Data Contract CLI

Test Workflow Stars Slack Status

The datacontract CLI is an open source command-line tool for working with Data Contracts. It uses data contract YAML files to lint the data contract, connect to data sources and execute schema and quality tests, detect breaking changes, and export to different formats. The tool is written in Python. It can be used as a standalone CLI tool, in a CI/CD pipeline, or directly as a Python library.

Main features of the Data Contract CLI

Getting started

Let’s look at this data contract: https://datacontract.com/examples/orders-latest/datacontract.yaml

We have a servers section with endpoint details to the S3 bucket, models for the structure of the data, servicelevels and quality attributes that describe the expected freshness and number of rows.

This data contract contains all information to connect to S3 and check that the actual data meets the defined schema and quality requirements. We can use this information to test if the actual data set in S3 is compliant to the data contract.

Let’s use pip to install the CLI (or use the Docker image, if you prefer).

$ python3 -m pip install datacontract-cli[all]

We run the tests:

$ datacontract test https://datacontract.com/examples/orders-latest/datacontract.yaml

# returns:
Testing https://datacontract.com/examples/orders-latest/datacontract.yaml
╭────────┬─────────────────────────────────────────────────────────────────────┬───────────────────────────────┬─────────╮
│ Result │ Check                                                               │ Field                         │ Details │
├────────┼─────────────────────────────────────────────────────────────────────┼───────────────────────────────┼─────────┤
│ passed │ Check that JSON has valid schema                                    │ orders                        │         │
│ passed │ Check that JSON has valid schema                                    │ line_items                    │         │
│ passed │ Check that field order_id is present                                │ orders                        │         │
│ passed │ Check that field order_timestamp is present                         │ orders                        │         │
│ passed │ Check that field order_total is present                             │ orders                        │         │
│ passed │ Check that field customer_id is present                             │ orders                        │         │
│ passed │ Check that field customer_email_address is present                  │ orders                        │         │
│ passed │ row_count >= 5000                                                   │ orders                        │         │
│ passed │ Check that required field order_id has no null values               │ orders.order_id               │         │
│ passed │ Check that unique field order_id has no duplicate values            │ orders.order_id               │         │
│ passed │ duplicate_count(order_id) = 0                                       │ orders.order_id               │         │
│ passed │ Check that required field order_timestamp has no null values        │ orders.order_timestamp        │         │
│ passed │ freshness(order_timestamp) < 24h                                    │ orders.order_timestamp        │         │
│ passed │ Check that required field order_total has no null values            │ orders.order_total            │         │
│ passed │ Check that required field customer_email_address has no null values │ orders.customer_email_address │         │
│ passed │ Check that field lines_item_id is present                           │ line_items                    │         │
│ passed │ Check that field order_id is present                                │ line_items                    │         │
│ passed │ Check that field sku is present                                     │ line_items                    │         │
│ passed │ values in (order_id) must exist in orders (order_id)                │ line_items.order_id           │         │
│ passed │ row_count >= 5000                                                   │ line_items                    │         │
│ passed │ Check that required field lines_item_id has no null values          │ line_items.lines_item_id      │         │
│ passed │ Check that unique field lines_item_id has no duplicate values       │ line_items.lines_item_id      │         │
╰────────┴─────────────────────────────────────────────────────────────────────┴───────────────────────────────┴─────────╯
🟢 data contract is valid. Run 22 checks. Took 6.739514 seconds.

Voilà, the CLI tested that the datacontract.yaml itself is valid, all records comply with the schema, and all quality attributes are met.

We can also use the datacontract.yaml to export in many formats, e.g., to SQL:

$ datacontract export --format sql https://datacontract.com/examples/orders-latest/datacontract.yaml

# returns:
-- Data Contract: urn:datacontract:checkout:orders-latest
-- SQL Dialect: snowflake
CREATE TABLE orders (
  order_id TEXT not null primary key,
  order_timestamp TIMESTAMP_TZ not null,
  order_total NUMBER not null,
  customer_id TEXT,
  customer_email_address TEXT not null,
  processed_timestamp TIMESTAMP_TZ not null
);
CREATE TABLE line_items (
  lines_item_id TEXT not null primary key,
  order_id TEXT,
  sku TEXT
);

Or generate an HTML export:

$ datacontract export --format html https://datacontract.com/examples/orders-latest/datacontract.yaml > datacontract.html

which will create this HTML export.

