Client Data Quality Automation & Alerts using Databricks
Challenges
The enterprise wanted Databricks to unify analytics, accelerate insights, and scale data-driven decision-making.
However, business teams experienced inconsistent data availability, and reports broke without warning. It did not stop there, but the analytics delivery slowed as engineering teams struggled to keep pipelines running.
Outcomes
35–50% reduction in DBU consumption through right-sized compute and targeted Photon enablement
Achieved 99% pipeline reliability following ingestion and orchestration redesign
Solution type
RAG
Challenges
Solution
Technology Stack
Outcomes
Summary: Databricks Lakehouse Modernisation Overview
One of the leading enterprises headquartered in the UK was aiming to adopt Databricks. The objective was to use Databricks as its strategic Lakehouse platform to modernise data engineering and analytics at scale.
The initial Databricks deployment was successful, but the platform started breaking down under real operational load. Serious issues around pipeline reliability, data consistency and uncontrolled compute consumption began to surface as day-to-day usage increased.
As these reliability and governance concerns persisted, Databricks utilisation was limited only to a small group of engineering-led teams.
Instead, significant manual efforts were required to keep the Lakehouse operational, setting the stage for a deeper platform-level investigation and recovery.