get a robust data platform up and running in minutes instead of months. To learn more, check out our product page.

Managing the deployment and infrastructure of dbt Core and Airflow is a not so hidden cost of choosing open source, however, at Datacoves we solve the deployment and infrastructure problems for you so you can focus on data, not infrastructure. A managed Visual Studio Code editor gives developers the best dbt experience with bundled libraries and extensions that improve efficiency. Orchestration of the whole data pipeline is done with Datacoves’ managed Airflow that also offers a simplified YAML based Airflow job configuration to integrate Extract and Load with Transform. Datacoves has best practices and accelerators built in so companies can get a robust data platform up and running in minutes instead of months. To learn more, check out our product page.

Airflow vs dbt: Which one should you choose?

When looking at the strengths of each tool, it’s clear that the decision isn’t an either-or solution, but they each have a place in your data platform. Analyzing the strengths of each reveals that Airflow should be leveraged for the end-to-end orchestration of the data journey and dbt should be focused on data transformation, documentation, and data quality. This holds true if you are adopting dbt through dbt Cloud. dbt Core does not come with a scheduler, so you will eventually need an orchestrator such as Airflow to automate your transformations as well as other steps in your data pipeline. If you implement dbt with dbt Cloud, you will be able to schedule your transformations but will still need an orchestrator to handle the other steps in your pipeline. You can also check out other dbt alternatives.

The following table shows a high-level summary.

dbt vs Airflow: Which tool to adopt first for your data platform?

By now you can see that each tool has its place in an end-to-end data solution, but if you came to this article because you need to choose one to integrate, then here is the summary.

If you're orchestrating complex workflows, especially if they involve various tasks and processes, Apache Airflow should be your starting point as it gives you unparalleled flexibility and granular control over scheduling and monitoring.

An organization starting out with basic requirements may be fine starting with dbt Core, but when end-to-end orchestration is needed, Airflow will need to play a role.

If your primary focus is data transformation and you're looking to apply software development best practices to your analytics, dbt is the right answer. Here is the key takeaway: these tools are not rivals, but allies. While one might be the starting point based on immediate needs, having both in your arsenal unlocks the full potential of your data operations.

dbt vs Airflow: Better together

While Airflow and dbt are designed to assist data teams in deriving valuable insights, they each excel at unique stages of the workflow. For a holistic data pipeline approach, it's best to integrate both. Use tools such as Airbyte or Fivetran for data extraction and loading and trigger them through Airflow. Once your data is prepped, let Airflow guide dbt in its transformation and validation, readying it for downstream consumption. Post-transformation, Airflow can efficiently distribute data to a range of tools, executing tasks like data feeds to BI platforms, refreshing ML models, or initiating marketing automation processes.

However, a challenge arises when integrating dbt with Airflow: the intricacies of deploying and maintaining the combined infrastructure isn't trivial and can be resource-intensive if not approached correctly. But is there a way to harness the strengths of both Airflow and dbt without getting bogged down in the setup and ongoing maintenance? Yes!

Conclusion

Both Apache Airflow and dbt have firmly established themselves as indispensable tools in the data engineering landscape, each bringing their unique strengths and capabilities to the table. While Apache Airflow has emerged as the premier orchestrator, ensuring that tasks and workflows are scheduled and executed with precision, dbt stands out for its ability to streamline and enhance the data transformation process. The choice is not about picking one over the other, but about understanding how they can be integrated to provide a comprehensive solution.

It's vital to approach the integration and maintenance of these platforms pragmatically. Solutions like Datacoves offer a seamless experience, reducing the complexity of infrastructure management and allowing teams to focus on what truly matters: extracting value from their data. In the end, it's about harnessing the right tools, in the right way, to chart the path from raw data to actionable intelligence. See if Datacoves dbt pricing is right for your organization.