Usage

# create a new data contract from example and write it to datacontract.yaml
$ datacontract init datacontract.yaml

# lint the datacontract.yaml
$ datacontract lint datacontract.yaml

# execute schema and quality checks
$ datacontract test datacontract.yaml

# execute schema and quality checks on the examples within the contract
$ datacontract test --examples datacontract.yaml

# export data contract as html (other formats: avro, dbt, dbt-sources, dbt-staging-sql, jsonschema, odcs, rdf, sql, sodacl, terraform, ...)
$ datacontract export --format html datacontract.yaml > datacontract.html

# import avro (other formats: sql, glue, bigquery...)
$ datacontract import --format avro --source avro_schema.avsc

# find differences between to data contracts
$ datacontract diff datacontract-v1.yaml datacontract-v2.yaml

# find differences between to data contracts categorized into error, warning, and info.
$ datacontract changelog datacontract-v1.yaml datacontract-v2.yaml

# fail pipeline on breaking changes. Uses changelog internally and showing only error and warning.
$ datacontract breaking datacontract-v1.yaml datacontract-v2.yaml

Programmatic (Python)

from datacontract.data_contract import DataContract

data_contract = DataContract(data_contract_file="datacontract.yaml")
run = data_contract.test()
if not run.has_passed():
    print("Data quality validation failed.")
    # Abort pipeline, alert, or take corrective actions...

Installation

Choose the most appropriate installation method for your needs:

pip

Python 3.11 recommended. Python 3.12 available as pre-release release candidate for 0.9.3

python3 -m pip install datacontract-cli[all]

pipx

pipx installs into an isolated environment.

pipx install datacontract-cli[all]

Docker

docker pull datacontract/cli
docker run --rm -v ${PWD}:/home/datacontract datacontract/cli

Or via an alias that automatically uses the latest version:

alias datacontract='docker run --rm -v "${PWD}:/home/datacontract" datacontract/cli:latest'

Optional Dependencies

The CLI tool defines several optional dependencies (also known as extras) that can be installed for using with specific servers types. With all, all server dependencies are included.

pip install datacontract-cli[all]

A list of available extras:

Dependency Installation Command
Avro Support pip install datacontract-cli[avro]
Google BigQuery pip install datacontract-cli[bigquery]
Databricks Integration pip install datacontract-cli[databricks]
Deltalake Integration pip install datacontract-cli[deltalake]
Kafka Integration pip install datacontract-cli[kafka]
PostgreSQL Integration pip install datacontract-cli[postgres]
S3 Integration pip install datacontract-cli[s3]
Snowflake Integration pip install datacontract-cli[snowflake]
Microsoft SQL Server pip install datacontract-cli[sqlserver]

Documentation

Commands

init

 Usage: datacontract init [OPTIONS] [LOCATION]

 Download a datacontract.yaml template and write it to file.

╭─ Arguments ──────────────────────────────────────────────────────────────────────────────────╮
│   location      [LOCATION]  The location (url or path) of the data contract yaml to create. │
│                             [default: datacontract.yaml]                                    │
╰──────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────────────────────────────────╮
│ --template                       TEXT  URL of a template or data contract                   │
│                                        [default:                                            │
│                                        https://datacontract.com/datacontract.init.yaml]     │
│ --overwrite    --no-overwrite          Replace the existing datacontract.yaml               │
│                                        [default: no-overwrite]                              │
│ --help                                 Show this message and exit.                          │
╰──────────────────────────────────────────────────────────────────────────────────────────────╯

lint

 Usage: datacontract lint [OPTIONS] [LOCATION]

 Validate that the datacontract.yaml is correctly formatted.

╭─ Arguments ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│   location      [LOCATION]  The location (url or path) of the data contract yaml. [default: datacontract.yaml]                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --schema        TEXT  The location (url or path) of the Data Contract Specification JSON Schema                                │
│                       [default: https://datacontract.com/datacontract.schema.json]                                             │
│ --help                Show this message and exit.                                                                              │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

test

 Usage: datacontract test [OPTIONS] [LOCATION]

 Run schema and quality tests on configured servers.

