In our earlier weblog, we explored the methodology really useful by our Skilled Providers groups for executing advanced knowledge warehouse migrations to Databricks. We highlighted the intricacies and challenges that may come up throughout such tasks and emphasised the significance of constructing pivotal choices in the course of the migration technique and design part. These selections considerably affect each the migration’s execution and the structure of your goal knowledge platform. On this submit, we dive into these choices and description the important thing knowledge 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 knowledge structure, the subsequent 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 Knowledge Mannequin, progressing by way of the Bronze, Silver, and Gold layers. This method includes organising knowledge governance with Unity Catalog, ingesting knowledge with instruments like LakeFlow Join and making use of methods like change knowledge 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 information—producing and onboarding knowledge, then remodeling it to fulfill 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 information for BI. That is significantly helpful for designing new Lakehouse knowledge fashions from scratch, implementing Knowledge Mesh, or redesigning knowledge domains.
Nevertheless, this method typically delays seen outcomes for enterprise customers, whose budgets usually fund these initiatives. Migrating BI final implies that enhancements in efficiency, insights, and assist for predictive analytics and GenAI tasks could not materialize for months. Altering enterprise necessities throughout migration may also create shifting goalposts, affecting venture momentum and organizational buy-in. The total 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 Fast Worth
The BI-first, or front-to-back, migration prioritizes the consumption layer. This method provides customers early entry to the brand new knowledge 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 guaranteeing agility, safety, and scalability in your migration efforts.
- Lakehouse Federation: Unify Entry Throughout Siloed Knowledge Sources
Lakehouse Federation permits organizations to seamlessly entry and question knowledge throughout a number of siloed enterprise knowledge warehouses (EDWs) and operational programs. It helps integration with main knowledge platforms, together with Teradata, Oracle, SQL Server, Snowflake, Redshift, and BigQuery. - LakeFlow Join:
LakeFlow Join revolutionizes the best way knowledge is ingested and synchronized by leveraging Change Knowledge Seize (CDC) expertise. This characteristic permits real-time, incremental knowledge ingestion into Databricks, guaranteeing that the platform all the time displays up-to-date info.
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 by way of Unity Catalog, and allow cross-system evaluation. Incrementally ingest required knowledge into Delta Lake, carry out ETL to construct Gold layer aggregates, and repoint BI experiences to Databricks. - Replicate, Then Migrate:
Use CDC pipelines to copy operational and EDW knowledge into the Bronze layer. Remodel the info in Delta Lake and modernize BI workflows, unlocking siloed knowledge for ML and GenAI tasks.
Each patterns will be applied 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 tasks. Legacy ETL will be migrated later, transitioning knowledge sources to their true origins and retiring legacy EDW programs.
Conclusion
These migration methods present a transparent path to modernizing your knowledge platform with Databricks. By leveraging instruments like Unity Catalog, Lakehouse Federation, and LakeFlow Join, you’ll be able to 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 bottom line is delivering incremental worth and sustaining momentum all through the transformation journey.
