Migrating from Hadoop to Databricks saved $1.3M.
Pythian deployed a phased migration to Databricks Lakehouse, allowing the company to separate storage and compute.
An aging, unsupported 25-component legacy Hadoop cluster left a major US energy provider facing serious security and regulatory compliance risks. To eliminate this technical debt, the company partnered with Pythian to migrate its multi-petabyte smart-meter and IoT telemetry data estate into a modern cloud architecture. Pythian deployed an automated, zero-disruption migration framework that completely separated storage and compute while streamlining IoT data governance. As a result, operations teams have reclaimed thousands of hours previously lost to manual triage, allowing engineers to run compliance reports in under an hour and instantly deploy predictive maintenance models using real-time IoT device streams.
Pythian operated as a direct extension of our engineering team, decomposing a decade of legacy data silos and safely deploying our automated pipeline stack into a secure cloud platform with zero operational friction."
Director of Grid Analytics
US Energy Provider
$1.3M
Annual operational savings
55%
TCO reduction
10x
Faster query performance
Squeeze more value from your IoT data while slashing Hadoop overhead.
A major US energy provider faced severe grid compliance risks from unsupported legacy clusters.
Pythian migrated the tightly coupled Hadoop architecture to a Databricks data lakehouse with real-time streaming and secure governance.
Securing our legacy data systems was a critical priority for the company. Pythian safely moved our data and strict security rules to the cloud without interrupting our daily compliance reporting or grid operations."
Director of Grid Analytics
US Energy Provider
Outdated clusters exposed security risks
Unsupported Hadoop clusters running Cloudera software carried 14 critical security gaps, creating massive IoT data risks that were flagged directly to the board.
Undocumented dependencies stalled data pipelines
Over 12 years of rapid IoT growth created a messy web of 400 legacy Hadoop tables and 300 unmanaged batch workflows with zero centralized tracking.
Slow batch engines missed regulatory deadlines
Outdated processing engines caused daily smart-meter data workflows to stretch to 11 hours, missing critical filing windows.
Skyrocketing infrastructure costs drained budgets
The utility company spent $2.4M annually maintaining physical on-premises servers alongside heavy overhead for scarce, specialized platform administrators.
Audited the sprawling legacy grid architecture to establish a clear baseline map.
The initial discovery phase mapped out system interdependencies and hidden data bottlenecks across the legacy clusters. The engineering team established a definitive baseline for the entire migration. The inventory served as the project's single source of truth to ensure a predictable, risk-free technical transition.
Built a modern Databricks Lakehouse platform to separate compute from storage.
The new cloud architecture utilized Delta Lake to separate processing layers from long-term data storage. Decoupling these systems eliminated resource contention across business functions while giving engineers elastic scaling. The platform also integrated advanced data governance frameworks to secure row-level privacy and satisfy strict federal regulatory compliance audits.
Migrated 4.2 petabytes of local data to the cloud with zero disruption.
Engineers seamlessly transferred multi-petabyte datasets from aging on-premises infrastructure into secure cloud object environments without database downtime. The migration team converted hundreds of complex legacy batch configurations directly into modern, automated orchestration workflows. Making the shift cleared massive operational technical debt and instantly unlocked high-velocity processing speeds.
Deployed Spark Structured Streaming pipelines to capture live IoT signals and power immediate reporting.
The deployment of real-time streaming data flows replaced slow overnight batch jobs with instantaneous processing for millions of smart-meter signals. Transitioning to active streams reduced mandatory compliance reporting windows from 11 hours to under 60 minutes. Integrated machine learning workflows now empower operations teams to run predictive grid models and proactively catch equipment failures.
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