soda-sql-core 2.1.0b2

Soda SQL library & CLI

Soda SQL

Data testing, monitoring and profiling for SQL accessible data.

What does Soda SQL do?

Soda SQL allows you to

• Stop your pipeline when bad data is detected
• Extract metrics and column profiles through super efficient SQL
• Full control over metrics and queries through declarative config files

Why Soda SQL?

To protect against silent data issues for the consumers of your data, it's best-practice to profile and test your data:

• as it lands in your warehouse,
• after every important data processing step
• right before consumption.

This way you will prevent delivery of bad data to downstream consumers. You will spend less time firefighting and gain a better reputation.

How does Soda SQL work?

Soda SQL is a Command Line Interface (CLI) and a Python library to measure and test your data using SQL.

As input, Soda SQL uses YAML configuration files that include:

• SQL connection details
• What metrics to compute
• What tests to run on the measurements

Based on those configuration files, Soda SQL will perform scans. A scan performs all measurements and runs all tests associated with one table. Typically a scan is executed after new data has arrived. All soda-sql configuration files can be checked into your version control system as part of your pipeline code.

Want to try Soda SQL? Head over to our 'Quick start tutorial' and get started straight away!

"Show me the metrics"

Let's walk through an example. Simple metrics and tests can be configured in scan YAML configuration files. An example of the contents of such a file:

metrics:
    - row_count
    - missing_count
    - missing_percentage
    - values_count
    - values_percentage
    - valid_count
    - valid_percentage
    - invalid_count
    - invalid_percentage
    - min
    - max
    - avg
    - sum
    - min_length
    - max_length
    - avg_length
    - distinct
    - unique_count
    - duplicate_count
    - uniqueness
    - maxs
    - mins
    - frequent_values
    - histogram
columns:
    ID:
        metrics:
            - distinct
            - duplicate_count
        valid_format: uuid
        tests:
            duplicate_count == 0
    CATEGORY:
        missing_values:
            - N/A
            - No category
        tests:
            missing_percentage < 3
    SIZE:
        tests:
            max - min < 20
sql_metrics:
    - sql: |
        SELECT sum(volume) as total_volume_us
        FROM CUSTOMER_TRANSACTIONS
        WHERE country = 'US'
      tests:
        - total_volume_us > 5000

Based on these configuration files, Soda SQL will scan your data each time new data arrived like this:

$ soda scan ./soda/metrics my_warehouse my_dataset
Soda 1.0 scan for dataset my_dataset on prod my_warehouse
  | SELECT column_name, data_type, is_nullable
  | FROM information_schema.columns
  | WHERE lower(table_name) = 'customers'
  |   AND table_catalog = 'datasource.database'
  |   AND table_schema = 'datasource.schema'
  - 0.256 seconds
Found 4 columns: ID, NAME, CREATE_DATE, COUNTRY
  | SELECT
  |  COUNT(*),
  |  COUNT(CASE WHEN ID IS NULL THEN 1 END),
  |  COUNT(CASE WHEN ID IS NOT NULL AND ID regexp '\b[0-9a-f]{8}\b-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-\b[0-9a-f]{12}\b' THEN 1 END),
  |  MIN(LENGTH(ID)),
  |  AVG(LENGTH(ID)),
  |  MAX(LENGTH(ID)),
  | FROM customers
  - 0.557 seconds
row_count : 23543
missing   : 23
invalid   : 0
min_length: 9
avg_length: 9
max_length: 9

...more queries...

47 measurements computed
23 tests executed
All is good. No tests failed. Scan took 23.307 seconds

The next step is to add Soda SQL scans in your favorite data pipeline orchestration solution like:

• Airflow
• AWS Glue
• Prefect
• Dagster
• Fivetran
• Matillion
• Luigi

If you like the goals of this project, encourage us! Star sodadata/soda-sql on Github.

Next, head over to our 'Quick start tutorial' and get your first project going!