Columnar (Parquet/ORC/Feather), row (Avro), and messaging (MessagePack/CBOR/Protobuf) formats plus star-schema and log data — for testing ETL, data-lake ingestion, and warehouse loaders.
A small employee table as Apache Parquet — the columnar format at the heart of modern data lakes and analytics. For testing Parquet readers (pandas, Spark, DuckDB) and conversion.
The same employee table as Apache ORC — the columnar format common in the Hive/Hadoop ecosystem. For testing ORC readers and Parquet↔ORC conversion.
The same table as Feather (Arrow IPC file) — the zero-copy on-disk form of an Apache Arrow table. For testing Arrow readers and fast columnar interchange.
The same records as Apache Avro — a compact row-based binary format that embeds its own schema, widely used in Kafka pipelines. For testing Avro decoders and schema evolution.
A small HDF5 file with a compound 'employees' dataset and a numeric 'readings' grid — the hierarchical format used across science and ML. For testing h5py/HDF5 readers and conversion.
A realistic e-commerce product catalogue (200 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
The e-commerce products table as Apache Parquet — the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
A realistic e-commerce customer directory (500 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
A realistic e-commerce order lines (customer_id → customers, product_id → products) (2000 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
The e-commerce orders table as Apache Parquet — the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
The date dimension of a star schema — one row per day with a YYYYMMDD surrogate key and calendar attributes (year, quarter, month, weekday). Joins to the sales fact table on date_key.
The product dimension of a star schema — surrogate key, SKU, name, category, and unit price. Joins to the sales fact table on product_key.
The customer dimension of a star schema — surrogate key, name, city, country, and segment. Joins to the sales fact table on customer_key. Names are synthetic.
The sales fact table at the centre of a star schema — 300 sale lines with foreign keys to the date, product, and customer dimensions plus quantity and amount measures. A fixture for testing joins, star-schema imports, and BI tools.
Binary orc source for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-source.
Json semantic reference for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-json.
Csv semantic reference for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-csv.
The same 16-dimensional embeddings as Apache Parquet — id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.
Machine-readable insertion and deletion counts per file. Two rows break naive arithmetic: the binary file reports `-` in both numeric columns rather than 0, and the rename path is brace-compressed into `docs/{README.md => guide.md}` rather than written out twice.
The author-grouped summary git generates for release notes: a name, a commit count in parentheses, and the subjects indented by six spaces. The counts here are what the .mailmap fixture in this category produces after coalescing aliases, so the two files can be used together.
A hand-maintained changelog in the Keep a Changelog layout: an Unreleased section, dated version headings in square brackets, the standard change-type subheadings, and reference-style compare links at the bottom. It describes the same releases as the generated twin in this group, so a converter between the two can be scored.
The machine-generated twin of the hand-written changelog: version headings that are themselves compare links, a leading warning glyph on the breaking-changes section, and every entry ending in a short-hash link back to the commit. The hashes are the same fictional ids used by the log fixtures in this category.
The canonical checkout trace with its spans emitted in the order they finished — children before parents, deepest first. This is what a real batch exporter produces, and a consumer that assumes parents arrive first will drop or orphan most of the trace.
Two traces streamed as newline-delimited OTLP requests, one resource per line, so a single trace is split across several lines exactly as a line-oriented collector sink writes it. A fixture for streaming ingestion, back-pressure and partial-trace assembly.
The same five OTLP log records reframed as one self-contained export per line, with the resource attributes repeated on every line. The trade a line-oriented sink makes: each line stands alone, at the cost of repeating the resource block five times.
Logs that record their own sampling decision, with periodic dropped_since_last counters so the true event rate can be reconstructed from a sampled stream. Counting sampled records as if they were the whole population is the mistake this fixture is built to expose.
The territory as one GeoJSON Feature per line, the shape streaming pipelines and BigQuery/Athena loaders expect. There is no enclosing FeatureCollection, so a reader that insists on one will reject it.
An Argo Workflows DAG template carrying the same six-task ETL topology as the Airflow, Graphviz and Mermaid fixtures in this category, so a converter or visualiser can be scored against one known answer across four formats.
Argo's steps template, whose double-nested list is a genuine parser trap: the outer list is sequential and the inner list is parallel, so a reader that flattens it reports four sequential steps instead of three groups.
An Airflow DAG definition in the classic operator style, using only the no-op EmptyOperator so the file describes a topology and performs no work. Carries the six-task ETL graph shared across the Airflow JSON, Argo, Graphviz and Mermaid fixtures.
A decorator-based Airflow TaskFlow DAG where dependencies are implied by function calls rather than >> operators — the shape static DAG extractors most often get wrong. Every task returns a literal, so nothing performs I/O.
The serialized-DAG shape Airflow stores in its metadata database, as standalone JSON: per-task metadata with explicit downstream_task_ids. Carries the same six-task topology as the Python, Argo, Graphviz and Mermaid fixtures.
The shared six-task topology as a plain node/edge JSON document that also publishes the expected answers — roots, leaves, longest path, and topological order — so a graph library or DAG visualiser can be scored without a hand-written oracle.
A CWL Workflow carrying the same six-step ETL topology as the Airflow and Argo fixtures, with dependencies expressed as step output references and a multi-source input resolved by pickValue. For cross-format DAG conversion tests.
CWL scatter in both forms: a single-parameter scatter over an array input, and a two-parameter scatter with scatterMethod flat_crossproduct. The scatter dimensionality is what distinguishes correct CWL engines from approximate ones.
The shared six-task ETL topology as a Graphviz digraph, with node and edge defaults, per-node attribute overrides, a same-rank constraint and C-style comments. Carries the identical graph to the Airflow, Argo, CWL and Mermaid fixtures.
The same topology plus one back edge, so the graph is deliberately not a DAG. A renderer must still draw it and a scheduler must reject it, which is why this is a reference fixture rather than a corrupt file.
The shared six-task ETL topology as a Mermaid flowchart, with mixed node shapes, a subgraph, a classDef and %% comments. The Mermaid twin of the Graphviz fixture, for scoring diagram-format converters against one known answer.
A Mermaid stateDiagram-v2 describing environment promotion with canary and rollback transitions, start and end pseudostates, and a note block. A second Mermaid diagram type, so a renderer is not only tested on flowcharts.
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