🌊 LakeBench

LakeBench is the first Python-based, multi-modal benchmarking framework designed to evaluate performance across multiple lakehouse compute engines and ELT scenarios. Supporting a variety of engines and both industry-standard and novel benchmarks, LakeBench enables comprehensive, apples-to-apples comparisons in a single, extensible Python library.

🚀 The Mission of LakeBench

LakeBench exists to bring clarity, trust, accessibility, and relevance to engine benchmarking by focusing on four core pillars:

1. End-to-End ELT Workflows Matter

   Most benchmarks focus solely on analytic queries. But in practice, data engineers manage full data pipelines — loading data, transforming it (in batch, incrementally, or even streaming), maintaining tables, and then querying.

   LakeBench proposes that the entire end-to-end data lifecycle managed by data engineers is relevant, not just queries.

2. Variety in Benchmarks Is Essential

   Real-world pipelines deal with with different data shapes, sizes, and patterns. One-size-fits-all benchmarks miss this nuance.

   LakeBench covers a variety of benchmarks that represent diverse workloads — from bulk loads to incremental merges to maintenance jobs to ad-hoc queries — providing a richer picture of engine behavior under different conditions.

3. Consistency Enables Trustworthy Comparisons

   Somehow, every engine claims to be the fastest at the same benchmark, at the same time. Without a standardized framework, with support for many engines, comparisons are hard to trust and even more difficult to reproduce.

   LakeBench ensures consistent methodology across engines, reducing the likelihood of implementation bias and enabling repeatable, trustworthy results. Engine subject matter experts are encouraged to submit PRs to tune code as needed so that their preferred engine is best represented.

4. Accessibility starts with pip install

   Most benchmarking toolkits are highly inaccessible to the beginner data engineer, requiring the user to build the package or installation via a JAR, absent of Python bindings.

   LakeBench is intentionally built as a Python-native library, installable via pip from PyPi, so it's easy for any engineer to get started—no JVM or compilation required. It's so lightweight and approachable, you could even use it just for generating high-quality sample data.

✅ Why LakeBench?

• Multi-Engine: Benchmark Spark, DuckDB, Polars, Daft, Sail and others, side-by-side
• Lifecycle Coverage: Ingest, transform, maintain, and query—just like real workloads
• Diverse Workloads: Test performance across varied data shapes and operations
• Consistent Execution: One framework, many engines
• Extensible by Design: Add engines or additional benchmarks with minimal friction
• Dataset Generation: Out-of-the box dataset generation for all benchmarks
• Rich Logs: Automatically logged engine version, compute size, duration, estimated execution cost, etc.

LakeBench empowers data teams to make informed engine decisions based on real workloads, not just marketing claims.

💪 Benchmarks

LakeBench currently supports four benchmarks with more to come:

• ELTBench: An benchmark that simulates typicaly ELT workloads:
  • Raw data load (Parquet → Delta)
  • Fact table generation
  • Incremental merge processing
  • Table maintenance (e.g. OPTIMIZE/VACUUM)
  • Ad-hoc analytical queries
• TPC-DS: An industry-standard benchmark for complex analytical queries, featuring 24 source tables and 99 queries. Designed to simulate decision support systems and analytics workloads.
• TPC-H: Focuses on ad-hoc decision support with 8 tables and 22 queries, evaluating performance on business-oriented analytical workloads.
• ClickBench: A benchmark that simulates ad-hoc analytical and real-time queries on clickstream, traffic analysis, web analytics, machine-generated data, structured logs, and events data. The load phase (single flat table) is followed by 43 queries.

Planned

• TPC-DI: An industry-standard benchmark for data integration workloads, evaluating end-to-end ETL/ELT performance across heterogeneous sources—including data ingestion, transformation, and loading processes.

⚙️ Engine Support Matrix

LakeBench supports multiple lakehouse compute engines. Each benchmark scenario declares which engines it supports via <BenchmarkClassName>.BENCHMARK_IMPL_REGISTRY.

