What are CDC (Change Data Capture) events?

Introduction

CDC (Change Data Capture) events refer to the process of identifying and capturing changes made to a database. The goal of CDC is to identify and capture all changes to the data, including inserts, updates, and deletes, in real-time or near real-time, in order to keep data in sync across multiple systems. This allows organizations to maintain a consistent and accurate view of their data, even as it changes over time.

CDC events are used in a variety of use cases, including data integration, data warehousing, and data replication. In data integration, CDC is used to keep data in sync between different systems, ensuring that changes made in one system are reflected in another. In data warehousing, CDC is used to keep a data warehouse up-to-date with changes made in source systems, allowing analysts to work with current and accurate data. In data replication, CDC is used to replicate data from one database to another, allowing organizations to maintain multiple copies of their data for backup and disaster recovery purposes.

There are several CDC techniques and technologies available, each with its own strengths and weaknesses. Some popular techniques include log-based CDC, trigger-based CDC, and triggerless CDC. Log-based CDC involves reading the transaction logs of a database to identify changes, while trigger-based CDC involves setting up triggers in a database to capture changes. Triggerless CDC, on the other hand, uses a specialized CDC tool to monitor changes to the database without relying on triggers.

When selecting a CDC technique, it’s important to consider factors such as the performance impact on the source database, the complexity of implementation, and the accuracy of the data capture. It’s also important to consider the specific requirements of the organization, such as the type of database, the frequency of changes, and the need for real-time data capture.

In conclusion, CDC events are

CDC (Change Data Capture) events are events that are generated when data in a database is changed. CDC events are typically used in data integration and data synchronization scenarios, where it is important to track and replicate changes made to a database.

CDC events can be generated by a variety of database systems, including relational databases, NoSQL databases, and others. They typically include information about the type of change that was made (e.g., insert, update, delete), as well as the specific data that was changed.

CDC events can be used in a number of different ways, including:

1. Data integration: CDC events can be used to replicate data changes made in one database to another database, either in real-time or in batch mode. This can be useful for integrating data from different systems or for creating data backup and recovery systems.
2. Data synchronization: CDC events can be used to synchronize data between different systems or between different instances of the same system. This can be useful for ensuring that data is consistent across different environments or for creating data replication and failover systems.
3. Event-driven architectures: CDC events can be used as the basis for building event-driven architectures, where changes to the database trigger downstream processes or actions.

CDC events are an important tool for data integration and data synchronization, and are widely used in a variety of scenarios where it is important to track and replicate changes made to a database.

Schema

The schema of a CDC event refers to the structure and format of the data that is captured as part of the CDC process. The schema typically includes information about the type of change that has been made (insert, update, delete), the time the change was made, and the data that has been affected.

The exact structure of the schema will depend on the specific CDC technology being used and the data source being monitored. However, a common schema for CDC events may include the following fields:

1. Operation type: The type of change that has been made to the data, such as insert, update, or delete.
2. Timestamp: The time the change was made, often in a format such as timestamp or datetime.
3. Table name: The name of the table in the database where the change was made.
4. Primary key: The unique identifier for the affected row in the database, used to identify the specific change that was made.
5. Column names and values: The names and values of the columns in the affected row that have been changed.
6. Before and after values: The values of the affected columns before and after the change was made.
7. Transaction ID: A unique identifier for the transaction in which the change was made, used for tracking changes over time.

It’s worth noting that the schema for CDC events can be customized to meet the specific requirements of an organization. The schema can be adjusted to include additional fields, such as information about the user who made the change or the application that initiated the change.