In our earlier weblog, we explored the methodology beneficial by our Skilled Providers groups for executing advanced information warehouse migrations to Databricks. We highlighted the intricacies and challenges that may come up throughout such initiatives and emphasised the significance of creating pivotal selections throughout the migration technique and design section. These selections considerably affect each the migration’s execution and the structure of your goal information platform. On this submit, we dive into these selections and description the important thing information factors essential to make knowledgeable, efficient selections all through the migration course of.
Migration technique: ETL first or BI first?
When you’ve established your migration technique and designed a high-level goal information structure, the following determination is figuring out which workloads emigrate first. Two dominant approaches are:
- ETL-First Migration (Again-to-Entrance)
- BI-First Migration (Entrance-to-Again)
ETL-First Migration: Constructing the Basis
The ETL-first, or back-to-front, migration begins by making a complete Lakehouse Information Mannequin, progressing by the Bronze, Silver, and Gold layers. This method entails establishing information governance with Unity Catalog, ingesting information with instruments like LakeFlow Join and making use of methods like change information seize (CDC), and changing legacy ETL workflows and saved procedures into Databricks ETL. After rigorous testing, BI reviews are repointed, and the AI/ML ecosystem is constructed on the Databricks Platform.
This technique mirrors the pure move of knowledge—producing and onboarding information, then reworking it to fulfill use case necessities. It permits for a phased rollout of dependable pipelines and optimized Bronze and Silver layers, minimizing inconsistencies and enhancing the standard of knowledge for BI. That is significantly helpful for designing new Lakehouse information fashions from scratch, implementing Information Mesh, or redesigning information domains.
Nonetheless, this method usually delays seen outcomes for enterprise customers, whose budgets sometimes fund these initiatives. Migrating BI final implies that enhancements in efficiency, insights, and assist for predictive analytics and GenAI initiatives might not materialize for months. Altering enterprise necessities throughout migration may create transferring goalposts, affecting mission momentum and organizational buy-in. The complete advantages are solely realized as soon as the whole pipeline is accomplished and key topic areas within the Silver and Gold layers are constructed.
BI-First Migration: Delivering Rapid Worth
The BI-first, or front-to-back, migration prioritizes the consumption layer. This method offers customers early entry to the brand new information platform, showcasing its capabilities whereas migrating workflows that populate the consumption layer in a phased method, both by use case or area.
Key Product Options Enabling BI-First Migration
Two standout options of the Databricks Platform make the BI-first migration method extremely sensible and impactful: Lakehouse Federation and LakeFlow Join. These capabilities streamline the method of modernizing BI programs whereas making certain agility, safety, and scalability in your migration efforts.
- Lakehouse Federation: Unify Entry Throughout Siloed Information Sources
Lakehouse Federation permits organizations to seamlessly entry and question information throughout a number of siloed enterprise information warehouses (EDWs) and operational programs. It helps integration with main information platforms, together with Teradata, Oracle, SQL Server, Snowflake, Redshift, and BigQuery. - LakeFlow Join:
LakeFlow Join revolutionizes the way in which information is ingested and synchronized by leveraging Change Information Seize (CDC) expertise. This characteristic permits real-time, incremental information ingestion into Databricks, making certain that the platform at all times displays up-to-date data.
Patterns for BI-First Migration
By leveraging Lakehouse Federation and LakeFlow Join, organizations can implement two distinct patterns for BI-first migration:
- Federate, Then Migrate:
Shortly federate legacy EDWs, expose their tables through Unity Catalog, and allow cross-system evaluation. Incrementally ingest required information into Delta Lake, carry out ETL to construct Gold layer aggregates, and repoint BI reviews to Databricks. - Replicate, Then Migrate:
Use CDC pipelines to duplicate operational and EDW information into the Bronze layer. Rework the information in Delta Lake and modernize BI workflows, unlocking siloed information for ML and GenAI initiatives.
Each patterns might be carried out use case by use case in an agile, phased method. This ensures early enterprise worth, aligns with organizational priorities, and units a blueprint for future initiatives. Legacy ETL might be migrated later, transitioning information sources to their true origins and retiring legacy EDW programs.
Conclusion
These migration methods present a transparent path to modernizing your information platform with Databricks. By leveraging instruments like Unity Catalog, Lakehouse Federation, and LakeFlow Join, you possibly can align your structure and technique with enterprise objectives whereas enabling superior analytics capabilities. Whether or not you prioritize ETL-first or BI-first migration, the hot button is delivering incremental worth and sustaining momentum all through the transformation journey.