╭─ Arguments ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│   location      [LOCATION]  The location (url or path) of the data contract yaml. [default: datacontract.yaml]                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --schema                                                       TEXT  The location (url or path) of the Data Contract           │
│                                                                      Specification JSON Schema                                 │
│                                                                      [default:                                                 │
│                                                                      https://datacontract.com/datacontract.schema.json]        │
│ --server                                                       TEXT  The server configuration to run the schema and quality    │
│                                                                      tests. Use the key of the server object in the data       │
│                                                                      contract yaml file to refer to a server, e.g.,            │
│                                                                      `production`, or `all` for all servers (default).         │
│                                                                      [default: all]                                            │
│ --examples                    --no-examples                          Run the schema and quality tests on the example data      │
│                                                                      within the data contract.                                 │
│                                                                      [default: no-examples]                                    │
│ --publish                                                      TEXT  The url to publish the results after the test             │
│                                                                      [default: None]                                           │
│ --publish-to-opentelemetry    --no-publish-to-opentelemetry          Publish the results to opentelemetry. Use environment     │
│                                                                      variables to configure the OTLP endpoint, headers, etc.   │
│                                                                      [default: no-publish-to-opentelemetry]                    │
│ --logs                        --no-logs                              Print logs [default: no-logs]                             │
│ --help                                                               Show this message and exit.                               │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Data Contract CLI connects to a data source and runs schema and quality tests to verify that the data contract is valid.

$ datacontract test --server production datacontract.yaml

To connect to the databases the server block in the datacontract.yaml is used to set up the connection. In addition, credentials, such as username and passwords, may be defined with environment variables.

The application uses different engines, based on the server type. Internally, it connects with DuckDB, Spark, or a native connection and executes the most tests with soda-core and fastjsonschema.

Credentials are provided with environment variables.

Supported server types:

Supported formats:

Feel free to create an issue, if you need support for an additional type and formats.

S3

Data Contract CLI can test data that is stored in S3 buckets or any S3-compliant endpoints in various formats.

Examples

JSON

datacontract.yaml

servers:
  production:
    type: s3
    endpointUrl: https://minio.example.com # not needed with AWS S3
    location: s3://bucket-name/path/*/*.json
    format: json
    delimiter: new_line # new_line, array, or none
Delta Tables

datacontract.yaml

servers:
  production:
    type: s3
    endpointUrl: https://minio.example.com # not needed with AWS S3
    location: s3://bucket-name/path/table.delta # path to the Delta table folder containing parquet data files and the _delta_log
    format: delta

Environment Variables

Environment Variable Example Description
DATACONTRACT_S3_REGION eu-central-1 Region of S3 bucket
DATACONTRACT_S3_ACCESS_KEY_ID AKIAXV5Q5QABCDEFGH AWS Access Key ID
DATACONTRACT_S3_SECRET_ACCESS_KEY 93S7LRrJcqLaaaa/XXXXXXXXXXXXX AWS Secret Access Key

BigQuery

We support authentication to BigQuery using Service Account Key. The used Service Account should include the roles:

Example

datacontract.yaml

servers:
  production:
    type: bigquery
    project: datameshexample-product
    dataset: datacontract_cli_test_dataset
models:
  datacontract_cli_test_table: # corresponds to a BigQuery table
    type: table
    fields: ...

Environment Variables

Environment Variable Example Description
DATACONTRACT_BIGQUERY_ACCOUNT_INFO_JSON_PATH ~/service-access-key.json Service Access key as saved on key creation by BigQuery. If this environment variable isn’t set, the cli tries to use GOOGLE_APPLICATION_CREDENTIALS as a fallback, so if you have that set for using their Python library anyway, it should work seamlessly.

Azure

Data Contract CLI can test data that is stored in Azure Blob storage or Azure Data Lake Storage (Gen2) (ADLS) in various formats.

Example

datacontract.yaml

servers:
  production:
    type: azure
    location: abfss://datameshdatabricksdemo.dfs.core.windows.net/dataproducts/inventory_events/*.parquet
    format: parquet

Environment Variables

Authentication works with an Azure Service Principal (SPN) aka App Registration with a secret.

