Databricks Data Warehouse

Modernizing Big Data Infrastructure from Hadoop for a Tier-1 Financial Institution

4 min read

Mar 5, 2026 1:46:39 PM

From unsupported Hadoop to governed Databricks Lakehouse — 55% cost reduction and production AI in under a year

A global financial institution with $200B+ in assets under management ran regulatory reporting, fraud detection, and risk analytics on an aging, unsupported Hadoop cluster. Pythian inventoried the 25+ component ecosystem, executed a phased migration, and delivered $1.3M in annual savings, 10x query performance, and a production-ready AI foundation.

55%

Reduction in infrastructure costs

$1.3M

Annual cost savings

10x

Faster query performance

Technologies used

Industry: Financial Services / Tier-1 Banking

Organization Scale: Global enterprise, $200B+ AUM, 15,000+ employees across 30 countries

Tech stack:

Cloudera CDH 6 (past end-of-support)
HDFS (multi-petabyte)
Hive, MapReduce, Spark
Oozie, Sqoop, Flume
Apache Ranger, Kerberos, Apache Atlas
Impala, Hue
Databricks Lakehouse (target)
Apache Airflow
Looker, Power BI

Unsupported infrastructure meets regulatory scrutiny

The institution built its data foundation on Hadoop 12 years ago. The cluster grew into a sprawling ecosystem powering hundreds of daily batch jobs, compliance feeds, and fraud detection pipelines. When CDH 6 reached end-of-support, running an unsupported platform was no longer viable. A previous consulting partner had estimated a multi-year migration and balked at the complexity. The institution needed a firm fluent in both legacy Hadoop and modern cloud.

14 critical vulnerabilities on an unsupported cluster

CDH 6 lost vendor support in September 2022. An internal security audit flagged 14 critical unpatched vulnerabilities, escalating the issue to the board's risk committee. A hardware refresh deadline six months away forced a build-versus-migrate decision.

12 years of undocumented Hadoop sprawl

The environment spanned 25+ interdependent components: 400+ Hive tables, 87 MapReduce jobs, hundreds of Spark applications, 300+ Oozie workflows, Sqoop and Flume pipelines, and Kerberos/Ranger security policies. No single person understood the full dependency map.

11-hour reports breaching regulatory deadlines

Batch reports that once took four hours had stretched to 11, routinely missing the regulator's morning window. Fraud scoring took 45 minutes on MapReduce. The institution spent $2.4M/year on infrastructure and $600K on Hadoop administrators it could no longer recruit.

Ecosystem decomposition to production AI

Migration required decomposing a tightly coupled web of storage, compute, orchestration, security, and governance — then rebuilding each layer without disrupting the regulatory reporting and risk operations the business depended on daily.

Strategic Architecture

Pythian designed a phased migration to Databricks Lakehouse, separating storage and compute for the first time. The architecture used Delta Lake with Iceberg interoperability, Unity Catalog (replacing Hive Metastore and Atlas), and a security model mapping Kerberos/Ranger to cloud-native IAM with the row-level access controls regulators required.

The forecasting layer

Pythian profiled every workload by complexity, business value, and regulatory sensitivity to determine the optimal migration sequence.

The conversion layer

Our engineers handled the hard translations — MapReduce to Spark, HiveQL to Spark SQL, Oozie to Airflow, Sqoop to CDC — preserving the business logic embedded in a decade of accumulated code.

The governance layer

Unity Catalog replaced both Hive Metastore and Apache Atlas, while Kerberos/Ranger policies were remapped to cloud-native IAM with the fine-grained, row-level controls regulators required.

Implementation roadmap

Phase 1: Assessment and stabilization

Breaking the silos between 3PLs and internal WMS to Pythian remediated the 14 critical vulnerabilities through configuration hardening and network-level controls, buying the institution time to migrate safely. We completed the full workload inventory and dependency map, then identified high-value workloads for first-wave migration and delivered a cost model to the CFO's office.create a single, real-time source of truth for inventory.

Phase 2: Migration and modernization

Pythian converted 87 MapReduce jobs to optimized Spark and refactored hundreds of existing Spark applications for Databricks Runtime. We rewrote 400+ HiveQL queries into Spark SQL and rebuilt more than 300 Oozie workflows as Airflow DAGs. The team replaced Sqoop-based ingestion with modern CDC pipelines, migrated 4.2 petabytes of HDFS data to cloud object storage, and remapped the Kerberos/Ranger security model to cloud-native IAM.

Phase 3: Outcomes and managed services

Pythian migrated regulatory reports to interactive Databricks SQL dashboards surfaced through Looker and Power BI, and re-deployed the fraud detection pipeline on Spark Structured Streaming. We enabled the data science team with Databricks MLflow for experiment tracking and model deployment. Pythian continues to provide managed services for ongoing optimization and cost governance.

Quantifying the migration

The compliance and risk teams transitioned from reactive batch processing to real-time intelligence. By decomposing a decade of technical debt and rebuilding on modern foundations, the institution unlocked capabilities that were impossible on legacy Hadoop.

10x faster regulatory reporting

Reports that took 11 hours on Hive/MapReduce now complete in under 60 minutes on Databricks SQL. Business analysts run self-service queries in seconds.

$1.3M in annual infrastructure savings

Costs dropped from $2.4M to $1.1M/year — a 55% reduction — with elastic scaling that matches spending to actual demand.

$600K in avoided recruitment costs

The institution reallocated three Hadoop administrators to cloud engineering roles, eliminating the need to recruit for a vanishing talent pool.

Zero regulatory audit findings

The institution replaced an unsupported cluster carrying 14 known vulnerabilities with a fully governed cloud environment that passed its next audit with zero findings.

Sub-30-second fraud scoring

Fraud detection dropped from 45 minutes on batch MapReduce to under 30 seconds on Spark Structured Streaming, enabling the team to flag suspicious transactions before they settle.

Production AI in 90 days:

The data science team deployed its first production ML model (credit risk scoring) using Databricks MLflow within 90 days of platform go-live — a capability that had been on the roadmap for three years.

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