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Microsoft SQL Server

Module mssql​

Certified

Important Capabilities​

CapabilityStatusNotes
Data Profiling✅Optionally enabled via configuration
Dataset Usage❌Not provided by this module, use bigquery-usage for that.
Descriptions✅Enabled by default
Detect Deleted Entities✅Enabled via stateful ingestion
Domains✅Supported via the domain config field
Platform Instance✅Enabled by default

This plugin extracts the following:

  • Metadata for databases, schemas, views and tables
  • Column types associated with each table/view
  • Table, row, and column statistics via optional SQL profiling

We have two options for the underlying library used to connect to SQL Server: (1) python-tds and (2) pyodbc. The TDS library is pure Python and hence easier to install, but only PyODBC supports encrypted connections.

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[mssql]'

Starter Recipe​

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: mssql
  config:
    # Coordinates
    host_port: localhost:1433
    database: DemoDatabase

    # Credentials
    username: user
    password: pass

sink:
  # sink configs

#------------------------------------------------------------------------
#Example: using ingestion with ODBC and encryption
#This requires you to have already installed the Microsoft ODBC Driver for SQL Server.
#See https://docs.microsoft.com/en-us/sql/connect/python/pyodbc/step-1-configure-development-environment-for-pyodbc-python-development?view=sql-server-ver15
# ------------------------------------------------------------------------

source:
  type: mssql
  config:
    # Coordinates
    host_port: localhost:1433
    database: DemoDatabase

    # Credentials
    username: admin
    password: password

    # Options
    use_odbc: "True"
    uri_args:
      driver: "ODBC Driver 17 for SQL Server"
      Encrypt: "yes"
      TrustServerCertificate: "Yes"
      ssl: "True"

