In our earlier weblog, we explored the methodology beneficial by our Skilled Companies groups for executing advanced information warehouse migrations to Databricks. We highlighted the intricacies and challenges that may come up throughout such tasks and emphasised the significance of constructing pivotal selections throughout the migration technique and design part. 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 choice 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 way of the Bronze, Silver, and Gold layers. This strategy 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 experiences are repointed, and the AI/ML ecosystem is constructed on the Databricks Platform.
This technique mirrors the pure movement of knowledge—producing and onboarding information, then reworking it to satisfy use case necessities. It permits for a phased rollout of dependable pipelines and optimized Bronze and Silver layers, minimizing inconsistencies and bettering the standard of knowledge for BI. That is notably helpful for designing new Lakehouse information fashions from scratch, implementing Information Mesh, or redesigning information domains.
Nonetheless, this strategy usually delays seen outcomes for enterprise customers, whose budgets usually fund these initiatives. Migrating BI final implies that enhancements in efficiency, insights, and help for predictive analytics and GenAI tasks might not materialize for months. Altering enterprise necessities throughout migration also can create transferring goalposts, affecting venture momentum and organizational buy-in. The total advantages are solely realized as soon as all the pipeline is accomplished and key topic areas within the Silver and Gold layers are constructed.
BI-First Migration: Delivering Instant Worth
The BI-first, or front-to-back, migration prioritizes the consumption layer. This strategy 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 strategy extremely sensible and impactful: Lakehouse Federation and LakeFlow Join. These capabilities streamline the method of modernizing BI techniques whereas guaranteeing agility, safety, and scalability in your migration efforts.
- Lakehouse Federation: Unify Entry Throughout Siloed Information Sources
Lakehouse Federation allows organizations to seamlessly entry and question information throughout a number of siloed enterprise information warehouses (EDWs) and operational techniques. It helps integration with main information platforms, together with Teradata, Oracle, SQL Server, Snowflake, Redshift, and BigQuery. - LakeFlow Join:
LakeFlow Join revolutionizes the best way information is ingested and synchronized by leveraging Change Information Seize (CDC) know-how. This function allows real-time, incremental information ingestion into Databricks, guaranteeing 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:
Rapidly 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 experiences to Databricks. - Replicate, Then Migrate:
Use CDC pipelines to duplicate operational and EDW information into the Bronze layer. Remodel the info in Delta Lake and modernize BI workflows, unlocking siloed information for ML and GenAI tasks.
Each patterns will be applied use case by use case in an agile, phased strategy. This ensures early enterprise worth, aligns with organizational priorities, and units a blueprint for future tasks. Legacy ETL will be migrated later, transitioning information sources to their true origins and retiring legacy EDW techniques.
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 targets whereas enabling superior analytics capabilities. Whether or not you prioritize ETL-first or BI-first migration, the secret is delivering incremental worth and sustaining momentum all through the transformation journey.