Environment Variable Example Description
DATACONTRACT_AZURE_TENANT_ID 79f5b80f-10ff-40b9-9d1f-774b42d605fc The Azure Tenant ID
DATACONTRACT_AZURE_CLIENT_ID 3cf7ce49-e2e9-4cbc-a922-4328d4a58622 The ApplicationID / ClientID of the app registration
DATACONTRACT_AZURE_CLIENT_SECRET yZK8Q~GWO1MMXXXXXXXXXXXXX The Client Secret value

Sqlserver

Data Contract CLI can test data in MS SQL Server (including Azure SQL, Synapse Analytics SQL Pool).

Example

datacontract.yaml

servers:
  production:
    type: sqlserver
    host: localhost
    port: 5432
    database: tempdb
    schema: dbo
    driver: ODBC Driver 18 for SQL Server
models:
  my_table_1: # corresponds to a table
    type: table
    fields:
      my_column_1: # corresponds to a column
        type: varchar

Environment Variables

Environment Variable Example Description
DATACONTRACT_SQLSERVER_USERNAME root Username
DATACONTRACT_SQLSERVER_PASSWORD toor Password
DATACONTRACT_SQLSERVER_TRUSTED_CONNECTION True Use windows authentication, instead of login
DATACONTRACT_SQLSERVER_TRUST_SERVER_CERTIFICATE True Trust self-signed certificate
DATACONTRACT_SQLSERVER_ENCRYPTED_CONNECTION True Use SSL

Databricks

Works with Unity Catalog and Hive metastore.

Needs a running SQL warehouse or compute cluster.

Example

datacontract.yaml

servers:
  production:
    type: databricks
    host: dbc-abcdefgh-1234.cloud.databricks.com
    catalog: acme_catalog_prod
    schema: orders_latest
models:
  orders: # corresponds to a table
    type: table
    fields: ...

Environment Variables

Environment Variable Example Description
DATACONTRACT_DATABRICKS_TOKEN dapia00000000000000000000000000000 The personal access token to authenticate
DATACONTRACT_DATABRICKS_HTTP_PATH /sql/1.0/warehouses/b053a3ffffffff The HTTP path to the SQL warehouse or compute cluster

Databricks (programmatic)

Works with Unity Catalog and Hive metastore. When running in a notebook or pipeline, the provided spark session can be used. An additional authentication is not required.

Requires a Databricks Runtime with Python >= 3.10.

Example

datacontract.yaml

servers:
  production:
    type: databricks
    host: dbc-abcdefgh-1234.cloud.databricks.com # ignored, always use current host
    catalog: acme_catalog_prod
    schema: orders_latest
models:
  orders: # corresponds to a table
    type: table
    fields: ...

Notebook

%pip install datacontract-cli[databricks]
dbutils.library.restartPython()

from datacontract.data_contract import DataContract

data_contract = DataContract(
  data_contract_file="/Volumes/acme_catalog_prod/orders_latest/datacontract/datacontract.yaml",
  spark=spark)
run = data_contract.test()
run.result

Dataframe (programmatic)

Works with Spark DataFrames. DataFrames need to be created as named temporary views. Multiple temporary views are suppored if your data contract contains multiple models.

Testing DataFrames is useful to test your datasets in a pipeline before writing them to a data source.

Example

datacontract.yaml

servers:
  production:
    type: dataframe
models:
  my_table: # corresponds to a temporary view
    type: table
    fields: ...

Example code

from datacontract.data_contract import DataContract

df.createOrReplaceTempView("my_table")

data_contract = DataContract(
  data_contract_file="datacontract.yaml",
  spark=spark,
)
run = data_contract.test()
assert run.result == "passed"

Snowflake

Data Contract CLI can test data in Snowflake.

Example

datacontract.yaml


servers:
  snowflake:
    type: snowflake
    account: abcdefg-xn12345
    database: ORDER_DB
    schema: ORDERS_PII_V2
models:
  my_table_1: # corresponds to a table
    type: table
    fields:
      my_column_1: # corresponds to a column
        type: varchar

Environment Variables

Environment Variable Example Description
DATACONTRACT_SNOWFLAKE_USERNAME datacontract Username
DATACONTRACT_SNOWFLAKE_PASSWORD mysecretpassword Password
DATACONTRACT_SNOWFLAKE_ROLE DATAVALIDATION The snowflake role to use.
DATACONTRACT_SNOWFLAKE_WAREHOUSE COMPUTE_WH The Snowflake Warehouse to use executing the tests.

Kafka

Kafka support is currently considered experimental.

