9/17/2023 0 Comments 18 chris craft runaboutThese changes in transactional databases are usually tracked by writing to the database transaction log, often called a write-ahead log (WAL) in a directory of the database. Change Data Capture is often called log-based replication. Log-based CDC is then read in real-time and sent to the destination in the form of an Orders Table.ĭatabases such as PostgreSQL, MongoDB, and MySQL can be configured to track changes such as inserts, updates, and deletes. The above diagram shows Database DML (Inserts, Updates, Deletes) executed on the database, which is then sent to the transaction log. Adding log-based CDC to the streaming ETL process allows the target system to remain up-to-date with the latest data from the source system, enabling real-time analytics and decision-making.Īn open-source streaming ETL system you may be familiar with would be Apache Kafka. In a streaming ETL process, data is extracted from the source system and transformed as it is received, rather than being extracted and transformed in batch intervals. Streaming ETL (extract, transform, load) is a type of data integration process that involves continuously extracting data from various sources, transforming it to fit the needs of the destination system, and loading it into the destination system in near real-time. Combined with one-to-many mapping, log-based CDC is highly scalable and simplifies your architecture. The Change Data Capture real-time streaming approach to data integration also allows for high volume and velocity data replication that is both reliable and scalable, while using fewer resources. Taking this further, rather than only processing the changes as is, it is also possible to carry out in-stream processing such as generating new metrics, lookups, or removing PII data if needed. The above diagram shows Source Data being updated with DML statements (Inserts, Updates, Deletes) and these log-based CDC records being sent to a destination. Better than a simple 1-to-1 replication, it's also possible to read once but carry out multiple writes. What is Change Data Capture? Change Data Capture refers to the process of capturing changes made to data in a source system such as a database, so that these change events can be used in the destination system, such as a data warehouse, data lake, data app, machine learning models, indexes, or caches. This is an easy step to take to provide your organization with real-time data and gain many benefits. The popular destinations are data warehouses or data lakes such as Snowflake, BigQuery, Databricks, and Delta Lake. Typically, organizations begin their journey with Change Data Capture by wishing to track changed data from a source database using database transaction logs, and popular source databases are often PostgreSQL, MySQL, MongoDB, SQL Server & Oracle. For this blog, we will only look at Change Data Capture in the context of a streaming ETL via a transaction log and not other change data capture methods such as database triggers. We will take a look at what Change Data Capture (CDC) is, how it works, what are the benefits of streaming change events, the business benefits, and use cases.
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