Legend:

• ✅ = Supported
• ⚠️ = Some queries fail due to syntax issues (i.e. Polars doesn't support SQL non-equi joins, Daft is missing a lot of standard SQL contructs, i.e. DATE_ADD, CROSS JOIN, Subqueries, non-equi joins, CASE with operand, etc.).
• ❌ = Not supported
• 🔜 = Coming Soon
• (Blank) = Not currently supported

Daft is unsupported as of LakeBench 2.0.0. Daft cannot read Parquet whose embedded Arrow schema uses Utf8View, and the bundled TPC generators emit that type, so every TPC-H, TPC-DS, and ELTBench table fails to load with DaftError::ValueError Unsupported Arrow DataType: Utf8View. The generators deliberately keep the type rather than rewriting output for one engine. The engine, its daft extra, and its integration tests all remain in the repository so support can be restored as soon as Daft handles Utf8View — only ClickBench, which loads from a plain Parquet sample, still runs. See reports/coverage/daft.md.

For detailed pass rates and per-query failure analysis, see the coverage reports.

📊 Engine Coverage Reports

Per-engine coverage reports are auto-generated by the integration test suite and show pass rates with individual query failure details. To refresh: run the integration tests for your engine of choice (see tests/integration/README.md).

Where Can I Run LakeBench?

Multiple modalities doesn't end at just benchmarks and engines, LakeBench also supports different runtimes and storage backends:

Runtimes:

• Local (Windows)
• Fabric
• Synapse
• HDInsight
• Google Colab ⚠️

Storage Systems:

• Local filesystem (Windows)
• OneLake
• ADLS gen2 (temporarily only in Fabric, Synapse, and HDInsight)
• S3 ⚠️
• GS ⚠️

* ⚠️ denotes experimental storage backends

What Table Formats Are Supported?

LakeBench currently only supports Delta Lake.

🔌 Extensibility by Design

LakeBench is designed to be extensible, both for additional engines and benchmarks.

• You can register new engines without modifying core benchmark logic.
• You can add new benchmarks that reuse existing engines and shared engine methods.
• LakeBench extension libraries can be created to extend core LakeBench capabilities with additional custom benchmarks and engines (i.e. MyCustomSynapseSpark(Spark), MyOrgsELT(BaseBenchmark)).

New engines can be added via subclassing an existing engine class. Existing benchmarks can then register support for additional engines via the below:

from lakebench.benchmarks import TPCDS
TPCDS.register_engine(MyNewEngine, None)

register_engine is a class method to update <BenchmarkClassName>.BENCHMARK_IMPL_REGISTRY. It requires two inputs, the engine class that is being registered and the engine specific benchmark implementation class if required (otherwise specifying None will leverage methods in the generic engine class).

This architecture encourages experimentation, benchmarking innovation, and easy adaptation.

Example:

from lakebench.engines import BaseEngine

class MyCustomEngine(BaseEngine):
    ...

from lakebench.benchmarks.elt_bench import ELTBench
# registering the engine is only required if you aren't subclassing an existing registered engine
ELTBench.register_engine(MyCustomEngine, None)

benchmark = ELTBench(engine=MyCustomEngine(...))
benchmark.run()

An engine that depends on packages outside the LakeBench core should declare them so construction fails with an actionable message rather than deep inside a run. Name the top-level modules the engine imports, not the distributions that provide them:

class MyCustomEngine(BaseEngine):
    REQUIRED_MODULES = ("mylib", "deltalake", "pyarrow")
    INSTALL_EXTRA = "mycustom"

INSTALL_EXTRA is optional; without it the hint falls back to pip install <modules>. Override the verify_dependencies() classmethod to add checks pip cannot express, such as a system driver.

Using LakeBench

📦 Installation

Install from PyPi:

pip install lakebench[duckdb,polars,fabric_data_warehouse,sail,sparkmeasure]

Engines import their heavy dependencies lazily, so a forgotten extra used to surface partway into a run as an error that named neither the engine nor the package to install. Every engine now checks its dependencies when it is constructed and reports all of the missing ones at once:

>>> from lakebench.engines import DuckDB
>>> DuckDB(schema_or_working_directory_uri="file:///tmp/lakebench")
lakebench.engines.base.MissingDependenciesError: DuckDB requires `duckdb`, which is not
installed. Install with `pip install lakebench[duckdb]`.

MissingDependenciesError subclasses ImportError, so existing except ImportError handling keeps working. Availability is resolved through the import system rather than through installed distribution names, so modules supplied by a managed runtime — such as pyspark on Fabric, Synapse, or HDInsight — count as present and do not trigger a false failure. FabricDataWarehouse additionally checks for the Microsoft ODBC Driver 18 for SQL Server, which pip cannot install.