Example

datacontract.yaml

servers:
  production:
    type: kafka
    host: abc-12345.eu-central-1.aws.confluent.cloud:9092
    topic: my-topic-name
    format: json

Environment Variables

Environment Variable Example Description
DATACONTRACT_KAFKA_SASL_USERNAME xxx The SASL username (key).
DATACONTRACT_KAFKA_SASL_PASSWORD xxx The SASL password (secret).

Postgres

Data Contract CLI can test data in Postgres or Postgres-compliant databases (e.g., RisingWave).

Example

datacontract.yaml

servers:
  postgres:
    type: postgres
    host: localhost
    port: 5432
    database: postgres
    schema: public
models:
  my_table_1: # corresponds to a table
    type: table
    fields:
      my_column_1: # corresponds to a column
        type: varchar

Environment Variables

Environment Variable Example Description
DATACONTRACT_POSTGRES_USERNAME postgres Username
DATACONTRACT_POSTGRES_PASSWORD mysecretpassword Password

export


 Usage: datacontract export [OPTIONS] [LOCATION]

 Convert data contract to a specific format. Prints to stdout or to the specified output file.

╭─ Arguments ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│   location      [LOCATION]  The location (url or path) of the data contract yaml. [default: datacontract.yaml]                 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *  --format        [jsonschema|pydantic-model|sodacl|dbt|dbt-sources|db  The export format. [default: None] [required]         │
│                    t-staging-sql|odcs|rdf|avro|protobuf|great-expectati                                                        │
│                    ons|terraform|avro-idl|sql|sql-query|html|go|bigquer                                                        │
│                    y|dbml|spark]                                                                                                     │
│    --output        PATH                                                  Specify the file path where the exported data will be │
│                                                                          saved. If no path is provided, the output will be     │
│                                                                          printed to stdout.                                    │
│                                                                          [default: None]                                       │
│    --server        TEXT                                                  The server name to export. [default: None]            │
│    --model         TEXT                                                  Use the key of the model in the data contract yaml    │
│                                                                          file to refer to a model, e.g., `orders`, or `all`    │
│                                                                          for all models (default).                             │
│                                                                          [default: all]                                        │
│    --help                                                                Show this message and exit.                           │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ RDF Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --rdf-base        TEXT  [rdf] The base URI used to generate the RDF graph. [default: None]                                     │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ SQL Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --sql-server-type        TEXT  [sql] The server type to determine the sql dialect. By default, it uses 'auto' to automatically │
│                                detect the sql dialect via the specified servers in the data contract.                          │
│                                [default: auto]                                                                                 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# Example export data contract as HTML
datacontract export --format html > datacontract.html

Available export options:

Type Description Status
html Export to HTML
jsonschema Export to JSON Schema
odcs Export to Open Data Contract Standard (ODCS)
sodacl Export to SodaCL quality checks in YAML format
dbt Export to dbt models in YAML format
dbt-sources Export to dbt sources in YAML format
dbt-staging-sql Export to dbt staging SQL models
rdf Export data contract to RDF representation in N3 format
avro Export to AVRO models
protobuf Export to Protobuf
terraform Export to terraform resources
sql Export to SQL DDL
sql-query Export to SQL Query
great-expectations Export to Great Expectations Suites in JSON Format
bigquery Export to BigQuery Schemas
go Export to Go types
pydantic-model Export to pydantic models
DBML Export to a DBML Diagram description
spark Export to a Spark StructType
Missing something? Please create an issue on GitHub TBD

Great Expectations

The export function transforms a specified data contract into a comprehensive Great Expectations JSON suite. If the contract includes multiple models, you need to specify the names of the model you wish to export.

datacontract  export datacontract.yaml --format great-expectations --model orders

The export creates a list of expectations by utilizing:

RDF

The export function converts a given data contract into a RDF representation. You have the option to add a base_url which will be used as the default prefix to resolve relative IRIs inside the document.

datacontract export --format rdf --rdf-base https://www.example.com/ datacontract.yaml

The data contract is mapped onto the following concepts of a yet to be defined Data Contract Ontology named https://datacontract.com/DataContractSpecification/ :

Having the data contract inside an RDF Graph gives us access the following use cases:

DBML

The export function converts the logical data types of the datacontract into the specific ones of a concrete Database if a server is selected via the --server option (based on the type of that server). If no server is selected, the logical data types are exported.

