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Start with Parquet for most structured analytical data, then validate the choice against your actual readers, writers, queries, and model-ingestion jobs. Parquet is columnar, supports several encodings and codecs, and is the physical file format used commonly with both Iceberg and Delta Lake. ORC can be the better fit when its projection, row-set indexes, and surrounding engine ecosystem match your workload. Iceberg and Delta Lake are table-management layers, not replacements for Parquet or ORC. Choose compression by measuring storage, CPU, latency, scan speed, and training-input throughput on representative data rather than adopting a universal “best” codec.
What you are choosing: file format, table format, and codec
An AI data lake usually has three separate decisions:
- File format: how columns, rows, pages, encodings, statistics, and types are stored inside each data file. Parquet and ORC are the main candidates covered here.
- Table format: how a collection of files is tracked, versioned, committed, and evolved. Apache Iceberg and Delta Lake provide this layer.
- Compression and encoding: how values are transformed and compressed inside files. Parquet, for example, supports multiple encodings and codecs.
Keeping these decisions separate prevents a common design error: treating “Iceberg versus Parquet” or “Delta versus ORC” as a direct format comparison. Iceberg’s specification supports Parquet, Avro, and ORC data files. Delta Lake documents versioned Parquet data files accompanied by a transaction log.
Which file format should you use?
Choose Parquet as the general starting point
Parquet is a columnar on-disk format designed for analytical storage. A query or feature-building job that needs only a subset of columns can avoid reading unrelated columns, reducing I/O compared with a row-oriented layout. Parquet’s specification includes dictionary encoding, run-length and bit-packing encodings, delta encodings, and byte-stream split.
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Byte-stream split deserves careful interpretation: it is a data transform, not compression by itself. The Parquet specification describes it as potentially improving compression ratio and speed when a compression algorithm is applied afterward. Whether it helps depends on the data types, value patterns, writer settings, and reader support.
Parquet is therefore a defensible default for structured features, labels, event records, embeddings metadata, and other tabular data when your query and machine-learning engines support the exact features you plan to write. Confirm implementation details rather than assuming that every Parquet reader accepts every encoding, logical type, or codec.
Choose ORC when its ecosystem and skipping behavior fit
ORC is also columnar and type-aware. Its specification describes dictionary encoding, bit packing, delta and run-length encoding, general 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.
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Do not use Arrow as your durable lake format
Apache Arrow is primarily an in-memory columnar representation used 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 layer between a data reader and a training or analytics process, while Parquet or ORC remains the durable object-storage representation.
How Iceberg and Delta Lake fit
Iceberg: metadata, manifests, and snapshots around files
Iceberg tracks individual data files through table metadata, manifests, and snapshots. Its specification names Parquet, Avro, and ORC as supported data-file formats. This makes Iceberg useful when you need table-level snapshots, concurrent changes, schema evolution, or reliable discovery of which files belong to a table.
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Iceberg capabilities are versioned. A feature that works in one engine or table-specification version may not work in another. Before enabling a feature, verify the Iceberg version and feature support in every writer, reader, catalog, and query engine that will touch the table.
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Delta Lake: a transaction log with versioned Parquet files
Delta Lake documents a transaction log alongside versioned Parquet data files. The combination provides table history and ACID transaction behavior, with Spark API compatibility documented by the project. Delta can be appropriate when atomic commits, concurrent writes, updates, deletes, and time-travel-style version management are central requirements.
Delta’s table behavior does not eliminate the need to choose file-level compression, encoding, and layout. You still need to test the Parquet files produced by your writers and consumed by your query and model-loading engines.
When adding a table layer is justified
Add Iceberg or Delta when the operational requirements exceed a plain directory of files:
- Snapshot isolation or reproducible table versions.
- Concurrent writers that need coordinated commits.
- Schema evolution managed at table level.
- Update and delete operations rather than append-only ingestion.
- Streaming or incremental pipelines that need transactional visibility.
If none of those requirements exists, a table-management layer may add operational complexity without solving a current problem. The choice should follow lifecycle and concurrency needs, not branding.
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Use the following axes before selecting a format. They are decision criteria, not published benchmark results.