Example Usage

To run any LakeBench benchmark, first do a one time generation of the data required for the benchmark and scale of interest. LakeBench provides datagen classes to quickly generate parquet datasets required by the benchmarks.

Data Generation

• TPC-H and TPC-DS data generation is blazing fast via a pinned build of the unified Rust tpcgen-cli from the tpcgen-rs project. The temporary Windows x86_64 and manylinux 2.17 x86_64 executables are committed under native/tpcgen. LakeBench will migrate to the official tpcgen-cli Python package after it is released on PyPI.

  The below are generation runtimes on a 64 v-core VM writing to OneLake. Scale factors below 1000 can easily be generated on a 2 v-core machine.

  Scale Factor	TPC-H Duration (hh:mm:ss)	TPC-DS Duration (hh:mm:ss)
  1	00:00:20	00:00:24
  10	00:00:34	00:01:01
  100	00:01:26	00:02:17
  1000	00:07:49	00:10:30

• ClickBench data is downloaded directly from the Clickhouse host site.

TPC-H Data Generation

from lakebench.datagen import TPCHDataGenerator

datagen = TPCHDataGenerator(
    scale_factor=1,
    target_folder_uri='/lakehouse/default/Files/tpch_sf1'
)
datagen.run()

TPC-DS Data Generation

from lakebench.datagen import TPCDSDataGenerator

datagen = TPCDSDataGenerator(
    scale_factor=1,
    target_folder_uri='/lakehouse/default/Files/tpcds_sf1'
)
datagen.run()

Notes:

• By default, each table is split automatically using its estimated total compressed size: 128 MiB files below 10 GiB, 256 MiB below 1 TiB, 512 MiB below 5 TiB, and 1 GiB for larger tables. Estimated physical size is calculated directly from each table's SF1000 baseline for any supported scale factor; part counts are always selected automatically.
• target_row_group_size_mb is an on-disk compressed-size target. LakeBench converts it to the uncompressed-byte value expected by tpcgen-cli using benchmark- and table-specific ZSTD(1) or Snappy compression ratios measured from SF10 output. All ZSTD(N) levels use the ZSTD(1) measurements for planning while the requested compression level is passed through unchanged. Automatic part counts are adjusted for the selected codec. The row-group conversion includes a 5% planning margin for upstream's estimated bytes-per-source-row model. Other compressed codecs require an explicit compression_factor, which is used for both row groups and file estimates. TPC-DS generation uses the upstream C-reference compatibility mode.
• Output remains organized as <root>/<table>/*.parquet. Filenames include the one-based part number and codec for quick inspection, for example lineitem/lineitem-00001.zstd.parquet or store_sales/store_sales-00001.zstd.parquet.
• To use the legacy implementation, install lakebench[tpcds_duckdb_datagen] on Python 3.10+ and pass backend="duckdb".- Editable/source installations use the matching vendored binary from
• output_format="native" emits the TPC generators' pipe-delimited text instead of Parquet, matching what the official dsdgen/dbgen tools produce. TPC-DS native format support is currently in preview. See Native TPC Generator Format below.- Editable/source installations use the matching vendored binary from native/tpcgen; installed wheels always use their packaged binary.
• Large generations targeting mounted filesystems can set num_threads=8 or num_threads=16 to limit concurrent file creation and atomic renames. The default remains all available CPU cores.
• The ClickBench dataset (only 1 size) should download with partitioned files in ~ 1 minute and ~ 6 minutes as a single file.

Native TPC Generator Format (.dat / .tbl)

TPC-H and TPC-DS can be generated and loaded in the TPC tools' native pipe-delimited text format instead of Parquet. TPC-DS native format support is currently in preview. This measures the load phase against the same raw format the official dsdgen and dbgen tools emit, rather than a pre-typed columnar file.

from lakebench.datagen import TPCHDataGenerator
from lakebench.benchmarks import TPCH
from lakebench.engines import Polars

TPCHDataGenerator(
    scale_factor=1,
    target_folder_uri='/lakehouse/default/Files/tpch_sf1_native',
    output_format="native",      # .tbl for TPC-H, .dat for TPC-DS
).run()

benchmark = TPCH(
    engine=Polars(schema_or_working_directory_uri='...'),
    scenario_name='native-load',
    scale_factor=1,
    input_folder_uri='/lakehouse/default/Files/tpch_sf1_native',
    input_format="native",
)
benchmark.run(mode='load_and_query')

Notes:

• Files are named exactly as the official tools name them, one folder per table. A single-part table uses the serial name <table>.tbl / <table>.dat. A multi-part table uses the parallel names: dbgen's <table>.tbl.<step> for TPC-H and dsdgen's <table>_<child>_<parallel>.dat for TPC-DS.
• Neither dsdgen nor dbgen splits output on its own; parts exist only because the operator runs the tool once per chunk, which is the normal way to generate large scale factors. LakeBench picks the part count for you from the estimated table size, so bigger scale factors naturally produce more parts, matching what a parallel dsdgen/dbgen run would leave on disk.
• The format carries no header and no types, so LakeBench derives each reader's schema from the benchmark's resolved DDL. As a result, native loads are always typed exactly as the DDL declares. The generator's Parquet output does not always agree with the DDL on integer width (for example TPC-H n_nationkey is int64 in Parquet but integer in the DDL), so on engines that do not pre-create tables the two formats can differ in integer width. Values are identical.
• Every generated line ends with a trailing delimiter. LakeBench reads one extra trailing column and drops it, so no reader needs a lenient mode.
• Empty fields are read as NULL, and quoting is disabled so " is treated as ordinary data.
• Parquet-only options (target_row_group_size_mb, compression, and compression_factor) are rejected with output_format="native". Automatic part counts use per-table native-to-uncompressed-Parquet size ratios measured at SF1.
• Supported on the DuckDB, Polars, Daft, Sail, and Spark engines. Engines with a benchmark-specific Parquet loader (such as Fabric Data Warehouse) reject input_format="native" rather than silently loading Parquet.
• output_format="native" requires backend="rust". With the currently bundled generator, tpcgen-cli tpcds dat has no thread option, so TPC-DS native generation runs single-threaded regardless of the available core count and rejects num_threads. Part count controls output splitting but does not make the current TPC-DS native run parallel. tpcgen-cli tpch tbl does accept --num-threads, so TPC-H native honours it exactly like Parquet (defaulting to all available CPU cores).

Naming the Input Location

Every benchmark accepts the input location as either input_folder_uri or input_parquet_folder_uri. They are aliases for the same value; passing both with different values raises. input_folder_uri is preferred because the input is not necessarily Parquet, but the original name remains fully supported.

Is BYO Data Supported?If you want to use your own TPC-DS, TPC-H, or ClickBench Parquet datasets, that is fine and encouraged as long as they are to specification. LakeBench keeps the canonical TPC-DS schema as its table and query contract, but automatically corrects these recognized legacy input names while loading Parquet:

Canonical names are accepted unchanged. Input containing both names, or neither required name, is rejected as ambiguous or invalid. Loaded Delta tables always use the canonical name.

Load-Time Statistics

TPC-H and TPC-DS benchmarks can include statistics generation in the measured load phase:

benchmark = TPCH(
    engine=engine,
    scenario_name="sf10",
    input_folder_uri="abfss://...",
    analyze="selective",
)

The analyze option supports:

• "none" (default): Do not generate statistics during load.
• "full": Ask the engine to generate statistics for every table column.
• "selective": Generate statistics only for the benchmark-maintained columns used by the workload.

For backward compatibility, analyze=True is equivalent to "full" and analyze=False is equivalent to "none".

Fabric Spark separately enables Delta extended statistics during writes by default. Set collect_stats_on_write=False only when isolating explicit analyze costs:

engine = FabricSpark(
    lakehouse_name="lakehouse",
    lakehouse_schema_name="schema",
    collect_stats_on_write=False,
)

Fabric Spark

from lakebench.engines import FabricSpark
from lakebench.benchmarks import ELTBench

engine = FabricSpark(
    lakehouse_workspace_name="workspace",
    lakehouse_name="lakehouse",
    lakehouse_schema_name="schema",
    spark_measure_telemetry=True
)

benchmark = ELTBench(
    engine=engine,
    scenario_name="sf10",
    mode="light",
    input_folder_uri="abfss://...",
    save_results=True,
    result_table_uri="abfss://..."
)

benchmark.run()

Note: The spark_measure_telemetry flag can be enabled to capture stage metrics in the results. The sparkmeasure install option must be used when spark_measure_telemetry is enabled (%pip install lakebench[sparkmeasure]). Additionally, the Spark-Measure JAR must be installed from Maven: https://mvnrepository.com/artifact/ch.cern.sparkmeasure/spark-measure_2.13/0.24