Spark

The export function converts the data contract specification into a StructType Spark schema. The returned value is a Python code picture of the model schemas.
Spark DataFrame schema is defined as StructType. For more details about Spark Data Types please see the spark documentation

Avro

The export function converts the data contract specification into an avro schema. It supports specifying custom avro properties for logicalTypes and default values.

Custom Avro Properties

We support a config map on field level. A config map may include any additional key-value pairs and support multiple server type bindings.

To specify custom Avro properties in your data contract, you can define them within the config section of your field definition. Below is an example of how to structure your YAML configuration to include custom Avro properties, such as avroLogicalType and avroDefault.

NOTE: At this moment, we just support logicalType and default

Example Configuration

models:
  orders:
    fields:
      my_field_1:
        description: Example for AVRO with Timestamp (microsecond precision) https://avro.apache.org/docs/current/spec.html#Local+timestamp+%28microsecond+precision%29
        type: long
        example: 1672534861000000  # Equivalent to 2023-01-01 01:01:01 in microseconds
        config:
          avroLogicalType: local-timestamp-micros
          avroDefault: 1672534861000000

Explanation

import

 Usage: datacontract import [OPTIONS]

 Create a data contract from the given source location. Prints to stdout.

╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *  --format                  [sql|avro|glue|bigquery|jsonschema|unity]  The format of the source file. [default: None] [required]         │
│    --source                  TEXT                                       The path to the file or Glue Database that should be imported.    │
│                                                                         [default: None]                                                   │
│    --glue-table              TEXT                                       List of table ids to import from the Glue Database (repeat for    │
│                                                                         multiple table ids, leave empty for all tables in the dataset).   │
│                                                                         [default: None]                                                   │
│    --bigquery-project        TEXT                                       The bigquery project id. [default: None]                          │
│    --bigquery-dataset        TEXT                                       The bigquery dataset id. [default: None]                          │
│    --bigquery-table          TEXT                                       List of table ids to import from the bigquery API (repeat for     │
│                                                                         multiple table ids, leave empty for all tables in the dataset).   │
│                                                                         [default: None]                                                   │
│    --help                                                               Show this message and exit.                                       │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Example:

# Example import from SQL DDL
datacontract import --format sql --source my_ddl.sql

Available import options:

Type Description Status
sql Import from SQL DDL
avro Import from AVRO schemas
glue Import from AWS Glue DataCatalog
protobuf Import from Protobuf schemas TBD
jsonschema Import from JSON Schemas
bigquery Import from BigQuery Schemas
unity Import from Databricks Unity Catalog partial
dbt Import from dbt models TBD
odcs Import from Open Data Contract Standard (ODCS)
Missing something? Please create an issue on GitHub TBD

BigQuery

Bigquery data can either be imported off of JSON Files generated from the table descriptions or directly from the Bigquery API. In case you want to use JSON Files, specify the source parameter with a path to the JSON File.

To import from the Bigquery API, you have to omit source and instead need to provide bigquery-project and bigquery-dataset. Additionally you may specify bigquery-table to enumerate the tables that should be imported. If no tables are given, all available tables of the dataset will be imported.

For providing authentication to the Client, please see the google documentation or the one about authorizing client libraries.

Examples:

# Example import from Bigquery JSON
datacontract import --format bigquery --source my_bigquery_table.json
# Example import from Bigquery API with specifying the tables to import
datacontract import --format bigquery --bigquery-project <project_id> --bigquery-dataset <dataset_id> --bigquery-table <tableid_1> --bigquery-table <tableid_2> --bigquery-table <tableid_3>
# Example import from Bigquery API importing all tables in the dataset
datacontract import --format bigquery --bigquery-project <project_id> --bigquery-dataset <dataset_id>

Unity Catalog

# Example import from a Unity Catalog JSON file
datacontract import --format unity --source my_unity_table.json
# Example import single table from Unity Catalog via HTTP endpoint
export DATABRICKS_IMPORT_INSTANCE="https://xyz.cloud.databricks.com"
export DATABRICKS_IMPORT_ACCESS_TOKEN=<token>
datacontract import --format unity --unity-table-full-name <table_full_name>

Glue

Importing from Glue reads the necessary Data directly off of the AWS API. You may give the glue-table parameter to enumerate the tables that should be imported. If no tables are given, all available tables of the database will be imported.

