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There is no universally best format or codec for every AI workload. Tabular features, text, images, audio and tensor data have different access patterns, so the right design usually separates queryable metadata from large modality-specific payloads and validates the complete pipeline end to end.
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What you are actually choosing
An AI data lake normally involves two separate decisions:
- File format: how bytes, columns, types, pages and encodings are arranged inside data files. Parquet and ORC are the principal columnar candidates covered here.
- Table format: how a collection of files is tracked over time, including metadata, snapshots, transactions, updates and deletes. Iceberg and Delta Lake provide this management layer.
Compression is a third decision inside the file-format choice. A codec changes storage size and processing cost, but it does not decide whether the table supports snapshots or concurrent writes. Keeping these layers distinct prevents a common design mistake: treating “Iceberg versus Parquet” or “Delta versus ORC” as if they were alternatives at the same level.
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Which file format should you use?
Use Parquet as the initial candidate for structured analytical data
Parquet is a columnar on-disk format designed for analytical access. A reader can work with selected columns rather than loading every field, and the specification defines several encodings that can reshape values before general-purpose compression. These include dictionary encoding, run-length and bit-packing encodings, delta encodings and byte-stream split.
Byte-stream split is an encoding transform, not compression by itself. The Parquet specification says it can improve compression ratio and speed when a compression algorithm is applied afterward. Therefore, a codec comparison that ignores writer encoding settings can produce a misleading result.
Parquet is a defensible cross-engine starting point because it is supported by many data and machine-learning ecosystems, but support is not identical everywhere. Confirm that every reader and writer in your ingestion, query and training chain understands the exact logical types, encodings and codec combinations you intend to use.
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Choose ORC when its ecosystem and access behavior fit better
ORC is also a columnar, type-aware format. Its specification describes dictionary encoding, bit packing, delta and run-length encoding, compression, column projection and min/max indexes. Readers can combine pushed-down filters with those indexes to skip row sets that cannot satisfy a predicate.
That structure can be valuable when your deployed engines make effective use of ORC’s projection and row-set indexes. It is not evidence that ORC universally outperforms Parquet. The reviewed material contains no controlled, current, multi-engine benchmark that establishes a winner for AI training or lake analytics, so the choice must be tested against your own workload.
Do not treat Iceberg as a replacement for Parquet or ORC
Iceberg is a table-management layer. Its specification tracks individual data files through metadata, manifests and snapshots, and it permits Parquet, Avro and ORC as data-file formats. You can therefore use Iceberg with Parquet or ORC rather than choosing Iceberg instead of them.
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Iceberg capabilities vary by specification version and by engine implementation. Before relying on a feature such as a particular schema evolution operation, row-level change, partition behavior or maintenance procedure, verify that the version implemented by every participating engine supports it.
Use Delta Lake for its transaction and table-management behavior
Delta Lake documents versioned Parquet data files together with a transaction log. It adds ACID transactions and Spark API compatibility around those files. As with Iceberg, Delta is useful when table state, concurrent changes, reproducible snapshots or update and delete semantics matter; it is not a competing physical encoding to Parquet.
Keep Apache Arrow in the in-memory category
Apache Arrow is primarily an in-memory columnar representation for computation and interoperability. A 2024 Microsoft Research paper describes Arrow as complementary to on-disk formats such as Parquet and ORC. Arrow can be an efficient interchange representation inside a processing or model-serving pipeline, but selecting Arrow in memory does not answer which durable files belong in object storage.
Comparison at a glance
| Layer or format | What it provides | When it is a fit | Important qualification |
|---|---|---|---|
| Parquet | Columnar data files; dictionary, run-length/bit-packing, delta and byte-stream-split encodings; multiple compression codecs | General structured analytics across varied engines | Verify exact encoding, logical-type and codec support in each implementation |
| ORC | Columnar, type-aware files; projection, compression and min/max row-set indexes | Engines and queries that benefit from ORC’s indexes and projection behavior | No reviewed evidence proves universal performance superiority over Parquet |
| Iceberg | Metadata, manifests and snapshots for tables using Parquet, Avro or ORC files | Snapshot isolation, evolution and table-level operations across supported engines | Features depend on specification and engine versions |
| Delta Lake | Versioned Parquet files, transaction log, ACID transactions and Spark API compatibility | Workloads needing transactionally managed Parquet tables | Validate transaction-log and feature support in all clients |
| Apache Arrow | In-memory columnar representation | Fast computation and interchange between processes or libraries | It is complementary to, not a substitute for, durable lake files |
Match the layout to the AI payload
Tabular features and labels
Use columnar files when training, feature generation or analysis often selects a subset of columns, filters rows or aggregates by dimensions. Keep stable identifiers, timestamps, labels and feature metadata in queryable columns. Partitioning and file layout should follow the filters and joins used by the actual jobs, not an abstract “AI” pattern.
