For context on getting started with ingestion, check out our metadata ingestion guide.
To install this plugin, run
pip install 'acryl-datahub[sql-profiles]'.
The SQL-based profiler does not run alone, but rather can be enabled for other SQL-based sources. Enabling profiling will slow down ingestion runs.
Running profiling against many tables or over many rows can run up significant costs. While we've done our best to limit the expensiveness of the queries the profiler runs, you should be prudent about the set of tables profiling is enabled on or the frequency of the profiling runs.
- Row and column counts for each table
- For each column, if applicable:
- null counts and proportions
- distinct counts and proportions
- minimum, maximum, mean, median, standard deviation, some quantile values
- histograms or frequencies of unique values
Supported SQL sources:
- AWS Athena
- Microsoft SQL Server
- Generic SQLAlchemy source
Check out the following recipe to get started with ingestion! See below for full configuration options.
For general pointers on writing and running a recipe, see our main recipe guide.
source: type: <sql-source> # can be bigquery, snowflake, etc - see above for the list config: # ... any other source-specific options ... # Options profiling: enabled: true sink: # sink configs
Note that a
. is used to denote nested fields in the YAML recipe.
|Whether profiling should be done.|
|Max number of documents to profile. By default, profiles all documents.|
|Offset in documents to profile. By default, uses no offset.|
|List of regex patterns for tables to profile.|
|List of regex patterns for tables to not profile.|
|Whether to ignore case sensitivity during pattern matching.|
If you've got any questions on configuring this source, feel free to ping us on our Slack!