# Local files Test local files in Parquet, JSON, CSV, or Delta format. This is the fastest way to see the CLI in action — no warehouse, no credentials. ## 1. Install ```bash uv tool install --python python3.11 --upgrade 'datacontract-cli[duckdb]' ``` See [Installation](../installation.md) for pip, pipx, and Docker. ## 2. Create a contract from a file Take any CSV file — or use this one, saved as `orders.csv`: ```text order_id,order_timestamp,customer_id,order_total,status ORD-1001,2024-01-01T10:00:00Z,CUST-1,4999,delivered ORD-1002,2024-01-02T11:30:00Z,CUST-2,2500,shipped ORD-1003,2024-01-03T09:15:00Z,CUST-3,1299,pending ``` Import it. The CLI profiles the data and generates a contract with a schema, value ranges, and a ready-to-test `servers` block: ```bash datacontract import csv --source orders.csv --output datacontract.yaml ``` ## 3. Test the data against the contract ```bash datacontract test datacontract.yaml ``` ``` Testing datacontract.yaml Server: production (type=local, format=csv, path=orders.csv) ╭────────┬──────────────────────────────────────────────────────┬──────────────┬─────────╮ │ Result │ Check │ Field │ Details │ ├────────┼──────────────────────────────────────────────────────┼──────────────┼─────────┤ │ passed │ Check that field 'order_id' is present │ order_id │ │ │ passed │ Check that field order_id has no missing values │ order_id │ │ │ passed │ Check that field order_total has a minimum of 1299.0 │ order_total │ │ │ ... │ │ │ │ ╰────────┴──────────────────────────────────────────────────────┴──────────────┴─────────╯ 🟢 data contract is valid. Run 17 checks. Took 1.2 seconds. ``` ## 4. Let it catch a violation Now break the data — append a row with a negative total and a duplicate customer: ```bash echo 'ORD-1004,2024-01-04T12:00:00Z,CUST-1,-100,delivered' >> orders.csv datacontract test datacontract.yaml ``` ``` 🔴 data contract is invalid, found the following errors: 1) customer_id Check that unique field customer_id has no duplicate values: Actual duplicate_count(customer_id) was 1, expected = 0 2) order_total Check that field order_total has a minimum of 1299.0: Actual invalid_count(order_total) was 1, expected = 0 ``` The command exits with code `1`, so the same call works as a gate in [CI/CD pipelines](../scheduling/index.md). ## Reference No environment variables are needed for local files. Data type inference and the per-format type handling: **[Local Files Reference](../reference/local.md)**. CSV files are read as UTF-8 unless the server declares an `encoding` (ODCS v3.2.0), such as `ISO-8859-1` or `UTF-16`. JSON files are always read as UTF-8. ## Troubleshooting - **`No files found that match the pattern`** — the `path` is a glob over file paths, not a directory, and it is resolved relative to the working directory rather than to the contract. - **No checks run at all** — the `format` in the `servers` block must be one of `csv`, `json`, `parquet`, or `delta`. It is never guessed at test time (only `datacontract import` infers it from the file suffix), so a missing or misspelled `format` leaves the table unreadable. - **Every schema reads the same files** — with more than one schema in the contract, put the `{model}` placeholder in the `path` (e.g. `./data/{model}/*.parquet`); it is substituted with each schema's name. - **A CSV value fails as a read error instead of a type check** — CSV files are read *as* the contract's types, so a value that cannot be coerced surfaces while reading. See [Data types](../reference/local.md#data-types). Ready for your real data? Do the same against [Snowflake](./snowflake.md), [BigQuery](./bigquery.md), or [Databricks](./databricks.md).