Text and embeddings
Text records and embedding vectors can be stored with structured metadata in columnar files when the workload scans or filters them analytically. Measure the model loader’s behavior separately: a format that is efficient for SQL-style exploration may not be the fastest way to feed batches into a training process. Record whether the loader reads many small records, large contiguous vectors or only selected columns.
Images, audio and other binary objects
Images and audio have payload characteristics that differ from tabular features. A practical design is to keep identifiers, labels, dimensions, duration, provenance and other searchable metadata in Parquet or ORC while retaining the original or transformed binary payload in an object-oriented representation suited to the model pipeline. The available evidence does not establish one universal image or audio training format, so benchmark the representation your loader actually consumes.
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Dense tensors and serialized examples
Dense tensor data may be accessed in batches, by sample, by shard or by contiguous ranges. Those patterns can favor different layouts. Preserve the source representation when replay, auditability or reprocessing matters, then test a transformed representation against real batch construction and augmentation code. Do not infer a tensor-format winner from a benchmark on tabular rows.
Raw, transformed and curated layers
AWS data-lake guidance describes raw layers that retain source-defined formats and transformed or curated layers that use analytics-oriented formats. This separation supports provenance and replay: raw objects remain available if a parser, feature definition or model must be rerun, while curated tables are organized for repeated analysis.
Compression and encoding: the trade-off is multidimensional
Compression saves storage and I/O, but it consumes CPU and can change latency and throughput. Evaluate at least five outcomes together: compressed bytes, write time, write CPU, read time and read CPU. Add predicate-scan performance and model-input throughput when those paths matter.
Parquet codec options
The Parquet specification lists uncompressed pages, Snappy, GZIP, LZO, Brotli, Zstandard and LZ4_RAW. They occupy different points on the compression-ratio and processing-cost spectrum. The specification also documents a legacy framing scheme for the older Parquet LZ4 codec and recommends that writers move toward LZ4_RAW for interoperability.
| Codec or setting | Selection signal supported by the documentation | What to verify |
|---|---|---|
| Snappy | A practical baseline when low processing overhead and broad ecosystem support matter | Read/write CPU, compressed bytes and support in every training and query reader |
| Zstandard | A practical candidate when additional storage reduction may justify codec CPU | Compression level, CPU cost, latency and model-input throughput on representative data |
| GZIP, Brotli and LZO | Codecs included in the Parquet specification with different ratio and processing trade-offs | Actual engine support and whether the ratio gain offsets slower processing for your jobs |
| LZ4_RAW | Preferred direction for new LZ4 writers where interoperability is required | That readers support LZ4_RAW rather than relying on the older legacy framing |
| Uncompressed | Removes codec CPU from the path | Whether extra storage and I/O outweigh simpler processing |
Snappy and Zstandard are an engineering shortlist, not a measured universal ranking. No controlled comparison in the available material reports a general compression percentage or throughput result for AI data lakes.
Encoding settings matter as much as codec labels
Encodings can reduce or reshape values before the codec runs. Dictionary encoding may suit repeated categorical values; delta and run-length techniques can suit ordered or repetitive data; byte-stream split can make floating-point streams more compressible when followed by a codec. The best setting depends on cardinality, ordering, null patterns, numeric types and file size.
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Capture writer configuration in your benchmark record. Two files both labeled “Parquet with Zstandard” may behave differently if they use different encodings, compression levels, row-group sizes or logical-type mappings.
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| Axis | Questions to answer |
|---|---|
| Interoperability | Can every ingestion, query, feature-store and training library read and write the exact format, logical types, encodings and codecs? |
| Access pattern | Does each operation read a few columns, many columns, complete rows, filtered ranges or large binary payloads? |
| Throughput | What are sustained write speed, scan speed and model-input throughput under realistic concurrency? |
| Compression cost | How do storage savings compare with CPU, memory pressure and latency? |
| Data skipping | Can statistics, predicates, projection and row-set indexes avoid reading irrelevant data? |
| Schema and nested data | Are nested types, nullability, decimal precision, timestamps and future schema changes represented consistently? |
| Table behavior | Do you need snapshots, concurrent writes, transactions, streaming commits, updates, deletes or time travel? |
| Operations | Can your team compact files, expire snapshots, repair metadata and monitor compatibility across versions? |
A practical selection workflow
- Preserve source data where it has operational value. Retain raw objects when provenance, replay, legal retention or reprocessing matters. Record source schema and ingestion metadata rather than overwriting the only copy.
