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Migrate a pyspark project to dbt

Supademo

This demo provides a walkthrough of migrating a PySpark-based e-commerce reporting project in Databricks to dbt. With DataMates, you can investigate and plan migration scope, convert code automatically, test thoroughly, execute in phases, validate results, and maintain detailed documentation. Transform PySpark code to dbt models with DataMates' conversion assistance. You can analyze PySpark repositories, and create equivalent dbt models and marts.

Walkthrough

  1. Select a DataMate and context from Knowledge Hub: The demo begins on the "DataMates" page within Altimate AI, where you would select a "Datamate" (an AI teammate for data tasks). It then guides you to the "Knowledge hub," which is a central repository for verified context and information relevant to your projects, helping to reduce AI hallucinations and rework.

  2. Utilizing the Knowledge Hub for Migration Guidance: Within the Knowledge Hub, you can find specific guides, such as "pyspark dbt migratiion which provides help with PySpark to dbt migration. The demo highlights the ability to copy a dynamic URL for your knowledge document, making it accessible to anyone with the link and usable in various IDEs. It also outlines key benefits of migrating to dbt, such as faster batch processing, increased data, and improved collaboration and data governance, and helps determine when to migrate versus when to keep PySpark.

  3. Initiating the Migration Process: The user indicates they have an e-commerce reporting project in Databricks using PySpark and need to migrate it to dbt, adhering to their organization's best practices. The DataMate (AI assistant) confirms its role in assisting with the migration and begins by understanding the current setup and gathering relevant information.

  4. Codebase Understanding and Migration Planning: The DataMate intelligently searches for relevant previous memories to reduce hallucinations or rework, demonstrating its understanding of your entire tech stack, coding styles, and architectural choices. It analyzes the existing PySpark project structure, including main application entry points, data readers, data quality validation, and transformation modules for revenue, customer, product, and time series analytics. The AI examines transformation modules to understand the business logic to be migrated and reviews the profiles.yml file to understand the data warehouse setup. The DataMate then proceeds to plan and implement the migration based on the organization's best practices.

  5. Review, Edit, and Testing: The process involves reviewing, editing, and accepting changes, with every interaction personalizing the DataMate for you. Finally, the migration is ready to be tested.