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| Decision axis | Questions to answer |
|---|---|
| Interoperability | Can every ingestion, query, feature-engineering, and training library read and write the required format, logical types, encodings, and codecs? |
| Read pattern | Do operations read a few columns, most columns, complete rows, or large binary payloads? |
| Throughput | What are sustained write speed, read speed, scan latency, and model-input delivery rate on your hardware and object store? |
| Compression | How many bytes are saved, and how much additional CPU and wall-clock time does compression or decompression consume? |
| Data skipping | Can predicates use statistics, row-group or row-set indexes, and partition layout to avoid unnecessary reads? |
| Schema needs | Do you require nested types, nullable fields, high-cardinality strings, decimal precision, or frequent schema changes? |
| Table behavior | Do snapshots, transactions, updates, deletes, and streaming commits justify Iceberg or Delta? |
| Operations | Which format minimizes monitoring, compaction, compatibility, and recovery work for your team? |
Which compression codec should you choose?
Understand the available Parquet codecs
Parquet lists uncompressed pages, Snappy, GZIP, LZO, Brotli, Zstandard, and LZ4_RAW. These choices occupy different points on the compression-ratio versus processing-cost spectrum. A codec that produces smaller objects can still be a poor choice if its CPU cost slows ingestion, interactive scans, or model training.
Parquet also documents an older LZ4 codec framing scheme. For interoperability, the specification recommends that writers move toward LZ4_RAW. Confirm that every reader in your chain supports the framing you select; a codec label alone does not guarantee compatibility.
A practical shortlist: Snappy and Zstandard
Snappy is a practical candidate when low processing overhead and broad ecosystem support are priorities. It is often a reasonable first test for transformed and curated analytical layers, but support and performance still need verification in your specific engines.
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Zstandard is a practical candidate when reducing storage and object-store I/O may justify additional codec CPU. The useful setting is workload-specific: compression level, data characteristics, processor capacity, and concurrent jobs all affect the result.
This shortlist is an engineering starting point, not a universal performance claim. The available material contains no controlled, current, multi-engine benchmark that proves one codec wins for AI training data.
Encoding settings are part of the compression decision
Do not compare codec names while ignoring encodings. Dictionary, run-length, delta, bit-packing, and byte-stream-split transforms alter the data presented to the general-purpose compressor. For example, byte-stream split may improve later compression for suitable numeric values, but it does not reduce size on its own.
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Record the writer version and encoding settings in your benchmark. A reader must support both the codec and the encodings used in the file.
AI data is more than a feature table
Structured features and labels
Tabular features, labels, timestamps, identifiers, and training metadata are strong candidates for Parquet or ORC. Column projection, predicate pushdown, statistics, and compression can reduce the amount of data moved into feature-building and training jobs.
Text, images, audio, and tensors
Large modality payloads have different access patterns. A training loop may need complete objects, sequential shards, random samples, or pre-batched tensors rather than a handful of scalar columns. The reviewed specifications establish general columnar analytics principles, not a universal durable representation for image, audio, video, text, or dense tensor data.
A common architecture is to keep stable metadata, labels, partitions, and object references queryable in Parquet or ORC while retaining source payloads in a representation suited to the model loader. Preserve the source objects when provenance, replay, or reprocessing matters. Then benchmark the transformed representation against the actual loader instead of assuming that a columnar table is automatically the fastest training input.
A step-by-step selection and benchmarking workflow
- Define the access contract. List ingestion jobs, interactive queries, feature generation, batch training, streaming training, backfills, updates, deletes, and recovery operations. For each, record columns or payloads read, expected concurrency, latency target, and throughput target.
- Preserve raw source data where required. A raw layer can retain source-defined formats for provenance and replay. Transformed and curated layers can use analytics-oriented formats. This separation follows the architecture guidance documented by Amazon Web Services; it is not a requirement to rewrite every source object immediately.
- Separate metadata from modality payloads. Keep labels, timestamps, partition keys, schema fields, and object references in a queryable table. Test how the model loader retrieves and batches the underlying text, image, audio, or tensor data.
- Select candidate file formats. Start with Parquet for broad analytical interoperability. Include ORC when its readers, projection behavior, and row-set indexes fit your engine chain. Confirm nested-type, logical-type, statistics, and schema-evolution support in the exact libraries you deploy.
