In the modern world, moving at the speed of business means moving at the speed of data. Without accurate, reliable, available data, organizations will struggle to meet customer needs and make well-informed business decisions.
To make the most of valuable data insights, it’s crucial to use real-time tracking, recording, and analyzing of data points. Change data capture helps to streamline data modernization initiatives across environments without creating undue resource burdens.
What is Change Data Capture?
Change data capture (CDC) refers to a process of recognizing, tracking, and delivering data changes within a database. Effectively, CDC in data looks for shifts, and when it identifies one, it creates a record.
Tracking data movements in real-time is crucial for accuracy and analysis, and change data capture offers continuous tracking and recording. After tracking changes, records are delivered in real time to a downstream process or system.
The Benefits of Change Data Capture
When evaluating database change data capture, it’s crucial to understand what you hope to accomplish and what benefits it may serve your organization. CDC will support your data integration strategy, ensuring data is always fresh, while using fewer system resources to parse information and deliver results. CDC is highly beneficial in supporting real-time and near-real time data driven applications and services, where data accuracy and freshness is critical.
Cost Savings
Data is the lifeblood of an organization, enabling well-informed and wise decision-making. Storing, transferring, deciphering, and executing large swaths of data is costly financially, in time and human resources.
Change data capture loads incrementally and enables organizations to deliver data access services to digital applications, as well as actionable insights continuously, saving the otherwise costly investment and saving bandwidth. More than that, making business decisions based on real-time data and enabling real-time data-driven transactions contributes to cost savings and production optimization in manufacturing environments through predictive maintenance and improving production line efficiencies.
Increased Revenue
Data is valuable, but the adage is true: timing is everything. Holding data archives is helpful (and, in some cases, your legal or regulatory responsibility), yet your data shine when it’s fresh.
By providing teams with current data, your business can make timely decisions and take action based on real-time insights. CDC ensures your data is accessible and accurate. Data uploads are processed more quickly, and data is always reliable.
With on-demand data and insights, your organization has a competitive edge. The high-quality data available by CDC empowers your business to make swift, intelligent decisions to increase your revenues.
Efficiency
CDC increases business efficiency by eliminating the need for bulk load updating and defined batch windows. All change data capture methods efficiently move data across the networking, making it the perfect solution for cloud and hybrid environments.
It’s also well-suited for stream processing solutions and syncs data across multiple systems. The real-time nature of CDC enables database migrations with zero downtime. Organizations can access real-time analytics, reliable fraud protection, and data synchronization across networks and locations.
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See How!How Change Data Capture Works
Change data capture provides faster updates and efficient scaling by enabling continuous updates to stored and processed data. Select your change data capture method based on the needs of your organization.
Some approaches to CDC include:
Trigger-based CDC
In this method, database triggers are defined and used to initiate the capture of changes. Triggers are specific events, including a modification in the source table using SQL syntax. Developers have control over trigger-based CDC methodologies as these triggers can be defined based on the realistic needs of the business and are reliable for real-time capture of data changes.
A drawback to this method appears when tables are replicated as triggers are defined for each table. If triggers are disabled to facilitate other functions, CDC reliability will suffer. Trigger-based CDC creates a separate table for each triggered change record and places a record in the database transaction log. This can impact latency and burden the system with the additional load.
Script-based CDC
Instead of defining individual triggers, your organization can opt for script-based CDC. With this method, you build a script at the SQL level that tracks key fields within the database. Changes can be replicated in real-time or during a bulk upload.
Organizations with a fast-paced data environment or one that changes frequently may find this approach challenging. The script-based method looks at select fields, decreasing reliability if the schema changes. While less resource-intensive than other methods, retrieving data from the source database will load the system.
Log-based CDC
Many organizations opt for log-based CDC as it is the most effective and efficient approach. This method leverages the existing transaction log, a feature within enterprise databases, to facilitate reconstruction in case of a system crash or other disaster event.
Log-based CDC monitors the transaction log and, when changes occur, pushes a real-time update to the destination data warehouse. Built for consistency and dependability, transaction logs are reliable and thorough.
This log exists in a location separate from the database records and does not require additional resources or procedures for CDC. With continuous log-based CDC, organizations experience low latency while streaming changes to the destination in real-time.
It should be noted that log-based CDC will only work with databases that support the technology, as transaction log formats are proprietary.
