One post tagged with "change data capture"

The out-of-the-box dbt snapshots provide change data capture (CDC) capability for tracking the changes to data in your data lake or data warehouse. The dbt snapshot metadata columns enable a view of change to data - which records have been updated and when. However, the dbt snapshot metadata doesn't provide a view of the processing audit - which process or job was responsible for the changes. The ability to audit at the processing level requires additional operational metadata.

The out-of-the-box dbt snapshot strategies (rules for detecting changes) likely provide the desired logic for detecting and managing data change. No change to the snapshot strategies or snapshot pipeline processing is desired, but additional operational metadata fields must be set and carried through with the data.

Objectives​

Both operational and governance requirements can drive the need for greater fidelity of operational metadata. Example considerations could include:

• use of the out-of-the-box dbt snapshot logic and strategies for Change Data Capture (CDC)
• addition of operational metadata fields to snapshot tables with processing details for ops support and audit
  • when new records are inserted, add operational processing metadata information to each record
  • when existing records are closed or end-dated, update operational metadata fields with processing metadata

Aside from including a new process_id value in records, these enhancements don't add further information to the table. Instead they are a materialization of the operational data that is easier to access. The same information could be derived from standard dbt metadata fields but would require a more complex SQL statement that includes a left outer self-join. As with any materialization decision, there is a trade-off between ease of access vs. additional storage requirements.

NULL vs High-End Date/Timestamp​

In addition to the ops support and audit requirements, there can also be a legacy migration complication related to how open records (the most current version of the record) are represented in snapshots. dbt snapshots represent open records using NULL values for dbt_valid_to fields. In legacy data lakes or data warehouses, the open records often are identified using a well-known high value for the effective end date/timestamp, such as 9999-12-31 or 9999-12-31 23:59:59. Adding additional snapshot metadata columns enables a legacy view of record changes without having to alter the dbt snapshot strategy or processing logic.

datetime('9999-12-31 23:59:59.999999', 'Australia/Melbourne')

timestamp('9999-12-31 23:59:59.999999', 'Australia/Melbourne')

Enhancing the default Snapshot​

Modify the default dbt snapshot behavior by overriding the dbt snapshot materialization macros. dbt enbles macros to be overridden using the following resolution or search order:

1. locally defined macros in the project's ./macros directory
2. macros defined in additional dbt packages included in the project packages.yml file
3. dbt adaptor-specific macros
4. dbt provided default macros

To inject additional snapshot metadata fields into snapshot tables override the following two default macros:

• default__build_snapshot_table() creates the snapshot table on the first run
• default__snapshot_staging_table() stages in the inserts and updates to be applied to the snapshot table

To update fields on snapshot update, override the following default macro:

• default__snapshot_merge_sql() performs the MERGE/UPSERT

Note that if the dbt database adaptor implements adaptor-specific versions of these macros, then update the adaptor-specific macro accordingly. For example the dbt-spark adaptor overrides the dbt default__snapshot_merge_sql() as spark__snapshot_merge_sql().

build_snapshot_table()​

The default__build_snapshot_table() macro is called on the first dbt snapshot invocation. This macro defines the content to include in the CREATE TABLE statement. The following example adds process id's using the dbt invocation_id and additional timestamp fields, including use of the well-known high timestamp value for open records. This value is defined as the variable default_high_dttm in the dbt_project.yml file. The dbt snapshot strategy processing uses the unmodified standard dbt columns, so modification to change detection logic is not required.

snapshot_staging_table()​

The default__snapshot_staging_table() macro is called on subsequent dbt snapshot invocations. This macro defines the content in the MERGE statement for inserts and updates. The following example adds the additional operational metadata fields to the insertions common table expression (CTE) and the updates (CTE). The dbt invocation_id is used again as the process_id for inserts on new records and updates that close existing records.

Note that the deletes CTE has not been updated with the additional fields. In scenarios that use the hard deletes feature, the deletes CTE would need to be modified as well.

snapshot_merge_sql()​

The default__snapshot_merge_sql() macro is called to perform the MERGE/UPSERT into the target snapshot table. This macro defines how fields in the records being closed should be updated. The update set section of the MERGE statement defines the updated columns and values.

Conclusion​

Overriding the default dbt snapshot macros enables the injection and updating of additional operational metadata in snapshot tables. Fields can be added such that the provided dbt logic and snapshot strategy processing is still applied. Still, the resulting snapshot tables contain the columns required for the data lake or data warehouse.

The sample dbt project in datwiz/dbt-snapshot-metadata/tree/main/dbt_snapshot_ops_metadata contains an implementation of the snapshot customization.