In our earlier weblog, we explored the methodology really useful by our Skilled Providers groups for executing complicated knowledge 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 through the migration technique and design part. These decisions considerably affect each the migration’s execution and the structure of your goal knowledge platform. On this publish, we dive into these selections and description the important thing knowledge factors essential to make knowledgeable, efficient decisions 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 following resolution 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 via the Bronze, Silver, and Gold layers. This method entails organising knowledge governance with Unity Catalog, ingesting knowledge with instruments like LakeFlow Join and making use of strategies like change knowledge seize (CDC), and changing legacy ETL workflows and saved procedures into Databricks ETL. After rigorous testing, BI stories are repointed, and the AI/ML ecosystem is constructed on the Databricks Platform.
This technique mirrors the pure circulation of information—producing and onboarding knowledge, 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 information for BI. That is significantly helpful for designing new Lakehouse knowledge fashions from scratch, implementing Information Mesh, or redesigning knowledge domains.
Nevertheless, 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 could not materialize for months. Altering enterprise necessities throughout migration can even create transferring goalposts, affecting challenge 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 offers 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 Information Sources
Lakehouse Federation allows 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 Information Seize (CDC) expertise. This function allows 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:
Shortly federate legacy EDWs, expose their tables through 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 stories to Databricks. - Replicate, Then Migrate:
Use CDC pipelines to duplicate 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 initiatives.
Each patterns might 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 initiatives. Legacy ETL might 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 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’s delivering incremental worth and sustaining momentum all through the transformation journey.