- Select the table-management layer. Add Iceberg or Delta when snapshots, transactions, concurrent changes, schema evolution, updates, deletes, or streaming commits justify it. Check the table-feature version implemented by each engine before relying on a feature.
- Build representative test data. Include realistic row counts, null rates, string cardinality, numeric distributions, nested values, skew, partition sizes, and the file sizes you expect in production. Include both ordinary and worst-case batches.
- Benchmark codecs and encodings. At minimum, test uncompressed output, Snappy, and Zstandard where your readers support them. Test relevant encoding settings, including dictionary behavior and byte-stream split for applicable numeric columns.
- Measure the complete path. Record compressed bytes, write CPU time, write wall-clock time, read CPU time, scan latency, predicate-filtered latency, decompression throughput, object-store bytes read, and model-input throughput. Measure cold and warm runs separately when caching can affect results.
- Test every engine in the chain. A file that one writer produces successfully may fail in a different reader because of unsupported codecs, encodings, logical types, or table features. Include ingestion, query, feature-engineering, and training libraries in compatibility tests.
- Tune layout and file size. Adjust partitioning, row-group or stripe sizing, clustering, statistics, and compaction for the actual storage and query system. Avoid creating huge numbers of tiny files, but do not assume one target size works for every engine.
- Document the decision. Record format, table format and version, codec, encoding settings, writer and reader versions, file-size policy, benchmark data characteristics, and rollback procedure. Re-run the test when upgrading a library or enabling a new table feature.
File sizing and layout: use service guidance carefully
Amazon Web Services guidance for Athena and Spectrum gives 128 MB as a recommended file size for querying. That figure is scoped to those query services; it is not a general AI data-lake or model-training optimum. Training throughput, object-store behavior, scheduler overhead, partition cardinality, and reader parallelism may lead to a different target.
Use file-size tests to balance parallelism against metadata and request overhead. Validate the result with your own query scans and model-input jobs, and revisit it after changing partition keys, table format, engine versions, or workload shape.
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Common failure modes and fixes
“The table format will solve file performance.”
Iceberg or Delta coordinates table state; it does not automatically choose an efficient codec, encoding, partition layout, or training representation. Fix this by benchmarking file-level settings and table-level behavior separately.
“The smallest files are always best.”
Many tiny files increase metadata operations, scheduling overhead, and connection or request costs. Compact files and measure scan and training throughput. The correct size depends on the engine and workload.
“Compression savings equal performance savings.”
Smaller objects can reduce I/O while increasing CPU time. Compare bytes, CPU, latency, and end-to-end throughput together; never select a codec from storage ratio alone.
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Implementations differ by library and version. A reader may support the format but not a particular codec, encoding, logical type, or table feature. Pin versions and run compatibility tests with representative files.
“A query benchmark predicts training performance.”
Analytical queries may project a few columns and apply selective predicates, while a model loader may read complete records or large payloads sequentially. Benchmark the actual training path, including batching, shuffling, prefetching, decompression, and object retrieval.
Decision checklist
- Are you choosing a physical file format, a table-management layer, or both?
- Which columns or payloads does each operation actually read?
- Do all deployed readers and writers support the required format features?
- Have you tested Parquet and ORC on realistic data rather than synthetic rows only?
- Have you tested Snappy and Zstandard, plus relevant encodings?
- Did you measure CPU, latency, compressed bytes, scan performance, and model-input throughput?
- Do snapshots, transactions, updates, deletes, or streaming justify Iceberg or Delta?
- Are raw source objects retained where provenance or replay requires them?
- Is your file-size target scoped to the actual query and training engines?
- Is the selected configuration documented with versions and a rollback plan?
Bottom line
Use Parquet as the first candidate for structured analytical data, evaluate ORC when its ecosystem and row-set skipping match your workload, and treat Iceberg or Delta as table-management choices layered around those files. Start codec testing with Snappy and Zstandard where supported, include encoding settings in the test, and choose the winner from end-to-end measurements of your ingestion, queries, and model loader. No available evidence supports a single format or codec as universally best for every AI data lake.
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