ETL using Databricks with Sigma

Problem Statement:

The client organization currently relies on Python scripts and CSV files for data processing and analytics. To enhance real-time data updates, improve efficiency, and leverage cloud-based analytics, the organization aims to migrate existing Python-based workflows to Databricks using Spark SQL while integrating Sigma’s input table functionality. This transition will enable external users to provide real-time inputs, transforming the data dynamically and creating insightful visualizations in Sigma.

The core objective is to migrate existing Python workflows to Spark SQL in Databricks while replacing CSV file-based inputs with Sigma input tables. The migration should ensure data integrity, performance optimization, and real-time data updates in Databricks views, ultimately enabling better decision-making through Sigma dashboards.

Solution Overview:

Requirement Analysis and Stakeholder Engagement:

• Conduct detailed requirement analysis sessions with stakeholders to understand the current Python-based workflows.
• Document existing data dependencies, transformations, and performance benchmarks.
• Define success criteria and key performance indicators (KPIs) for the migration.

Integration of SSMS Database with Databricks:

• Establish a connection between the SQL Server Management Studio (SSMS) database and Databricks as a foreign catalog.
• Ensure smooth integration to allow direct querying and transformation of SSMS tables in Databricks.

Integration of Sigma with Databricks:

• Connect Sigma with Databricks to facilitate real-time data synchronization.
• Implement Sigma input tables, allowing external users to enter and modify data dynamically.
• Enable these input tables to update warehouse views in Databricks in real-time.

Data Transformation and View Creation Using Spark SQL:

• Utilize foreign catalog tables in Databricks to perform necessary transformations.
• Create transformed views in Databricks that aggregate and enrich the input data.
• Ensure that views update dynamically as users provide inputs in Sigma tables.
• The view updates dynamically as data changes in Sigma input tables.
• The foreign catalog tables from SSMS serve as the primary source for transformations.

Visualization in Sigma:

• Integrate Databricks-transformed views with Sigma.
• Create dashboards in Sigma using both Sigma warehouse views and Databricks-transformed views.
• Ensure real-time reflection of data changes in Sigma visualizations.

Code Migration and Optimization:

• Translate existing Python scripts to Spark SQL for efficient transformations in Databricks.
• Optimize transformations to fully utilize Databricks’ distributed processing capabilities.
• Replace CSV-based input handling with Sigma input tables to streamline data updates.

Integration and Testing:

• Integrate the newly developed Spark SQL-based transformations into the Databricks environment.
• Implement automated testing scripts to validate functionality and performance.
• Conduct performance tuning to ensure faster processing and real-time updates.

Deployment and Monitoring:

• Deploy the transformed workflows into the production Databricks environment.
• Set up monitoring tools within Databricks to track performance and detect issues.
• Implement automated alerts for data processing failures or performance degradation.

Documentation and Training:

• Document the entire migration process, including technical details of the new Spark SQL workflow.
• Provide training sessions and materials for end-users and support teams to familiarize them with the new system.

Tech Stack Leveraged:

• Azure Databricks (Spark SQL) for scalable data processing.
• Sigma Computing for real-time input tables and visualization.
• SQL Server Management Studio (SSMS) for data storage and querying.
• Azure Storage for data persistence and backup.
• Databricks Monitoring Tools for tracking system performance.

Benefits Delivered:

• Real-Time Data Updates: Users can input data in Sigma, and the changes reflect instantly in Databricks views and dashboards.
• Scalability & Performance: Migrating to Databricks with Spark SQL enables efficient distributed processing, reducing processing time significantly.
• Cost Reduction: Eliminates dependency on CSV file-based workflows, reducing data redundancy and manual handling.
• Advanced Analytics & AI: Leverages Databricks for machine learning and predictive analytics beyond traditional SQL transformations.
• Seamless Collaboration: The integration of Databricks and Sigma enables multiple teams to work collaboratively with real-time data insights.
• Improved Decision-Making: By replacing static CSV files with dynamic Sigma input tables, stakeholders can make data-driven decisions more effectively.

This migration ensures that the client organization transitions smoothly to a cloud-based, scalable data processing environment while enhancing real-time analytics capabilities.