3 Real-World Change Data Capture Examples
Change data capture is a necessity. In order to ensure that the changes you’ve made over time in one data set are automatically transferred to a second data set, you must ensure that change data capture has taken place. There is even more than one method to carry out change data capture.
However, some methods work better than others. Read on to learn about three successful change capture examples.
What are the methods of change data capture?
There are several methods of change data capture. The first is time stamps or version numbers. It’s a widely used strategy – the system makes a note of the time or the version of the last change. However, there are some disadvantages to this method: the ability to timestamp has to be built into your database, it makes your database bloated, and it’s not always reliable.
The second method of change data capture is table triggers. When you make a change, subscribed users receive push notifications. While it’s a reliable and detailed method, there are some drawbacks; table triggers are a huge drag on your database resources, and there’s a negative impact on the performance of the applications that depend on the source database.
“Table triggers are reliable, yet they are a huge drag on database resources.”
Read our eBook
Streaming Legacy Data for Real-Time Insights
Learn more about the challenges to streaming legacy data and how Connect (Precisely’s change data capture solution) can help your business stream real-time application data from legacy systems to mission critical business applications and analytics platforms that demand the most up-to-date information for accurate insights.
Snapshots (also known as table comparisons) are the third method. As the name implies, they take snapshots of the database. Snapshots are a reliable and accurate method, yet you have to repeatedly move all of your data in monitored tables, which slows down performance. Moreover, there’s no complete record of intermediate changes between snapshot captures.
The fourth method, log scraping, involves software reading your logs. Log scraping doesn’t impact database performance, and changes are captured in real-time. At the same time, it’s based on the software’s ability to read the log, which is problematic – every log has a different format. Also, if there’s connectivity loss, you’ll wind up with lost or duplicated data.
3 change data capture examples
We’ll illustrate how data change capture works with three real-world examples.
- Dickey’s Barbecue Pit implemented Precisely Connect to modernize its antiquated Excel-based point-of-sales system analytics. Thanks to Connect, Dickey’s can make everyday operations data available to non-technical business users. Redshift is updated every 15-20 minutes so that users can gain quick, current business insights.
- Guardian Life Insurance used Connect to reduce time-to-market for an analytics project and to make data assets available to the whole enterprise. With Connect, Guardian Life was able to add new information to a data lake for each new project. Guardian Life now has centralized, standardized data assets that are searchable and accessible, which decreased its time-to-market and accelerated data acquisition.
- Connect was also the right solution for a global hotel chain seeking timely reporting and data collection on room availability, event bookings, and information from over 4,000 properties around the world. With Connect, the hotel now has near-real-time reporting capabilities, which allows managers to make better decisions and satisfy customers.
To learn more about Connect and how it can help with change data capture, read our eBook: Streaming Legacy Data for Real-Time Insights