Understanding the condition of company operations depends on the business transactions that are stored in relational databases. Organizations require a method for analyzing data as it is created since it loses value rapidly over time. Organizations usually copy data-to-data warehouses for analysis in order to prevent interruptions to operational databases.
In cloud migrations, where data is constantly changing and stopping the apps that link to live databases is not an option, time-sensitive data replication is also a crucial factor.
Businesses utilized batch-based methods in the past to transfer data once or several times each day. Nevertheless, batch transfer adds delay and lowers the organization’s operational value.
For the near real-time transfer of data from relational databases (such as SQL Server or Oracle) to data warehouses, data lakes, or other databases, Change Data Capture (CDC) has become the go-to option. We will go over four distinct Change Data Capture techniques in this post, along with the reasons Change Data Capture is perfect for cloud migrations and business intelligence that happens almost instantly.
What exactly is Change Data Capture?
A software procedure called “Change Data Capture” finds and monitors modifications to data within a database. As new database events happen, CDC moves and processes data continually to deliver real-time or almost real-time data mobility.
Change Data Capture works well to enable low-latency, dependable, and scalable data replication in high-velocity data settings where choices are made quickly. Additionally, Change Data Capture is perfect for cloud migrations with no downtime.
The requirement to replicate data across various environments makes choosing the best change data capture strategy for your organization more important than ever, since over 80% of firms aim to employ multi-cloud strategies by 2025.
Change Data Capture for ETL
A data warehouse, database, or data lake receives data that has been extracted from several sources using the data integration process known as ETL (extract, transform, and load). Database queries (batch-based) or Change Data Capture (near real time) can be used to extract data.
Data is processed and transformed into the proper format for the intended destination during the transformation step. Modern ETL technologies replace disk-based processing with in-memory processing, enabling real-time data processing, enrichment, and analysis, while old ETL includes a delayed transformation step. Loading data into the intended destination is the last stage of ETL.
Change Data Capture Patterns
The CDC uses a variety of techniques to identify data changes. These are the most often-applied techniques:
- Timestamp-based
The time of the most recent modification can be reflected in a column that database designers can add to the table structure. Among other names for this column are LAST_UPDATED and LAST_MODIFIED. This field can be queried by downstream applications or systems to retrieve the entries that have been updated since the last execution time.
- Trigger-based
Trigger functions are supported by most databases. These are stored procedures that, upon the occurrence of a certain event on a table (such the INSERT, UPDATE, or DELETE of a record), are automatically carried out. To record any changes to the data, a trigger is required for every activity per table. These modifications to the data are kept in a different table within the same database, sometimes called an event table or shadow table. Developers can also incorporate messaging systems, which will allow these updates to be pushed to queues and subscribed to by pertinent target systems.
- Log-based
Transactional databases record all modifications made to the database, including INSERT, UPDATE, and DELETE actions, together with the related timestamps, in files known as transaction logs. Although they may also be used to spread changes to target systems, these logs are mostly utilised for disaster recovery and backup reasons. Real-time data changes are recorded. This approach spares source databases from the computational burden of reading transaction logs, as target systems are able to access them.
Best Practices for Change Data Capture
In order to successfully deploy CDC, businesses need adhere to basic best practices to guarantee that their data is accurate, dependable, and performs well. Among the CDC’s recommended practices are the following:
- Recognize the data you need:
Recognize your needs for data integration before implementing CDC. Describe in detail the latency requirements, objectives, frequency of updates, and data sources. This will facilitate the decision-making process and assist you in choosing the best CDC design and approach.
- Choose the appropriate CDC technique:
Select a CDC approach that makes sense for your needs and particular use scenarios. Think about things like data volume, performance, and source system capabilities before choosing a solution.
- Include protocols for monitoring and logging:
Make sure you have enough systems in place to monitor the effectiveness and caliber of the CDC tools. It is also a good idea to set up alerts for mistakes and abnormalities in the data.
- Keep in mind the performance and scalability capacities:
Ensure that the design of your CDC is strong enough to support scalability and manage growing amounts of data. Companies choose for load balancing, query speed optimization, and horizontal scaling when dealing with large datasets.
The Synergy of CDC and Streaming ETL
The integration of Change Data Capture with Streaming ETL brings forth a powerful synergy that elevates the capabilities of data processing to new heights. Here is how this collaboration unfolds:
- Real-time Data Availability:
By leveraging CDC in streaming ETL workflows, organizations can ensure that only the changed data is processed and analyzed in real-time. This minimizes latency, providing decision-makers with up-to-the-moment insights into critical business operations.
- Reduced Resource Consumption:
Traditional batch ETL processes often involve the extraction and processing of large volumes of data, leading to resource-intensive operations. CDC, by capturing only changes, reduces the data payload, resulting in optimized resource utilization and lower operational costs.
- Enhanced Data Accuracy:
Real-time processing of changed data means that analytical models and reports are based on the most current information available. This not only improves decision-making accuracy but also ensures that organizations are operating with the latest insights.
- Scalability and Flexibility:
The combination of CDC and Streaming ETL provides scalability to meet the demands of growing data volumes. This flexibility allows organizations to adapt to changing business requirements and seamlessly integrate new data sources into their analytics ecosystem.
- Event-Driven Architecture:
Streaming ETL with CDC is inherently aligned with an event-driven architecture, where data changes trigger actions in real-time. This event-driven approach ensures that organizations can respond promptly to business events, automate workflows, and maintain a competitive edge in dynamic markets.
Conclusion
Change data capture is not merely an advanced technological tool. CDC is a competitive advantage for many progressive companies. Companies using CDC may move at the speed of their data and outpace the vast majority of enterprises that are still using batch processing by remaining several steps ahead of the competition.
