Understanding IBM Data Replication on C4PD change log scenario: A guide to track database schema drift

The importance of effective database management cannot be overstated. As databases continue to grow and evolve, it’s essential to track the changes made to the schema, tables and data. This is where a change log—a critical component of database management that helps ensure data consistency, integrity and recoverability—comes in.

Recently, IBM® Data Replication on IBM Cloud Pak® for Data (CP4D) has released a change log feature that enables users to track and record changes made to their database schemas and data. Let’s take a closer look at the change log, including what it is, how it works and why it’s essential for database replication and management.

IBM Data Replication on CP4D supports copy scenario and change log scenario. A copy scenario maintains an identical copy of the source table at the target database. While this approach can ensure data consistency, it doesn’t provide a record of changes made to the database over time.

A change log scenario maintains a history of all changes. This capability makes it an essential tool for tracking database schema evolution—by capturing and preserving every single change in the database and creating an audit trail.

A change log plays a critical role in enabling data lineage—it provides a detailed record of each change made to data over time. By analying the change log data, data stewards and 

• Reconstruct the history of changes made to data.
• Identify the sources of data and their transformations.
• Verify the accuracy and integrity of data.
• Improve data quality and regulatory compliance.

With the recent release of the IBM Data Replication on CP4D change log feature, users can easily track and record changes made to their database schema and data, ensuring data consistency, integrity and recoverability.

The IBM Data Replication on CP4D change log feature supports three types of Data Definition Language (DDL) change log, which gives users the flexibility to choose the approach that best fits their needs.

1. Log and apply DDL: This type applies schema changes without any attempt to retain the schema history. DDLs are replicated and the previous state is not preserved.
2. Schema evolution with new table: This type creates a new table in the target database every time, upon seeing a DDL with new structure. The previous table is preserved, and new upcoming data are replicated to the new table.
3. Schema evolution in same change log table: This type retains schema history by backing up columns within the table if a column attribute changes. If a table is dropped or renamed, the current state is backed up by renaming the table, and a new table is created with the new name.

The IBM Data Replication on CP4D change log feature allows users to specify extra metadata columns to include with each change log record, which provides a more comprehensive view of the changes made. The following change log columns can be included in replication:

• SEQUENCE: An inter-transaction sequence counter starting at 1.
• OPTYPE: A column that indicates the type of Data Manipulation Language (DML) operation.
• TRANSACTION_ID: A column that contains the transaction ID for each operation.
• TIMESTAMP: A column that contains the source commit timestamp for each operation.

Example: If we insert a row, then update and delete the same row, the state of the change log table looks like this:

The IBM Data Replication on CP4D change log creates the following metadata tables:

• change_log_ddl: A table that stores information about DDLs applied to the source database.
• change_log_txid_to_commit_timestamp: A table that stores the mapping between transaction IDs and commit timestamps.
• change_log_ddltype_to_description: A table that contains the description of the ddl_type mentioned in the change_log_ddl table.

Let’s look at how the change log might function in two real-world scenarios. 

At MyShop, our team is responsible for maintaining the company’s product database, which includes information about products, prices and inventory. It is essential to track any changes made to this database to support auditing and ensure data consistency.

As part of the MyShop team, we want to track all changes made to the product database—including DDL operations—to maintain data integrity. To achieve this goal, we use the IBM Data Replication on CP4D change log feature.

With this solution in place, MyShop can now monitor and record every change made to its databases effectively, enhancing transparency and auditability.

Healthcare companies handle sensitive patient data that is subject to strict regulatory requirements. Ensuring data integrity, traceability and compliance is critical to avoid legal penalties and maintain patient trust. Tracking every change made to patient records helps organizations demonstrate adherence to these regulations. The healthcare company uses the IBM Data Replication on CP4D change log to track all changes made to patient data. This change log captures and records every modification, ensuring that all data changes are documented.

The company successfully demonstrates compliance with regulatory requirements. The change log acts as a single source of truth for all data changes and simplifies tracking and troubleshooting of any data inconsistencies.

A change log is a critical component of database management that provides details of all changes made to a database schema, tables and data over time. With the recent release of the IBM Data Replication on CP4D change log feature, users can now easily track and record changes made to their database schema and data.

The change log feature helps ensure data consistency, integrity and recoverability while also providing a complete and auditable record of all changes made to their database.

By understanding the concepts and terminology surrounding change logs, data lineage and data provenance, data stewards and analysts can ensure the trustworthiness and transparency of their data assets.