Legacy Data Warehouse Migration to Databricks

Challenges

The client’s old data warehouse blocked their business growth and decision-making.

First, processing data took too much time starting with reports that sometimes took 4-8 hours to finish. This slowed down the team’s reactions to market changes and during peak seasons like Black Friday. Secondly, different departments ran their own pipelines, which caused data silos in sales metrics around 15-20% and inconsistent reports between teams, resulting in misleading strategies.

Next Infrastructure upkeeping was also a big problem. The system needed manual fixes, constant updates, and performance adjustments, which increased running costs. The setup also couldn’t handle real-time data processing or advanced tasks like predicting trends, forecasting demand, or analyzing customer behaviors.

The client wanted a flexible cloud-based platform to combine data, speed up performance, and encourage future progress.

Solutions

Lucent Innovation holds expertise in Databricks engineering services. We worked closely with the client and suggested migrating the rigid warehouse to a modern Databricks Lakehouse architecture.

To avoid disrupting business processes, we prepared a detailed 6-month migration plan. We started by reviewing the current data sources, workflows, and pipelines in the first 1-4 weeks. In the first 2 months, we moved on to set up the foundation. We deployed Databricks in a hybrid environment using AWS and Azure with Terraform. This setup included Delta Lake for reliable, ACID-compliant storage.

Over the 2 months, we focused on moving historical data. We transferred more than five years of stored data in bulk, resolving schema differences with custom scripts to maintain data accuracy.

The focus during Months 3 to 6 was on building Incremental Pipelines and preparing for Go-Live. We created both real-time and batch pipelines using Databricks Workflows. We also shifted reporting over to interactive Databricks SQL dashboards to ensure a smooth transition.

We used the reliable Bronze-Silver-Gold medallion framework:

• Bronze: stores the raw data as it is received from the original sources without any processing. 
• Silver: contains refined, standardized, and enhanced data. It includes checks for accuracy and removes duplicates.
• Gold: holds combined and organized datasets. These are ready to use with business intelligence tools and machine learning models.

This upgrade not only brought the data platform into the modern era but also prepared it to support AI and machine learning in the future.