Like most other modern data integration systems, StreamSets supports log-based CDC.
It is most useful to use when you’re not worried about maintaining a history of all the changes to your database. Kicks off a trigger after every operationĬompares two versions of the data with queriesĬDC looks the most like Type 1 Slowly Changing Dimensions as overwriting new data as it appears. Query-based CDC involves using queries to find differences between datasets and can be untenable with larger datasets as it can require much more resources to perform this comparison. Log-based CDC is considered to be more efficient than a trigger CDC method.
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Essentially, however, log-based CDC updates a log for every INSERT, UPDATE or DELETE and reads that information when it is time to insert into the target database, while trigger CDC kicks off a trigger every operation with the same result. Differences that are explored elsewhere in our blogs in detail. There are actually three different ways of performing CDC : log-based, query-based, and trigger based. By sipping into your target database just the delta or changed data you get a much more streamlined process.
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Very often, the alternative to CDC is a full load from one table to another resulting in a very costly and time consuming operation. What Is Change Data Capture?ĬDC is a method of detecting and extracting new or updated records in a source and loading just this new information into your destination. Check out a few SCD patterns to see examples of how they can help you manage customer records. StreamSets supports both type 1 and type 2 Slowly Changing Dimensions. Stores two versions per record: a previousįor a modern data integration tool to be considered truly modern support for SCD is key. History is maintained, new data is inserted as new rows In short, Type 1 stores no historical data, Type 2 stores all historical data, and Type 3 stores limited historical data. the data that would’ve been overwritten in Type 1). In Type 3, one column is designated for storing previous data (i.e. In Type 2, new data are inserted as new records and the data that would have been overwritten are flagged as inactive or closed with effective time and expiration time assigned to the change to maintain a history. In Type 1, any new data that is ingested overwrites existing data. SCD types 4, 5, and 6 are inefficient and overly complicated for maintaining a history of all changes or overwriting old data, which are the two essential purposes of Slowly Changing Dimensions. There are actually six types of SCD with the most common being Type 1, Type 2 and Type 3. What Are Slowly Changing Dimensions (SCD)?
The difference between the two is almost entirely about what happens in the target database to the data. While some might observe that the difference between slowly changing dimensions (SCD) And Change Data Capture (CDC) might be subtle, there is in fact a technical difference between the two processes.īoth processes detect changes in a source database and deliver the changed data to a target database.
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