dbt and Azure Data Factory are data transformation tools, but dbt excels in SQL-based transformations for data teams, while Azure Data Factory offers a broader, visual, no-code approach for Azure-centric users.
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dbt
Azure Data Factory
About
dbt is a data transformation tool that helps data teams work together more efficiently. It allows anyone who knows SQL to build data pipelines by providing a framework for writing, testing, and deploying code. This helps companies deliver reliable data products faster and cheaper by standardizing how data is processed and documented within their data warehouse. dbt integrates with popular data platforms like Snowflake, Databricks, and BigQuery.
Azure Data Factory is a cloud-based platform designed to help businesses connect and integrate data from various sources. It allows you to build data pipelines that automate the process of extracting, transforming, and loading (ETL) data, regardless of where it resides - be it on-premises servers or cloud storage. This empowers your team to analyze data efficiently and make informed decisions without delving into complex coding.
Summary
Main difference
dbt is a transformation tool that uses SQL and is ideal for data teams familiar with SQL and looking for a cost-effective solution with a free tier for developers. Azure Data Factory is a cloud-based ETL platform with a visual interface and broad integration with Azure services, making it better for Azure-centric businesses needing a scalable, no-code solution.
Relative strengths of dbt (compared to Azure Data Factory)
Strong SQL support and transformation capabilities.
Cost-effective, especially for smaller teams with a free developer tier.
Integrates with popular data warehouses like Snowflake, Databricks, and BigQuery.
Relative weaknesses of dbt (compared to Azure Data Factory)
Limited visual interface and no-code capabilities compared to Azure Data Factory.
Less extensive pre-built connectors compared to Azure Data Factory’s wider range.
Debugging complex transformations can be challenging.
dbt is a SQL-based data transformation tool that empowers data teams to collaborate effectively. It simplifies the process of building, testing, and deploying data pipelines, enabling faster delivery of reliable data products. Users praise dbt's robust documentation generation and incremental models for handling large datasets. Some users desire more extensive documentation and column-level lineage. dbt integrates with popular data platforms like Snowflake, Databricks, and BigQuery.
Azure Data Factory is a powerful data integration platform ideal for large enterprises, especially those in the software and IT sectors. Users praise its robust capabilities and Azure integration, while some find complex transformations challenging. It simplifies data pipeline creation, automating ETL processes and handling large datasets effectively.
Best fit for medium to large companies with established data teams.
Suitable for data-driven businesses across various industries.
Ideal for large enterprises seeking robust data integration.
Best suited for the software, IT, and telecommunications industry.
dbt and Azure Data Factory features
Supported
Partially supported
Not supported
Type in the name of the feature or in your own words tell us what you need
Data pipeline planning
Not supported
dbt itself does not plan data pipelines, but integrates with other tools that do.
Not supported
Azure Data Factory does not directly support dbt Mesh for data pipeline planning.
Multiple development interfaces
Supported
dbt supports multiple development interfaces, including a cloud IDE, CLI, and a visual editor.
Supported
Azure Data Factory supports development through its web UI and integrates with Azure DevOps and GitHub for collaboration.
SQL data transformation
Supported
dbt fully supports performing data transformations using SQL queries.
Supported
Azure Data Factory supports data transformation using SQL queries through features like Data Flows, Stored Procedures, and Copy Activity.
BI Tool Integration
Supported
dbt offers robust integration with BI tools, including features like data transformation, metadata synchronization, and version control, making it a valuable tool for advanced data solutions and data management.
Supported
Azure Data Factory supports integration with various BI tools through its data integration and transformation capabilities, as evidenced by its ability to connect to platforms like Azure Synapse Analytics and its use of linked services for data movement.
Data Governance Tool Integration
Supported
dbt integrates with various data governance tools, including Secoda and Atlan, to provide features like data lineage, metadata management, model governance, and access control. This makes it a suitable tool for organizations looking to enhance their data governance practices.