Examples:

# Example import from AWS Glue with specifying the tables to import
datacontract import --format glue --source <database_name> --glue-table <table_name_1> --glue-table <table_name_2> --glue-table <table_name_3>
# Example import from AWS Glue importing all tables in the database
datacontract import --format glue --source <database_name>

breaking

 Usage: datacontract breaking [OPTIONS] LOCATION_OLD LOCATION_NEW

 Identifies breaking changes between data contracts. Prints to stdout.

╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    location_old      TEXT  The location (url or path) of the old data contract yaml. [default: None] [required]        │
│ *    location_new      TEXT  The location (url or path) of the new data contract yaml. [default: None] [required]        │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                                                              │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

changelog

 Usage: datacontract changelog [OPTIONS] LOCATION_OLD LOCATION_NEW

 Generate a changelog between data contracts. Prints to stdout.

╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    location_old      TEXT  The location (url or path) of the old data contract yaml. [default: None] [required]        │
│ *    location_new      TEXT  The location (url or path) of the new data contract yaml. [default: None] [required]        │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                                                              │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

diff

 Usage: datacontract diff [OPTIONS] LOCATION_OLD LOCATION_NEW

 PLACEHOLDER. Currently works as 'changelog' does.

╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    location_old      TEXT  The location (url or path) of the old data contract yaml. [default: None] [required]        │
│ *    location_new      TEXT  The location (url or path) of the new data contract yaml. [default: None] [required]        │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                                                               │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

catalog


 Usage: datacontract catalog [OPTIONS]

 Create an html catalog of data contracts.

╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --files         TEXT  Glob pattern for the data contract files to include in the catalog. [default: *.yaml]             │
│ --output        TEXT  Output directory for the catalog html files. [default: catalog/]                                  │
│ --help                Show this message and exit.                                                                       │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Publish


 Usage: datacontract publish [OPTIONS] [LOCATION]

 Publish the data contract to the Data Mesh Manager.

╭─ Arguments ────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│   location      [LOCATION]  The location (url or path) of the data contract yaml. [default: datacontract.yaml]             │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                                                                │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Integrations

Integration Option Description
Data Mesh Manager --publish Push full results to the Data Mesh Manager API
OpenTelemetry --publish-to-opentelemetry Push result as gauge metrics

Integration with Data Mesh Manager

If you use Data Mesh Manager, you can use the data contract URL and append the --publish option to send and display the test results. Set an environment variable for your API key.

# Fetch current data contract, execute tests on production, and publish result to data mesh manager
$ EXPORT DATAMESH_MANAGER_API_KEY=xxx
$ datacontract test https://demo.datamesh-manager.com/demo279750347121/datacontracts/4df9d6ee-e55d-4088-9598-b635b2fdcbbc/datacontract.yaml --server production --publish

Integration with OpenTelemetry

If you use OpenTelemetry, you can use the data contract URL and append the --publish-to-opentelemetry option to send the test results to your OLTP-compatible instance, e.g., Prometheus.

The metric name is “datacontract.cli.test.result” and it uses the following encoding for the result:

datacontract.cli.test.result Description
0 test run passed, no warnings
1 test run has warnings
2 test run failed
3 test run not possible due to an error
4 test status unknown
# Fetch current data contract, execute tests on production, and publish result to open telemetry
$ EXPORT OTEL_SERVICE_NAME=datacontract-cli
$ EXPORT OTEL_EXPORTER_OTLP_ENDPOINT=https://YOUR_ID.apm.westeurope.azure.elastic-cloud.com:443
$ EXPORT OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer%20secret # Optional, when using SaaS Products
$ EXPORT OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf # Optional, default is http/protobuf - use value grpc to use the gRPC protocol instead
# Send to OpenTelemetry
$ datacontract test https://demo.datamesh-manager.com/demo279750347121/datacontracts/4df9d6ee-e55d-4088-9598-b635b2fdcbbc/datacontract.yaml --server production --publish-to-opentelemetry

Current limitations:

Best Practices

We share best practices in using the Data Contract CLI.

Data-first Approach

Create a data contract based on the actual data. This is the fastest way to get started and to get feedback from the data consumers.

