Supported data sources for curation and data quality
You can connect to many data sources from which you can import metadata, against which you can run metadata enrichment or data quality rules, which you can use to create dynamic views, and to which you can write the output of data quality rules.
A dash (—) in any of the columns indicates that the data source is not supported for this purpose.
By default, data quality rules and the underlying DataStage flows support standard platform connections. Not all connectors that were supported in traditional DataStage and potentially used in custom DataStage flows are supported in IBM Knowledge Catalog.
Requirements and restrictions
Understand the requirements and restrictions for connections to be used in data curation and data quality assessment.
Required permissions
Users must be authorized to access the connections to the data sources. For metadata import, the user running the import must have the SELECT or a similar permission on the databases in question.
General prerequisites
Connection assets must exist in the project for connections that are used in these cases:
- For running metadata enrichment including advanced analysis (in-depth primary key analysis, in-depth relationship analysis, or advanced data profiling) on assets in a metadata enrichment
- For running data quality rules
- For creating query-based data assets (dynamic views)
- For writing output of data quality checks or frequency distribution tables
Supported source data formats
In general, metadata import, metadata enrichment, and data quality rules support the following data formats:
-
All: Tables from relational and nonrelational data sources
For Amazon S3, the Delta Lake table format
-
Metadata import: Any format from file-based connections to the data sources. For Microsoft Excel workbooks, each sheet is imported as a separate data asset. The data asset name equals the name of the Excel sheet.
-
Metadata enrichment: Tabular: CSV, TSV, Avro, Parquet, Microsoft Excel (For workbooks uploaded from the local file system, only the first sheet in a workbook is profiled.)
-
Data quality rules: Tabular: Avro, CSV, Parquet, ORC; for data assets uploaded from the local file system, CSV only
Database support for analysis output tables
In general, output tables that are generated during analysis can be written to these databases:
If a specific database connector also supports output tables, the Target for output tables column shows a checkmark.
File-storage connectors
| Connector | Metadata import | Metadata enrichment | Definition-based rules |
|---|---|---|---|
| Amazon S3 | ✓ | ✓ | ✓ |
| Apache HDFS | ✓ | ✓ | ✓ |
| Box | ✓ | ✓ 1 | — |
| Generic S3 | ✓ | ✓ 1 | — |
| IBM Cloud Object Storage | ✓ | ✓ | — |
| IBM Match 360 | ✓ | ✓ | ✓ |
| Microsoft Azure Data Lake Storage | ✓ | ✓ 1 | ✓ |
Notes:
1 Advanced analysis is not supported for this data source.
Database connectors
| Connector | Metadata import | Metadata enrichment | Definition-based rules | SQL-based rules | SQL-based data assets | Target for output tables |
|---|---|---|---|---|---|---|
| Amazon RDS for MySQL | ✓ | ✓ | — | — | — | — |
| Amazon RDS for Oracle | ✓ | — | ✓ | ✓ | — | — |
| Amazon RDS for PostgreSQL | ✓ | ✓ | — | — | — | — |
| Amazon Redshift | ✓ | ✓ 1 | ✓ | ✓ | ✓ | — |
| Apache Cassandra | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| Apache Hive | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ 5 |
| Apache Impala with Apache Kudu | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| Google BigQuery | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ 6 |
| Greenplum | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| IBM Cloud Data Engine | ✓ | ✓ | — | — | — | — |
| IBM Cloud Databases for MongoDB | ✓ | ✓ | — | — | — | — |
| IBM Cloud Databases for MySQL | ✓ | ✓ | — | — | — | — |
| IBM Cloud Databases for PostgreSQL | ✓ | ✓ | — | — | — | — |
| IBM Data Virtualization | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| IBM Data Virtualization Manager for z/OS 2 | ✓ | ✓ | — | — | — | — |
| IBM Db2 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| IBM Db2 Big SQL | ✓ | ✓ | — | — | — | — |
| IBM Db2 for z/OS | ✓ | ✓ | — | — | — | — |
| IBM Db2 on Cloud | ✓ | ✓ | ✓ | ✓ | — | ✓ |
| IBM Db2 Warehouse | ✓ | ✓ | — | — | — | — |
| IBM Informix | ✓ | ✓ | — | — | — | — |
| IBM Netezza Performance Server | ✓ | ✓ | ✓ | ✓ | — | — |
| Connector | Metadata import | Metadata enrichment | Definition-based rules | SQL-based rules | SQL-based data assets | Target for output tables |
| MariaDB | ✓ | ✓ | — | — | — | — |
| Microsoft Azure Databricks7 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Microsoft Azure SQL Database | ✓ | ✓ 1 | ✓ | ✓ | ✓ | — |
| Microsoft SQL Server | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| MongoDB | ✓ | ✓ | ✓ | ✓ | — | — |
| MySQL | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| Oracle 3 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| PostgreSQL | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Salesforce.com | ✓ | ✓ 1 4 | — | — | — | — |
| SAP ASE | ✓ | ✓ 1 | ✓ | ✓ | ✓ | — |
| SAP OData Authentication method: username and password |
✓ | ✓ 8 | ✓ | — | — | — |
| SingleStoreDB | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Snowflake | ✓ | ✓ 1 | ✓ | ✓ | ✓ | — |
| Teradata | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Notes:
1 Advanced analysis is not supported for this data source.
2 With Data Virtualization Manager for z/OS, you add data and COBOL copybooks assets from mainframe systems to catalogs in IBM Cloud Pak for Data. Copybooks are files that describe the data structure of a COBOL program. Data Virtualization Manager for z/OS helps you create virtual tables and views from COBOL copybook maps. You can then use these virtual tables and views to import and catalog mainframe data from mainframes into IBM Cloud Pak for Data in the form of data assets and COBOL copybook assets.
The following types of COBOL copybook maps are not imported: ACI, Catalog, Natural
When the import is finished, you can go to the catalog to review the imported assets, including the COBOL copybook maps, virtual tables, and views. You can use these assets in the same ways as other assets in Cloud Pak for Data.
For more information, see Adding COBOL copybook assets.
3 Table and column descriptions are imported only if the connection is configured with one of the following Metadata discovery options:
- No synonyms
- Remarks and synonyms
4 Some objects in the SFORCE schema are not supported. See Salesforce.com.
5 To create metadata-enrichment output tables in Apache Hive at an earlier version than 3.0.0, you must apply the workaround described in Writing metadata enrichment output to an earlier version of Apache Hive than 3.0.0.
6 Output tables for advanced profiling: If you rerun advanced profiling at too short intervals, results might accumulate because the data might not be updated fast enough in Google BigQuery. wait at least 90 minutes before rerunning advanced profiling with the same output target. For more information, see Stream data availability. Alternatively, you can define a different output table.
7 Hive Metastore and Unity catalog
8 Information whether the data asset is a table or a view cannot be retrieved and is thus not shown in the enrichment results.
Learn more
- Importing metadata
- Enriching your data assets
- Creating rules from data quality definitions
- Creating SQL-based rules
Parent topic: Curation