Avro — Row Binary + Schema
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.
| idint64 | namestring | emailstring | departmentstring | activebool | scoredouble | joineddate |
|---|---|---|---|---|---|---|
| 1001 | Ada Lovelace | ada.lovelace@example.com | Engineering | true | 98.5 | 2021-03-01 |
| 1002 | Alan Turing | alan.turing@example.com | Research | true | 95 | 2020-06-15 |
| 1003 | Grace Hopper | grace.hopper@example.com | Engineering | false | 91.2 | 2019-11-20 |
| 1004 | Katherine Johnson | katherine.johnson@example.com | Operations | true | 96.8 | 2022-01-10 |
| 1005 | Edsger Dijkstra | edsger.dijkstra@example.com | Research | false | 89.4 | 2018-09-05 |
Specifications
- Rows
- 5
- Columns
- 7
- Format
- Apache Avro
- Layout
- row
- Schema
- embedded
What is a .avro file?
Apache Avro (.avro) is a compact binary, row-oriented data format that stores its JSON schema in the file header alongside the records, enabling schema evolution. It is widely used with Kafka and Hadoop for message and data serialization.
How to use this file
Use an example .avro file to test Avro readers, embedded-schema parsing, and schema-evolution or Avro-to-JSON tooling.
How to use this file for testing
“Avro — Row Binary + Schema” is a deterministic Novus Examples fixture for Conversion testing, Data engineering. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.
Documented properties for this file: 5 rows · 7 columns · schema: embedded. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import fastavro
with open("employees.avro", "rb") as f:
for record in fastavro.reader(f):
print(record)Related files
- h5HDF5 — Hierarchical Scientific DataA 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.
- orcConvert v2 ORC Employee Table SourceBinary orc source for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-source.
- csvConvert v2 ORC Expected CSVCsv semantic reference for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-csv.
- jsonConvert v2 ORC Expected JSONJson semantic reference for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-json.
- csvE-commerce Customers (CSV, 500 rows)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.
- csvE-commerce Orders (CSV, 2000 rows)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.
Generated by generation/data_binary.py. Free for any use, no attribution required — license.