Data Lakehouse Platform Development for Data Analytics

What this project is

Data lakes are cheap and messy. Warehouses are structured and expensive at scale. A lakehouse keeps data in open object storage, then adds a table layer with transactions, schema enforcement, and time travel, so the same data serves both cheap storage and reliable analytics.

The table format is the key decision. It decides how writes, deletes, schema changes, and concurrent readers behave, and switching formats later means rewriting data. The platform must therefore choose its format deliberately and build governance around it from the start.

Core features

• Open table formats with transactions, schema evolution, and time travel.
• Partitioning and file sizing tuned per table for query speed and cost.
• Multiple query engines reading the same tables for SQL, notebooks, and streaming.
• Schema enforcement and data validation on writes.
• Access controls and audit logs at table and column level.
• Compaction and cleanup jobs keeping file counts and storage costs under control.

How it is built

1. Storage and format first. Object storage plus one table format, proven on a real workload.
2. Core tables second, migrated from existing lake or warehouse tables with validation.
3. Query engines third, adding engines teams already use against the same tables.
4. Governance fourth. Access controls, schema rules, and audit logging.
5. Optimisation last. Partitioning, compaction, and cost tuning at scale.

Technology considerations

Object storage layout decides query cost: partition choices and file sizes matter more than engine choice for most workloads. The table format's transaction log is the source of truth, so backup and recovery must cover it alongside the data files. Compaction jobs are operational essentials, not tuning extras, since small files destroy query performance over time. Storage and query costs vary with volume; confirm with a quote.

• Object storage with a chosen open table format and transaction log.
• Partitioning and file-size policies per table.
• Multiple engines reading the same table layer.
• Compaction, cleanup, and vacuum jobs on schedule.

Challenges to plan for

• Small-file sprawl. Frequent small writes degrade queries until compaction runs regularly.
• Format lock-in. Migrating table formats later is expensive, so the first choice deserves care.
• Governance gaps. Open storage with weak controls leaks data faster than a warehouse would.
• Engine differences. Engines reading the same tables may disagree on semantics, so validation matters.

A worked example

A media company stores years of viewing logs, too large and varied for its warehouse budget, but its analysts need reliable tables, not a folder of files. It builds a lakehouse: viewing events land in object storage, a table layer adds transactions, schema enforcement, and time travel, and analysts query the same tables from SQL tools and notebooks alike.

When a bad write corrupts a day's viewing table, time travel lets the team query the table as it was before the write, recover the correct data, and fix the pipeline without restoring from backup. Compaction jobs run nightly, merging small files from frequent writes so query performance stays stable as data grows. Storage costs stay well below the old warehouse bill, and the data science team reads the same tables its analysts use, ending the era of separate extracts that disagreed. The lakehouse did not organise the data by itself; the table discipline and governance around it did.

A year in, the media company's finance team asks whether the lakehouse actually saved money against the old warehouse, and the answer is documented rather than guessed: storage costs per terabyte, query costs per team, and the compaction jobs' effect on both are all visible in the platform's own reporting. The data science team, which once maintained its own extracts, now works from the same tables as analysts, and a disagreement between a notebook result and a dashboard result is resolved in an afternoon by tracing both to the same table version, instead of a week of reconciling extracts.

Who it is for, and its limits

This fits teams with large, varied data volumes, or teams wanting one storage layer for analytics, ML, and streaming.

It does not fit small, stable warehouses where a managed warehouse is simpler. It also does not organise data by itself; table discipline and governance do that work.

Frequently asked questions