Cdc change data capture11/30/2023 ![]() Execute ETL operations to move all the data changes in the OLTP system to data lake or data warehouse. ![]() Propagate changes to downstream subscribers, which is backend of other system in the organization.Tracking data changes for audit purposes.CDC functions enable the change data to be consumed easily and systematically.ĬDC is a widely used feature by enterprise customers for a variety of purposes: These change tables provide a historical view of the changes made over time to source tables. Changes are captured by using a capture process that reads changes from the transaction log and places them in corresponding change tables. You might find that you need to use CT in some places, and CDC in others, depending on the downstream processing of the data.We are excited to announce the public preview of change data capture (CDC) in Azure SQL Databases, a feature that has been requested by multiple customers.Ĭhange data capture (CDC) provides historical change information for a user table by capturing both the fact that Data Manipulation Language (DML) changes (insert / update / delete) were made and the changed data. ![]() Pick a table or two, enable one, test with some workload changes, then evaluate. However you really need to spend time working with both to make a decision about which one meets your needs. There’s an entry in BOL that compares them, and you should understand the differences. Which feature should you use? Is CT better than CDC? They work differently, and they capture different amounts of data. The amount can grow quickly in a busy database, so you need to be sure that you extract the information you need and prune the change tables periodically. This gives you lots of history and information about your table, but it’s a lot of data. Deletes get one row (old data) and updates get two rows (old and new data). For each DML operation, you get a row(s) added to the change table. You get a change table that is a copy of your table, along with a few additional columns that contain metadata. Change Data Capture (CDC)ĬDC is more well known, and seemed like a great tool when I first saw it, but like many useful enhancements, there is a bit of complexity that you have to work through in order to understand and use this feature.ĬDC is a little more complex to implement, and it creates a bit more data in your database. NET frameworks allow this, and it’s a great way to limit the load on your database server. This is really useful for those applications that cache data and periodically query to update their caches. ![]() You need to join this table with the source table to actually get the data. The queries you run will return a table that lets you know which rows have changed since the last check, and then let you know the type of DML change. This only lets you know that a particular row has changed since your last query. ![]() This is really a feature that allows the net changes made to data to be easily returned from a query. Change Tracking (CT)ĬT is not as well known as CDC, and I see it talked about less. They are similar, but there are some differences, and you might choose to use them in different situations. At first it seems like these two items ought to be synonyms, but they’re separate features. Change Tracking (CT) and Change Data Capture (CDC) were both added to SQL Server in 2008. ![]()
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