The Lean Table Format

Hudi, Iceberg, and Delta are the hottest jargon in modern data lake design. They bring impressive capabilities to cloud object storage such as ACID transactions, schema evolution, time travel, incremental updates, and more. Used well, they make large data platforms far more reliable.

But here is the uncomfortable truth nobody likes to say out loud:
 If your data is small, you probably do not need any of them.

If your datasets are only a few gigabytes, the complexity of a table format is likely unnecessary overhead. Table formats solve problems that only appear at scale. For lean teams with lean data, they often add cost, cognitive load, and operational friction without providing meaningful upside.

In a lean data lake, your goal is to minimize moving parts. Table formats are only one small component of a much larger system, and skipping them can save your team time, money, and maintenance work.

Why Table Formats Do Not Matter at Small Scale

Iceberg, Hudi, and Delta bring real benefits, but they also bring real complexity. You must tune metadata behavior, manage manifests, configure compaction, choose merge strategies, and maintain a catalog. At small scale, these decisions rarely change anything.

For data under roughly 10GBs, many of the advantages simply do not matter because…

1. Metadata is tiny
2. Rewriting whole partitions is cheap
3. File counts stay manageable
4. Advanced indexing and partition pruning are unnecessary

A Simple Triage: Do You Actually Need a Table Format

Use these questions to check yourself before reaching for Iceberg, Hudi, or Delta.

What is the size of the data I am processing
 If you can overwrite the full dataset or the relevant partitions every time you update it, a table format is unnecessary. Plain Parquet works perfectly well.

Can I rely on simple partitioning
 If you can partition your data cleanly, for example by ingest date or batch identifier, then you can track the latest state simply by reading the most recent partition. You do not need a transaction layer for this pattern.

Do I need built in history, updates, and deletes
 If you do not need automatic versioning, change data capture, or fine grained row level updates, then you do not need the transaction machinery of a table format. You can maintain history by writing snapshots or dated partitions whenever needed.

What Small Teams Should Do Instead

If you are operating with modest data sizes, your time is better spent building simple and reliable patterns rather than wrestling with the complexity of table formats. A lean data lake benefits most from predictable structure and clear processing steps, not from sophisticated storage engines.

A few practices will carry you much further than adopting Iceberg, Hudi, or Delta at this scale.

Use clean folder structures with Parquet files
 Organize your data by clear partitions such as ingest date, batch identifier, or logical grouping. Good partitioning keeps queries fast and maintenance simple without any transaction layer involved.

Store raw, cleaned, and modeled data in separate zones
 You can follow a simple medallion style flow with Bronze for raw data, Silver for cleaned and standardized data, and Gold for business ready outputs. This separation alone gives you traceability, reproducibility, and clarity without needing a table format.

Rely on full overwrites or partition overwrites when data changes
 At small sizes, overwriting a partition or even an entire table is cheap. This avoids the need for row level updates, compaction settings, or merge strategies. It also keeps pipelines easy to reason about.

Keep history by writing new snapshots
 Instead of depending on built in time travel, simply write new versions of your data with a timestamp or version number. It is transparent, easy to debug, and works perfectly well for modest volumes.

Write metadata to a metadata database
Your gold layer can write metadata to a metadata database so cloud query engines such as Trino, AWS Athena, Redshift Spectrum, Google BigQuery External Tables, and Azure Synapse Serverless can discover your Parquet files and query them as regular tables without requiring a table format. This approach gives you the benefits of structured table access while keeping your storage simple and lightweight. It also allows different teams and tools to work with your gold datasets without forcing everyone into a specific storage engine or transaction model.

Wrapping Up

Table formats are powerful technology, but they are not a requirement for every data platform. They shine in environments with massive scale, complex ingestion patterns, and heavy update workloads. If your datasets are small and your pipelines are straightforward, the complexity they introduce is usually not worth the cost.

A lean approach gives you clarity and speed. You can build a trustworthy, maintainable data lake using simple Parquet files, clean folder structures, and well designed processing layers. When your data volume grows or your workloads become more demanding, you can always adopt a table format later with a clear understanding of why you need it.

Keep things simple and nimble until complexity pushes you over the edge.