  1. Use an existing physical schema (e.g., SQL DDL) as a starting point to define your logical data model in the contract. Double check right after the import whether the actual data meets the imported logical data model. Just to be sure.
     $ datacontract import --format sql ddl.sql
     $ datacontract test
    
  2. Add examples to the datacontract.yaml. If you can, use actual data and anonymize. Make sure that the examples match the imported logical data model.
    $ datacontract test --examples
    
  3. Add quality checks and additional type constraints one by one to the contract and make sure the examples and the actual data still adheres to the contract. Check against examples for a very fast feedback loop.
    $ datacontract test --examples
    $ datacontract test
    
  4. Make sure that all the best practices for a datacontract.yaml are met using the linter. You probably forgot to document some fields and add the terms and conditions.
    $ datacontract lint
    
  5. Set up a CI pipeline that executes daily and reports the results to the Data Mesh Manager. Or to some place else. You can even publish to any opentelemetry compatible system.
    $ datacontract test --publish https://api.datamesh-manager.com/api/runs
    

Contract-First

Create a data contract based on the requirements from use cases.

  1. Start with a datacontract.yaml template.
    $ datacontract init
    
  2. Add examples to the datacontract.yaml. Do not start with the data model, although you are probably tempted to do that. Examples are the fastest way to get feedback from everybody and not loose someone in the discussion.

  3. Create the model based on the examples. Test the model against the examples to double-check whether the model matches the examples.
     $ datacontract test --examples
    
  4. Add quality checks and additional type constraints one by one to the contract and make sure the examples and the actual data still adheres to the contract. Check against examples for a very fast feedback loop.
     $ datacontract test --examples
    
  5. Fill in the terms, descriptions, etc. Make sure you follow all best practices for a datacontract.yaml using the linter.
     $ datacontract lint
    
  6. Set up a CI pipeline that lints and tests the examples so you make sure that any changes later do not decrease the quality of the contract.
     $ datacontract lint
     $ datacontract test --examples
    
  7. Use the export function to start building the providing data product as well as the integration into the consuming data products.
     # data provider
     $ datacontract export --format dbt
     # data consumer
     $ datacontract export --format dbt-sources
     $ datacontract export --format dbt-staging-sql
    

Schema Evolution

Non-breaking Changes

Examples: adding models or fields

Development Setup

Python base interpreter should be 3.11.x (unless working on 3.12 release candidate).

# create venv
python3 -m venv venv
source venv/bin/activate

# Install Requirements
pip install --upgrade pip setuptools wheel
pip install -e '.[dev]'
pre-commit install
pre-commit run --all-files
pytest

Docker Build

docker build -t datacontract/cli .
docker run --rm -v ${PWD}:/home/datacontract datacontract/cli

Docker compose integration

We’ve included a docker-compose.yml configuration to simplify the build, test, and deployment of the image.

Building the Image with Docker Compose

To build the Docker image using Docker Compose, run the following command:

docker compose build

This command utilizes the docker-compose.yml to build the image, leveraging predefined settings such as the build context and Dockerfile location. This approach streamlines the image creation process, avoiding the need for manual build specifications each time.

Testing the Image

After building the image, you can test it directly with Docker Compose:

docker compose run --rm datacontract --version

This command runs the container momentarily to check the version of the datacontract CLI. The --rm flag ensures that the container is automatically removed after the command executes, keeping your environment clean.

Use with pre-commit

To run datacontract-cli as part of a pre-commit workflow, add something like the below to the repos list in the project’s .pre-commit-config.yaml:

repos:
  - repo: https://github.com/datacontract/datacontract-cli
    rev: "v0.10.9"
    hooks:
      - id: datacontract-lint
      - id: datacontract-test
        args: ["--server", "production"]

Available Hook IDs

Hook ID Description Dependency
datacontract-lint Runs the lint subcommand. Python3
datacontract-test Runs the test subcommand. Please look at test section for all available arguments. Python3

Release Steps

  1. Update the version in pyproject.toml
  2. Have a look at the CHANGELOG.md
  3. Create release commit manually
  4. Execute ./release
  5. Wait until GitHub Release is created
  6. Add the release notes to the GitHub Release

Contribution

We are happy to receive your contributions. Propose your change in an issue or directly create a pull request with your improvements.

Companies using this tool

License

MIT License

Credits

Created by Stefan Negele and Jochen Christ.