Supported
Azure Data Factory integrates with Azure Purview for data governance, enabling data discovery, lineage tracking, and compliance. It also leverages Azure Key Vault and Azure Active Directory for enhanced security and access control.
ETL Process Support
Supported
dbt is well-suited to support smooth ETL processes by optimizing data transformation, integrating with version control, automating workflows, and ensuring data quality and governance.
Supported
Azure Data Factory (ADF) is built to orchestrate and automate data integration workflows, including ETL processes. Its capabilities such as data transformation activities, scalability, visual interface for pipeline orchestration, and integration with other Azure services enable efficient and scalable data integration, ensuring smooth ETL processes.
Browse all features
Qualities
Value and Pricing Transparency
+1
Strongly positive sentiment
+0
Neutral sentiment
Customer Service
-0.2
Neutral sentiment
+0.6
Rather positive sentiment
Ease of Use
+0.93
Strongly positive sentiment
+1
Strongly positive sentiment
Reliability and Performance
+0.87
Strongly positive sentiment
+0.56
Rather positive sentiment
Ease of Implementation
+0.47
Rather positive sentiment
+0.75
Strongly positive sentiment
Scalability
+0.6
Rather positive sentiment
+1
Strongly positive sentiment
dbt and Azure Data Factory Pricing
dbt Cloud is a web-based IDE for data transformation. It offers different pricing plans for individual developers, teams, and enterprises.
Plans
FreeDeveloper
The fastest way to get started with dbt Cloud. This plan includes a 14-day free trial of the Teams plan, browser-based IDE, job scheduling, one project, logging & alerting, data documentation, source freshness reporting, continuous integration, native support for GitHub and GitLab, US hosting, multi-factor authorization for logins.
$100/mo/seatTeam
Pay-as-you-go pricing for emerging dbt Cloud teams. This plan includes all features in the Developer plan plus one project, five read-only seats, unlimited concurrent running jobs, API access, outbound webhooks, and dbt Semantic Layer, powered by MetricFlow.
CustomEnterprise
Scale dbt Cloud to the changing needs of your business. This plan includes all the features in the Team plan plus unlimited projects, single sign-on (SSO), multiple deployment regions, service level agreements (SLAs), professional services, role-based ACLs, fine-grained Git permissions, audit logging, native support for GitHub, GitLab, and Azure DevOps, and advanced CI.
We couldn't find a pricing page for Azure Data Factory.
dbt and Azure Data Factory review insights
234 reviews analysed from
Users love
The documentation it generates when all the models are designed. It clearly defines which intermediate and final layers are connected to each other.
The incremental model runs greatly helped me in optimizing large data models as I was dealing with billions of rows of data.
dbt is the best Transformation tool out there in the industry and I love dbt for its testing capabilities and modeling and semantic layer.
Ease of use and how easily you could maintain
Robust and versatile data integration capabilities.
User-friendly interface with drag-and-drop functionality.
Seamless integration with other Azure services.
Handles large data volumes efficiently.
Offers both automated and manual methods for pipeline execution and debugging.
Users dislike
It would be better if we have column level lineage available in dbt.
The documentation is not very extensive.
The scheduler will make it complete transformation tool for data engineers.
It should provide a tool to better enable model documentation.
Difficult to write complex transformation logic in ADF.
Pipeline execution can be slow due to internal failures that are hard to debug.
Limited connector support for third-party data providers.
Logging functionality could be improved.
Documentation could be more comprehensive with more real-world examples.
dbt and Azure Data Factory Ratings
G2
4.8/5
(156)
Glassdoor
3.7/5
(44)
G2
4.6/5
(78)
Company health
Employee growth
32% increase in the last year
3% increase in the last year
Web traffic
13% decrease in the last quarter
11% decrease in the last quarter
Financing
November 2021 - $414M
No data
How important is SQL-centric development for your data team?
