Migrating a Manufacturing and Retail Data Warehouse from Oracle to ClickHouse
Client:
Premium food manufacturer selling through tens of thousands of retail outlets in the United States.
Challenge:
A legacy Oracle warehouse containing 20 years of sales data could not support growing analytical demand. Complex joins and aggregations caused slow queries and out-of-memory failures, delaying the wider migration program.
Key Success Metrics:
- Resolved query performance and out-of-memory failures.
- Stabilized ETL processing across datasets exceeding 300 million rows.
- Accelerated the move toward retiring the legacy Oracle warehouse.
Business Challenge
Twenty years of sales data, complex analytical queries and limited ClickHouse experience combined to stall the migration program.
Legacy performance constraints
Large sales datasets and complex analytical queries exceeded the practical limits of the existing warehouse design.
Data was difficult to use
Downstream analysts needed better-structured datasets for sales, logistics, manufacturing, and business intelligence.
Migration progress was too slow
Limited ClickHouse expertise and unresolved technical issues delayed the expansion of the new platform and retirement of Oracle.
Architecture and Tech Stack
ClickHouse became the target platform, with refreshable materialized views managing ETL dependencies, while the Oracle warehouse holding 20 years of sales data was decommissioned.
ClickHouse
Oracle (legacy)
Implementation and Solution
The team first identified the causes of slow queries and memory failures. It then corrected the schema and query design, created analyst-ready datasets, and improved ETL dependency management.
Performance and memory diagnosis
Identified the root causes of the slow queries and out-of-memory exceptions on the largest sales datasets.
Schema and query optimization
Reworked schema design, query structure and ClickHouse query settings so that memory use stayed within limits at full data volume.
Business intelligence dataset development
Built analyst-ready datasets for sales, logistics and manufacturing, modeled to ClickHouse best practice for performance and memory footprint.
Refreshable materialized views and team enablement
Encoded the dependencies between views in ClickHouse refreshable materialized views, so that transformations run in a predictable order without external orchestration, and worked with the client analysts on ClickHouse internals and query efficiency.
Results (ROI)
Queries completed within memory limits at full data volume, ETL ran reliably on the largest datasets, and the client's analysts took on ClickHouse work themselves.
Performance and memory failures resolved
Schema, query structure and ClickHouse query settings were reworked so that memory use stayed within limits at full data volume.
Reliable ETL across 300 million-plus row datasets
Refreshable materialized views run transformations in a predictable order, stabilizing ETL across datasets exceeding 300 million rows.
Client analysts upskilled on ClickHouse
The team worked with the client's analysts on ClickHouse internals and query efficiency.
Legacy warehouse retirement accelerated
Resolving the blocking technical issues let the wider migration program move forward toward decommissioning Oracle.
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