Modernize your information warehouse by migrating to Databricks
Legacy enterprise information warehouses (EDWs) have gotten a bottleneck for companies aiming to scale operations and undertake superior analytics. Conventional EDWs battle with:
Scalability: Costly {hardware} upgrades delay initiatives and restrict progress.
Value effectivity: Excessive upfront capital prices versus versatile pay-as-you-go cloud fashions.
Superior analytics: Restricted help for real-time insights, AI, and machine studying (ML).
Flexibility: Inflexible programs that may’t adapt to new use instances or combine numerous information sources.
Migrating to a contemporary platform like Databricks presents elastic scalability, diminished prices, built-in safety, and the power to combine open supply applied sciences, avoiding vendor lock-in. This transformation empowers organizations to unlock real-time analytics and AI-driven decision-making whereas future-proofing their infrastructure.
Clients are additionally realizing these benefits of transferring to the lakehouse. Take a look at this case examine from Trek Bicycle!
Earlier than Databricks, we needed to run our retail analytics as soon as a day on North American time, which meant our different areas obtained their information late. Now, we refresh the lakehouse 3 times per day, one for every area, and stakeholders obtain contemporary information in time to drive their selections. Primarily based on the outcomes we’ve achieved within the lakehouse, we’re taking a Databricks-first method to all our new initiatives. We’re even migrating lots of our on premises BI options to Databricks as a result of we’re all-in on the lakehouse.
— Advait Raje, Staff Lead, Information Engineering at Trek Bicycle
A confirmed technique for low-risk migration
Migrating an EDW is advanced, however doesn’t need to be dangerous. Databricks supplies a structured five-step framework to make sure a clean transition, which lots of of consumers have used.
Clients have been utilizing this migration technique to speed up their migration initiatives. Take a look at this case examine from VGZ!
Our new information platform was applied seamlessly inside three quarters: one for preliminary setup and golden supply extraction, adopted by 1 / 4 devoted to stabilization. This environment friendly rollout has introduced us outcomes which have exceeded our expectations and left us greater than glad.
– Rohan Horstman, Information Options Architect, VGZ
Step 1: Assess
A complete analysis of your present atmosphere lays the muse for achievement. Key assessments embody:
Enterprise use instances: Establish crucial workloads and future wants with stakeholder enter.
Structure analysis: Doc dependencies, information flows, and integration factors.
Technical metrics: Analyze ETL processes, question efficiency, and compliance necessities.
Step 2: Outline
Designing the longer term state of your information warehouse entails aligning enterprise targets with technical capabilities. This step contains defining key efficiency indicators (KPIs), too. Modernization selections embody:
Raise-and-shift migration: Shortly decommission legacy programs utilizing automated instruments like BladeBridge (now a part of Databricks).
Full modernization: Rebuild workflows to allow superior use instances like ML-based predictive analytics.
Hybrid method: Begin with lift-and-shift, then modernize incrementally by including new capabilities.
Choosing the precise know-how stack is crucial—Databricks Lakehouse combines Delta Lake storage with distributed computing for scalability and interoperability.
Step 3: Select
Databricks helps two major migration technique approaches:
Ingestion and ETL first (back-to-front): Construct a stable information basis earlier than migrating dashboards—superb for gradual optimization.
BI first (front-to-back): Replicate dashboards first to showcase quick enterprise worth—superb for driving person adoption early on.
Utilizing Databricks-native options like Lakehouse Federation ensures seamless transitions throughout each methods.
Step 4: Validate
Earlier than scaling up migration efforts, validate assumptions and establish potential dangers. Validation ought to embody one of many following:
Proof of Idea (POC): Check the feasibility of particular pipelines or queries.
Minimal Viable Product (MVP): Ship important options on a subset of workloads to show worth rapidly.
Clear success standards guarantee confidence within the migration course of.
Step 5: Implement
The ultimate step entails full-scale implementation with steady efficiency optimization and monitoring. Key focus areas embody:
Information governance and safety insurance policies for compliance.
Efficiency tuning through indexing, caching, and autoscaling mechanisms.
Ongoing monitoring to handle points proactively and optimize prices.
Data switch
The final step with any migration is information switch. A profitable migration extends past technical implementation to embody organizational adoption and experience improvement. Guaranteeing your groups can successfully make the most of the Databricks Platform requires deliberate planning for information acquisition and cultural adaptation to new workflows and capabilities.
Be sure to embody abilities improvement and alter administration, in addition to thorough documentation and a information base.
Databricks instruments that simplify migration
Databricks accelerates migrations with purpose-built options like Delta Lake’s unified storage format, automated ETL pipelines, and integrations with main ISVs akin to Fivetran and Tableau. These instruments cut back complexity whereas enabling real-time analytics and AI-driven insights.
Subsequent steps
Able to modernize your information warehouse? Obtain our eBook, “Remodeling Legacy Information Warehouses: A Strategic Migration Blueprint,” for detailed methods and greatest practices that guarantee a low-risk transition to the Databricks Information Intelligence Platform.