Fabric Data Warehouse

from lakebench.engines import FabricDataWarehouse
from lakebench.benchmarks import TPCDS

engine = FabricDataWarehouse(
    warehouse_name="warehouse",
    warehouse_server="xxxxx.datawarehouse.fabric.microsoft.com",
    schema_name="dbo",
)

benchmark = TPCDS(
    engine=engine,
    scenario_name="sf1000",
    scale_factor=1000,
    input_folder_uri="abfss://...",
    save_results=True,
    result_table_uri="abfss://..."
)

benchmark.run()

Note: use Python 3.10+ and install with pip install lakebench[fabric_data_warehouse]. Make sure Microsoft ODBC Driver 18 for SQL Server is available on the host. The engine authenticates with the notebook identity's Fabric token, so it must run inside a Fabric notebook.

Delta (deltalake==1.5.1) and PyArrow are used only for result-log appends. The extra relies on the core pyarrow>=14.0.0 floor rather than forcing an upgrade of Fabric Runtime 1.3's Arrow installation, and can share Daft's Delta 1.5.x dependency. SQL reads require pandas 1.4+ with SQLAlchemy 2; pandas 2 is not required.

Polars

from lakebench.engines import Polars
from lakebench.benchmarks import ELTBench

engine = Polars( 
    schema_or_working_directory_uri = 'abfss://...'
)

benchmark = ELTBench(
    engine=engine,
    scenario_name="sf10",
    mode="light",
    input_folder_uri="abfss://...",
    save_results=True,
    result_table_uri="abfss://..."
)

benchmark.run()

Managing Queries Over Various Dialects

LakeBench uses SQLGlot to translate benchmark queries to each engine's dialect. Every benchmark starts from an immutable upstream SQL source; compatibility changes are registered AST rules, not alternate SQL files.

Each benchmark's full rule inventory — what each rule does, why it exists, and what it does and does not change — is documented on its own page:

See docs/query-normalization/ for the shared pipeline, the rule contract, and guidance on adding a rule.

Query Resolution Strategy

Runtime compilation proceeds in this order for every benchmark:

1. Load the canonical source and parse it with the benchmark's CANONICAL_QUERY_DIALECT.
2. Apply SOURCE_NORMALIZERS to parsed statement bundles, for lowerings that must happen before the bundle is reduced to a single query.
3. Apply QUERY_NORMALIZERS: shared "*" rules first, then rules for the query ID.
4. Apply ENGINE_QUERY_NORMALIZERS registered for the engine class and its ancestors, base classes first. Each class uses the same "*"-then-query convention.
5. Qualify catalog/schema references and render the AST directly to the engine's SQLGLOT_DIALECT, without an intermediate SQL serialization.

Join normalization is not registered by default for any benchmark; WHERE join predicates remain in place even when SQLGlot renders comma joins as CROSS JOIN. The shared join-normalization function remains available for explicit registration.

Static TPC query sets currently cover SF1000 and SF10000. Other data scales log a warning and use SF1000 query substitutions; result metadata records the mismatch via query_set_scale_matches_data.

Breaking change in v2: engine, parent-engine, and third-party SQL-file query overrides are no longer searched for any benchmark, including ClickBench. Existing overrides must be migrated to registered structural rules. Engine-specific DDL resolution is unchanged.

SQLGlot Upgrade Guardrails

SQLGlot is pinned to 30.18.0 on Python 3.9+. Python 3.8 retains 26.30.0 because newer SQLGlot releases require Python 3.9+. The AST adapters support both versions, including FROM/WITH argument names, DROP VIEW target lists, and GROUPING function nodes.

tests/test_tpc_sqlglot_compatibility.py checks reviewed output fingerprints in tests/fixtures/tpc_query_rendering.json for all 500 TPC-H/TPC-DS renderings (both static scales, Spark and Fabric, on both pinned SQLGlot versions). Each dialect is rendered through the engine that emits it, so engine-registered rules are part of what the fingerprints pin. Review actual SQL differences before refreshing these fingerprints; do not regenerate them just to clear a failure. The 26.30.0-to-30.18.0 comparison found 472 identical outputs and 28 differences limited to equivalent NOT LIKE spelling and generated subquery alias names. Generated source hashes remain independently checked against their manifests.