sink:
  # sink configs

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

View All Configuration Options
Field [Required]TypeDescriptionDefaultNotes
database [✅]stringdatabase (catalog). If set to Null, all databases will be considered for ingestion.None
database_alias [✅]stringAlias to apply to database when ingesting.None
host_port [✅]stringMSSQL host URL.localhost:1433
include_table_location_lineage [✅]booleanIf the source supports it, include table lineage to the underlying storage location.True
include_tables [✅]booleanWhether tables should be ingested.True
include_views [✅]booleanWhether views should be ingested.True
options [✅]objectAny options specified here will be passed to SQLAlchemy's create_engine as kwargs. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine for details.None
password [✅]string(password)passwordNone
platform_instance [✅]stringThe instance of the platform that all assets produced by this recipe belong toNone
sqlalchemy_uri [✅]stringURI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.None
uri_args [✅]map(str,string)None
use_odbc [✅]booleanSee https://docs.sqlalchemy.org/en/14/dialects/mssql.html#module-sqlalchemy.dialects.mssql.pyodbc.None
username [✅]stringusernameNone
env [✅]stringThe environment that all assets produced by this connector belong toPROD
database_pattern [✅]AllowDenyPatternRegex patterns for databases to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
database_pattern.allow [❓ (required if database_pattern is set)]array(string)None
database_pattern.deny [❓ (required if database_pattern is set)]array(string)None
database_pattern.ignoreCase [❓ (required if database_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
domain [✅]map(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allow [❓ (required if domain is set)]array(string)None
domain.key.deny [❓ (required if domain is set)]array(string)None
domain.key.ignoreCase [❓ (required if domain is set)]booleanWhether to ignore case sensitivity during pattern matching.True
profile_pattern [✅]AllowDenyPatternRegex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow [❓ (required if profile_pattern is set)]array(string)None
profile_pattern.deny [❓ (required if profile_pattern is set)]array(string)None
profile_pattern.ignoreCase [❓ (required if profile_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
schema_pattern [✅]AllowDenyPatternRegex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.deny [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.ignoreCase [❓ (required if schema_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
table_pattern [✅]AllowDenyPatternRegex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow [❓ (required if table_pattern is set)]array(string)None
table_pattern.deny [❓ (required if table_pattern is set)]array(string)None
table_pattern.ignoreCase [❓ (required if table_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
view_pattern [✅]AllowDenyPatternRegex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow [❓ (required if view_pattern is set)]array(string)None
view_pattern.deny [❓ (required if view_pattern is set)]array(string)None
view_pattern.ignoreCase [❓ (required if view_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
profiling [✅]GEProfilingConfig{'enabled': False, 'limit': None, 'offset': None, 'report_dropped_profiles': False, 'turn_off_expensive_profiling_metrics': False, 'profile_table_level_only': False, 'include_field_null_count': True, 'include_field_distinct_count': True, 'include_field_min_value': True, 'include_field_max_value': True, 'include_field_mean_value': True, 'include_field_median_value': True, 'include_field_stddev_value': True, 'include_field_quantiles': False, 'include_field_distinct_value_frequencies': False, 'include_field_histogram': False, 'include_field_sample_values': True, 'field_sample_values_limit': 20, 'max_number_of_fields_to_profile': None, 'profile_if_updated_since_days': None, 'profile_table_size_limit': 5, 'profile_table_row_limit': 5000000, 'profile_table_row_count_estimate_only': False, 'max_workers': 20, 'query_combiner_enabled': True, 'catch_exceptions': True, 'partition_profiling_enabled': True, 'partition_datetime': None}
profiling.catch_exceptions [❓ (required if profiling is set)]booleanTrue
profiling.enabled [❓ (required if profiling is set)]booleanWhether profiling should be done.None
profiling.field_sample_values_limit [❓ (required if profiling is set)]integerUpper limit for number of sample values to collect for all columns.20
profiling.include_field_distinct_count [❓ (required if profiling is set)]booleanWhether to profile for the number of distinct values for each column.True
profiling.include_field_distinct_value_frequencies [❓ (required if profiling is set)]booleanWhether to profile for distinct value frequencies.None
profiling.include_field_histogram [❓ (required if profiling is set)]booleanWhether to profile for the histogram for numeric fields.None
profiling.include_field_max_value [❓ (required if profiling is set)]booleanWhether to profile for the max value of numeric columns.True
profiling.include_field_mean_value [❓ (required if profiling is set)]booleanWhether to profile for the mean value of numeric columns.True
profiling.include_field_median_value [❓ (required if profiling is set)]booleanWhether to profile for the median value of numeric columns.True
profiling.include_field_min_value [❓ (required if profiling is set)]booleanWhether to profile for the min value of numeric columns.True
profiling.include_field_null_count [❓ (required if profiling is set)]booleanWhether to profile for the number of nulls for each column.True
profiling.include_field_quantiles [❓ (required if profiling is set)]booleanWhether to profile for the quantiles of numeric columns.None
profiling.include_field_sample_values [❓ (required if profiling is set)]booleanWhether to profile for the sample values for all columns.True
profiling.include_field_stddev_value [❓ (required if profiling is set)]booleanWhether to profile for the standard deviation of numeric columns.True
profiling.limit [❓ (required if profiling is set)]integerMax number of documents to profile. By default, profiles all documents.None
profiling.max_number_of_fields_to_profile [❓ (required if profiling is set)]integerA positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.None
profiling.max_workers [❓ (required if profiling is set)]integerNumber of worker threads to use for profiling. Set to 1 to disable.20
profiling.offset [❓ (required if profiling is set)]integerOffset in documents to profile. By default, uses no offset.None
profiling.partition_datetime [❓ (required if profiling is set)]string(date-time)For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.None
profiling.partition_profiling_enabled [❓ (required if profiling is set)]booleanTrue
profiling.profile_if_updated_since_days [❓ (required if profiling is set)]numberProfile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.None
profiling.profile_table_level_only [❓ (required if profiling is set)]booleanWhether to perform profiling at table-level only, or include column-level profiling as well.None
profiling.profile_table_row_count_estimate_only [❓ (required if profiling is set)]booleanUse an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.None
profiling.profile_table_row_limit [❓ (required if profiling is set)]integerProfile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery5000000
profiling.profile_table_size_limit [❓ (required if profiling is set)]integerProfile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery5
profiling.query_combiner_enabled [❓ (required if profiling is set)]booleanThis feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.True
profiling.report_dropped_profiles [❓ (required if profiling is set)]booleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.None
profiling.turn_off_expensive_profiling_metrics [❓ (required if profiling is set)]booleanWhether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.None
stateful_ingestion [✅]StatefulStaleMetadataRemovalConfigBase specialized config for Stateful Ingestion with stale metadata removal capability.None
stateful_ingestion.enabled [❓ (required if stateful_ingestion is set)]booleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadata [❓ (required if stateful_ingestion is set)]booleanSoft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.True

Code Coordinates​

  • Class Name: datahub.ingestion.source.sql.mssql.SQLServerSource
  • Browse on GitHub

Questions​

If you've got any questions on configuring ingestion for Microsoft SQL Server, feel free to ping us on our Slack