Data Lake Modernization
Open data platform on Hadoop + Spark + Kafka — an alternative to proprietary data platforms
Modernize proprietary Hadoop platforms into open, scalable data lakehouses on upstream Apache and S3-based storage with enterprise support, governance, and managed operations.
Why Modernize?
Benefits of moving to open data lakehouse
Platform Capabilities
Complete data lakehouse stack
Storage & Lakehouse
Hadoop (HDFS) or Ceph S3 backend with Apache Iceberg for schema evolution and governance
Processing & Streaming
Apache Spark for ETL, Spark Operator for Kubernetes, and Kafka for real-time processing
SQL, BI & Exploration
Trino for interactive SQL across Iceberg tables and Superset for self-service BI dashboards
Orchestration & DataOps
Apache Airflow for pipeline orchestration with DataOps practices and environment promotion
ML Enablement
JupyterHub for notebooks, MLflow for experiment tracking, and KServe for model serving
Production Operations
Observability integration, upgrade strategy, patch cadence, and reliability improvements
Modernization Approach
Phased migration to production
Assessment & Blueprint
2-4 weeks
Inventory current platform, define target architecture
Foundation Build
4-8 weeks
Deploy core services, establish data zones and guardrails
Workload Migration
Iterative
Prioritize and migrate pipelines progressively
Production Hardening
Ongoing
Upgrade strategy, runbooks, managed services handoff
Use Cases
What you can build with the modern data platform
Start Your Data Lake Modernization
Get a modernization assessment to validate target architecture, migration strategy, and a practical path to production.
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