SQL-centric development is crucial for our data team if they primarily use SQL for data transformation and prefer a framework that leverages their existing SQL skills. If the team prefers a visual, low-code/no-code approach or works with diverse data formats requiring extensive coding beyond SQL, a less SQL-focused tool might be more suitable. The ultimate importance depends on the team's current workflow and comfort level with SQL-based development.
Which platform better suits your existing cloud infrastructure and services?
Azure Data Factory better suits an existing Azure cloud infrastructure. Its seamless integration with other Azure services simplifies data pipeline management and leverages existing resources. While dbt is a powerful transformation tool, it lacks the same level of native integration with Azure.
What are the advantages of dbt?
dbt excels in SQL-based data transformation, offering a robust framework for developing, testing, and deploying data models. This streamlines the data pipeline creation process, making it more efficient and collaborative for data teams. Furthermore, dbt's strong integration with popular BI tools enhances data management and facilitates advanced data solutions. It also integrates with various data governance tools to improve data lineage, metadata management, and access control.
What are the disadvantages of dbt?
dbt's limitations include less extensive documentation compared to some alternatives, potentially hindering user onboarding and troubleshooting. Some users have also pointed out the lack of column-level lineage, which can make detailed data lineage tracking more challenging. While dbt excels at transformations, it lacks a built-in scheduler, meaning users often rely on external tools for orchestrating data pipelines. Finally, although dbt's pricing is transparent, the limited pricing tiers may not be flexible enough for some organizations.
Alternatives to dbt and Azure Data Factory
Prophecy
Prophecy is a low-code platform designed to make building data pipelines faster and easier for data teams of all skill levels. It offers an AI-powered visual designer that converts workflows into high-quality code (Spark or SQL). This allows both data engineers and business users to build, deploy, and monitor data pipelines for analytics and machine learning, reducing the dependency on highly technical coding skills. Prophecy integrates with popular cloud data platforms like Databricks and Snowflake and focuses on the entire data pipeline lifecycle: development, deployment, and observability.
Databricks Data Intelligence Platform is a cloud-based platform designed to help mid-size to large businesses manage and analyze data. It offers a range of tools for data warehousing, data engineering, data science, and machine learning, all in one place. Databricks aims to simplify data processes, reduce costs, and enable companies to build and deploy AI solutions. They are well-known for their work with open-source technologies, making them a popular choice for businesses invested in those ecosystems.
Dataddo
Dataddo is a data integration platform that helps you connect, move, and transform data between various sources like business apps, databases, and data warehouses. It offers a no-code interface for simple connections and advanced features for complex data workflows. Dataddo manages the technical aspects like API changes and pipeline maintenance. It suits various needs such as sending data to dashboards, comprehensive data integration (ETL, ELT, Reverse ETL), and even building custom data products. It prioritizes security and compliance with industry standards.
Keboola
Keboola is a data management platform designed to simplify how businesses collect, manage, and use their data. It offers a range of tools for connecting to various data sources, automating data workflows, and building data pipelines without extensive coding. This allows users of all technical levels to create reports, analyze information, and share insights across their organization. Keboola focuses on providing a secure, scalable, and user-friendly platform that empowers businesses to make better decisions through data-driven insights.
Estuary Flow
Estuary Flow is a cloud-based data integration platform designed to help businesses build real-time data pipelines. It offers pre-built connectors to various data sources, allowing you to extract, transform, and load data to your desired destinations. Estuary Flow supports both real-time and batch data processing and provides a user-friendly interface for setting up and managing data pipelines. The platform prioritizes data security and compliance with certifications like SOC 2 Type II. Estuary Flow aims to simplify data integration processes, offering fast and reliable data movement for analytics, operational efficiency, and AI applications.
Hevo Data
Hevo Data is a cloud-based data integration platform designed to simplify how businesses gather and use data. It allows you to pull data from over 150 different sources, like your company's sales software, marketing tools, or databases. Hevo automatically combines this data in a central location for easy analysis, freeing your team from manually managing data and ensuring it's always accurate and up-to-date. Hevo prides itself on its ease of use, reliability, and security, making it suitable for businesses of all sizes.
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