The upgrade does not make the existing compatibility rules redundant. The built-in Fabric dialect is a suitable Warehouse target, but does not replace them — see the per-benchmark pages linked above for what each rule still covers.

Case-sensitive binding checks intentionally bypass identifier normalization so they catch mismatches like SR_FEE versus the declared sr_fee column. Successful grammar parsing alone does not establish Warehouse execution support.

Registering Compatibility Rules

Registries live in each benchmark's _query_normalizers.py and are bound on the benchmark class. AST rules accept (expression, context), modify the supplied copied AST, and return None. Source rules instead receive a mutable list of parsed statements; after source lowering, exactly one query must remain. Rules must validate expected shapes and raise explicitly rather than substitute another query.

context.dialect is the source parser dialect; context.target_dialect is the engine's output dialect. Prefer registering an engine-specific accommodation in ENGINE_QUERY_NORMALIZERS keyed by the engine class rather than gating a global rule on context.target_dialect: a dialect is a rendering target that several engines can share, so a dialect gate applies the fix to engines that never asked for it. Reserve context.target_dialect for rules that are genuinely a property of the renderer family rather than of one engine. The runtime supplies it to both source and query rules; direct callers of apply_query_normalizers can pass target_dialect, engine_type, and engine_normalizers explicitly.

For example, an engine integration can extend the TPC-H engine registry:

TPCH.ENGINE_QUERY_NORMALIZERS = {
    **TPCH.ENGINE_QUERY_NORMALIZERS,
    MyEngine: {"q14": (normalize_q14_for_my_engine,)},
}

Preserve generated literals and make AST rules idempotent. Current engine accommodations include Daft DOUBLE arithmetic casts in TPC-H q1/q8/q9/q14, Sail's NULLIF denominator in TPC-DS q12, and the Fabric Data Warehouse T-SQL accommodations for TPC-DS q17/q22/q29/q35/q39 and ClickBench grouping, aggregate widening, and q29 host extraction. These are semantic accommodations (numeric precision and division-by-zero behavior), not merely syntax fixes. Applied rule identifiers are recorded per query in execution_telemetry["query_normalization_rules"], alongside the benchmark's normalizer version in engine metadata. Successful transpilation alone does not establish specification equivalence or engine execution support.

Full guidance on writing and registering a rule — including bumping NORMALIZER_VERSION and refreshing rendering fingerprints — is in docs/query-normalization/.

Viewing Generated Queries

To inspect the final query that will be executed for any engine:

benchmark = TPCH(engine=MyEngine(...))
query_str = benchmark._return_query_definition('q14')
print(query_str)  # Shows final transpiled/customized query

All engines now receive the same selected TPC source substitutions, with compatibility changes explicit and reviewable.

📬 Feedback / Contributions

Got ideas? Found a bug? Want to contribute a benchmark or engine wrapper? PRs and issues are welcome!

Licensing and Third-Party Material

LakeBench is released under the MIT License (see LICENSE). It also redistributes material from third-party projects that remain under their own licenses and are not covered by MIT. These are itemized, with attribution and a description of modifications, in THIRD-PARTY-NOTICES.md.

Most notably:

• ClickBench queries and schema come from ClickHouse/ClickBench (Alexey Milovidov and the ClickHouse team, 2022), which is published under CC BY-NC-SA 4.0. The queries are vendored verbatim from a pinned upstream commit; provenance and hashes are recorded in PROVENANCE.md and source_manifest.json alongside them. If the NonCommercial or ShareAlike terms matter for your use, review them before redistributing LakeBench or building on it.
• tpcgen-rs is bundled as a prebuilt binary in platform wheels under Apache 2.0.
• TPC-H / TPC-DS tools kits, templates, and generator executables are not redistributed; only generated query text and a reproducibility manifest are checked in. TPC-H and TPC-DS are trademarks of the Transaction Processing Performance Council, and LakeBench results are not audited, endorsed, or comparable to published TPC results.

Acknowledgement of Other LakeBench Projects

The LakeBench name is also used by two unrelated academic and research efforts:

• RLGen/LAKEBENCH: A benchmark designed for evaluating vision-language models on multimodal tasks.
• LakeBench: Benchmarks for Data Discovery over Lakes (paper link): A benchmark suite focused on improving data discovery and exploration over large data lakes.

While these projects target very different problem domains — such as machine learning and data discovery — they coincidentally share the same name. This project, focused on ELT benchmarking across lakehouse engines, is not affiliated with or derived from either.