- Classify payloads by access pattern. Separate structured features and labels from text, images, audio and tensor payloads. Keep identifiers and searchable metadata in analytical tables, and test how the model loader accesses the payload itself.
- Create a Parquet and ORC candidate. Use the same representative rows, types, partitions and ordering for both. Include nested fields and nulls if they occur in production.
- Confirm implementation support. Check the exact versions and feature matrices of every writer and reader. Test logical types, encodings, codecs, schema evolution and predicate pushdown rather than assuming nominal format support is sufficient.
- Add a table layer only for a table-layer requirement. Choose Iceberg or Delta Lake when snapshots, transactions, concurrent changes, evolution or update/delete behavior justify the metadata and operational layer. Validate the selected feature against every engine version.
- Benchmark codecs and encodings together. At minimum, compare uncompressed, Snappy and Zstandard where supported. Include the encoding settings and compression levels you would deploy.
- Measure complete jobs. Run ingestion, representative analytical queries, feature generation, batch construction and model reads. Collect compressed bytes, write and read CPU, elapsed time, scan volume, predicate performance and samples per second delivered to the model.
- Tune layout after observing the workload. Adjust partitioning, row-group or stripe organization, ordering and file size for the storage and query system actually in use. Recheck performance after compaction and schema changes.
How to design a useful benchmark
Use representative data, not a toy sample
Include the real distribution of string cardinality, repeated categories, nulls, skew, numeric ranges, nested structures and typical row widths. Test both cold and warm reads if caching affects your production path. Include the largest and smallest common partitions so that small-file behavior is visible.
Test each stage that can become the bottleneck
- Ingest: source parsing, encoding, compression and object-store writes.
- Query: projection, filters, joins, aggregations and data skipping.
- Feature generation: repeated reads, shuffles and intermediate writes.
- Training input: decoding, batching, sharding, prefetch and delivery to accelerators.
- Maintenance: compaction, snapshot or log handling and schema changes.
Record results with their conditions
Store the engine, library and format versions, codec and level, encoding settings, file size, row-group or stripe settings, partition layout, concurrency and hardware alongside every result. A number without those conditions cannot safely guide another table or engine.
Compare not only the fastest run but also resource use and operational cost. A codec that saves storage yet starves model workers on CPU may be a poor choice; a larger file that scans efficiently may still be unsuitable if it delays incremental updates.
File sizing and layout
AWS guidance for Athena and Amazon Redshift Spectrum gives 128 MB as a recommended file size for that querying context. Treat 128 MB as a scoped service recommendation, not as a universal AI-data-lake or model-training target. Training readers, object-store behavior, partition counts and update frequency may justify a different size.
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Common failure modes and fixes
A reader opens the format but fails on a feature
Nominal Parquet or ORC support does not guarantee support for every logical type, encoding or codec. Reproduce the failure with a minimal file, identify the writer setting that created it and either upgrade the reader, change the setting or publish a compatibility profile that all jobs follow.
Compression saves storage but slows training
Measure CPU time and samples delivered to the model, not just object-store bytes. Lower the compression level, use a lower-overhead codec, alter encodings or cache a training-oriented representation when the model path is CPU-bound.
Queries scan too much data
Check whether projections and filters reach the reader, whether statistics are available, and whether partitioning or ordering matches real predicates. ORC’s documented min/max row-set indexes can help when the engine uses them; comparable gains should be demonstrated rather than assumed for every format.
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Metadata operations become the bottleneck
Investigate small files, excessive partitions, snapshot or transaction-log growth and overdue compaction. A table-management layer adds capabilities but also creates metadata and maintenance responsibilities.
Schema evolution breaks downstream jobs
Test additions, type changes, nullability changes, nested-field changes and timestamp handling in a staging table. Record the compatibility rules for every consumer and reject files that violate them before they reach shared training or serving paths.
One layout is forced onto every modality
Do not make images, audio or tensors imitate tabular rows solely for consistency. Keep queryable metadata in the analytical layer and benchmark the payload representation used by the model loader. Preserve source objects when you need replay or a new transformation later.
Quick Recap
A decision checklist
- Have you separated physical file format from table-management requirements?
- Can every production reader and writer handle the selected logical types, encodings and codecs?
- Did you test Parquet and ORC with the same representative data and access patterns?
- Did you measure ingestion, queries, feature generation and model-input throughput?
- Did you include CPU, latency, compressed bytes and scan volume rather than storage alone?
- Are raw source objects retained when provenance or replay requires them?
- Are metadata, labels and identifiers queryable even when payloads are binary or tensor-based?
- Is any 128 MB file-size target clearly limited to the AWS Athena/Spectrum guidance that recommends it?
- Have you documented engine versions and table-feature compatibility for Iceberg or Delta Lake?
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