Replication, change data capture and analytics access
Many systems need mainframe data without changing where it lives. Change data capture copies committed changes as they happen; virtualization lets tools query the data in place; streaming and data lakes feed analytics. Each trades freshness, cost and load differently.
Why make mainframe data available
Analysts, data scientists and digital channels want customer and transaction data quickly and in familiar formats. Historically this meant nightly extract jobs that wrote files, converted them and sent them on, so data was a day old. Modern approaches aim to deliver it sooner while keeping the mainframe as the place where the data is actually maintained.
Change data capture
Change data capture (CDC) reads committed changes, usually from the database's recovery log, and sends them to a target such as another database, a message queue or a streaming platform. Because it reads the log, it adds little load to the application and needs no change to the COBOL programs.
- For Db2 for z/OS, tables to be captured are usually defined with
DATA CAPTURE CHANGESso the log holds full row images. - For IMS and VSAM, capture depends on the product and on how the data is logged; check what your product supports and what logging must be enabled.
- Products in this space include IBM Data Replication, Qlik Replicate and Precisely Connect, among others.
Options for analytics access
| Approach | How it works | Freshness | Watch out for |
|---|---|---|---|
| Batch extract | Scheduled jobs unload data to files | Hours to a day | Old data; conversion errors in file transfer |
| Replication (CDC) | Changes copied continuously to a target database | Seconds to minutes | Target schema drift; monitoring lag |
| Virtualization | Queries run against the data where it lives through a virtual layer | Live | Query load on production; cost of processor time |
| Streaming | Change events published to a platform such as Apache Kafka | Seconds | Event ordering, schema management, consumers falling behind |
| Data lake or warehouse | Data lands in a central analytics store, often fed by CDC or streaming | Depends on feed | Governance and copies multiplying |
Data virtualization (for example IBM Data Virtualization Manager for z/OS) presents VSAM, IMS, Db2 and other sources as if they were tables, so tools can query them with SQL without copying. It gives the freshest data but runs the work on the mainframe, so heavy analytical queries need control, often through WLM classification and offloading eligible work to zIIP processors where the product supports it.
Choosing an approach
- Need live, low-volume lookups? Virtualization or an API.
- Need near-real-time copies for many consumers? CDC into streaming.
- Need large historical analysis? CDC or batch into a data lake or warehouse.
- Need the data in a new application's own database? Replication, with clear rules that the copy is read-only.
Operating a CDC feed
A CDC pipeline is a production service. Teams monitor latency (how far the target is behind), errors on apply, and log retention: if capture stops for longer than the logs are retained (active and archive), it cannot catch up from the log and the target must be reloaded. Schema changes on the source, such as a new column, must be coordinated with the replication configuration.
Subscription CUSTFEED State ACTIVE Source DB2P.CUSTOMER Target kafka topic customer.changes Latency 4 sec Rows applied today 1,284,330 Errors 0 Last log position read 12:02:41
Common mistakes
If both copies are updated, they diverge. Replicas fed by CDC should be read-only.
Large queries consume production processor time. Classify the work in WLM and agree limits with capacity planning.
If capture stops for longer than logs are retained, the target needs a full reload. Monitor latency and alert early.
What you will see at work
- DBAs enable the logging options and authorities that capture products need on source tables.
- Support teams monitor replication latency and restart subscriptions after outages.
- Architects choose between virtualization, CDC and batch extracts based on freshness, volume and cost.
Key terms
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