The blog explains how integrating dbt (Data Build Tool) with Datagaps DataOps Suite combines dbt’s SQL-based, version-controlled data transformation capabilities with Datagaps’ data quality, validation, profiling, observability, and governance capabilities. The integration uses REST APIs and plugins to trigger dbt jobs, load the resulting datasets, and automatically compare source and target data. This helps teams detect mismatches, isolated records, and duplicates while improving data quality, monitoring, collaboration, documentation, and overall data governance.
Key Takeaways:
- Combine transformation and validation: dbt handles structured, version-controlled data transformations, while Datagaps validates and monitors the resulting data.
- Automate data validation: REST APIs and plugins can trigger dbt jobs and automatically compare the resulting source and target datasets for discrepancies.
- Improve data quality and observability: Datagaps enables profiling, quality checks, real-time monitoring, anomaly detection, and faster identification of transformation issues.
- Support governance and collaboration: The integration provides shared visibility into transformation logic, data quality, lineage, and automated documentation, helping teams build a more cohesive data strategy.
What is dbt Data Build Tool?
Introduction – In today’s data-driven world, organizations are continually seeking ways to optimize data operations, enhance data quality, and ensure robust governance practices. The integration of powerful tools like DBT (Data Build Tool) and Datagaps DataOps Suite has emerged as a game-changing solution. Individually, these tools offer impressive capabilities, and when integrated, they create a symbiotic relationship that addresses challenges related to data transformation, quality, profiling, and observability. In this article, we delve into the integration of DBT and Datagaps DataOps Suite, exploring how this combination fosters more efficient data operations and empowers data-driven decision-making.
Data Build Tool(dbt): A Brief Overview
DBT, or Data Build Tool, is a popular open-source command-line tool designed primarily for transforming data analytics. It allows data analysts and engineers to transform data within their warehouse in a structured and version-controlled manner. With its focus on SQL-based transformations, DBT promotes collaboration, transparency, and maintainability in data pipelines.
Datagaps DataOps Suite: Enhancing Data Quality and Governance
DataOps suite that encompasses a range of functionalities, including data quality assurance, profiling, observability, and governance. The suite’s core objective is to improve data operations across the board by ensuring that data is of high quality, well understood, and observable throughout its lifecycle. With plug-and-play integration systems, a plethora of supported data sources, cloud and on-prem system support, and much more the Suite is most comprehensive Data Quality, Profiling, and Governance system in the data sphere.
Integration Mechanics: Leveraging REST APIs and Plugin Support
The integration of DBT and Datagaps DataOps Suite is made possible through a combination of REST APIs and plugin support within the Datagaps DataOps Suite. This allows seamless communication between the two platforms, enabling data engineers and analysts to utilize the strengths of both tools without friction. Data transformations orchestrated through DBT can be automatically monitored, profiled, and governed within the Datagaps DataOps Suite, forming a cohesive data management ecosystem.
Plugins for Integration
The suite provides end-users with complete customizability options and developer-friendly tools to create any set of customizations, integrations, processing nodes, test case validation, and other toolsets.
As seen in the images, the application will help developers define parameters, variables, and datasets which are subsequently used in the code component to create toolsets capable of a variety of tasks.
The end users only have to plug in the required variables or parameters and the application subsequently takes care of the translations. The example showcases the various types of plugins that themselves can be exported or imported with the different use-cases seen in the enterprise data space. These plugins can be build in Scala as well.
Trigger a DBT Job and Compare the Results
In this showcase, we use the plugin showcased earlier to first trigger a DBT job after which we compare two datasets for incorrectness.
The dataflow displays the plugin node, loading of the 2 datasets, and data comparison node.
Options of Chosen Plugin
Output Plugin
Output of Data Compare
As seen in the screenshots, the application first triggers a DBT job based on the user’s inputs, then loads up the Source and Target Datasets which were created post the job’s completion, and finally runs a data comparison check.
The failure of this test case triggers a notification to the users showcasing the various mismatched, isolated and duplicate records seen in the datasets as a compiled report.
Benefits and Use-Cases
Enhanced Data Quality and Profiling: The integration ensures that data transformed using DBT undergoes rigorous data quality checks and profiling within the Datagaps DataOps Suite. This leads to cleaner, more reliable data, reducing errors and enhancing the trustworthiness of analyses.
Observability and Monitoring: Datagaps DataOps Suite’s observability features allow teams to monitor data transformations executed through DBT in real-time. This enables swift identification of issues, performance bottlenecks, and anomalies, leading to quicker resolution times.
Efficient Collaboration: Data engineers and analysts can collaborate more efficiently using the integrated solution. DBT’s transformation logic and Datagaps DataOps Suite’s observability tools provide a shared context for better communication and decision-making.
Unified Data Governance: While Datagaps DataOps Suite doesn’t focus primarily on governance, its capabilities contribute to effective data governance practices. Organizations can ensure that data transformations adhere to compliance requirements and maintain a clear understanding of data lineage.
Automated Documentation: The integration automates the process of documenting data transformations, lineage, and quality checks. This documentation is vital for maintaining a historical record of changes and ensuring transparency.
Holistic Data Strategy: Integrating DBT and Datagaps DataOps Suite supports organizations in developing a holistic data strategy. It bridges the gap between transformation and operational aspects, empowering organizations to make data-driven decisions confidently.
Conclusion
The integration of Data Build Tool (DBT) and Datagaps DataOps Suite is a remarkable example of how combining specialized tools can create a more robust and comprehensive data management solution. By harnessing the strengths of both tools, organizations can streamline data transformation, enhance data quality, improve observability, and ensure better data governance practices.
As the data landscape continues to evolve, this integration paves the way for more effective and efficient data operations, enabling organizations to unlock the full potential of their data-driven initiatives.
FAQs: dbt and Datagaps DataOps Suite Integration
1) What is the benefit of integrating dbt with Datagaps DataOps Suite?
2) How does Datagaps validate data transformed by dbt?
3) Can dbt and Datagaps integration be automated?
4) How does the integration improve data quality?
5) Does the integration support data observability and governance?
Rajesh Kumar A
Digital Marketing Manager, Datagaps
Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.
Subrahmanya Narayana Chirravuri
Senior Director, Technology, Datagaps
Senior Director of Technology at Datagaps. Leads engineering for the ETL, BI, and data-quality validation platforms.
