There is a solid means through which changes can be detected (more on that in a moment).Querying the source data can be slow, due to the size of the data or the technical limitations.The size of the data source is relatively large.Here are a few factors which favor toward using an incremental pattern of loading: There are a lot of variables that can affect the speed, accuracy, and reliability of a load process, so you shouldn’t assume that the solution that worked last time is going to be a perfect fit in the next one. The decision to use an incremental or full load should be made on a case-by-case basis. ![]() Part of being a good data engineer is knowing when to use which load type. If you can’t easily identify in the source the changed data, you’ll have to choose from one of several not-as-good options for managing the incremental payload. There’s not always a clear way to identify new and changed data. That added complexity can be minimal or it can be very significant. With incremental loads, the developer must add additional load logic to find the new and changed data. A full load takes all the data in structure X and moves it to structure Y. We sometimes refer to a full load as a “dumb load”, because it’s an incredibly simple operation. Incremental logic tends to be more complex. However, there are some things to be aware of when considering this pattern. Incremental data loads are usually the preferred way to go in fact, this design pattern is on my inventory of ETL Best Practices. By only loading new and changed data, you can preserve in your destination data store all the source data, including that which has since been deleted from its upstream source. However, there may still be a need to report on that data in downstream systems. Many source systems purge old data periodically. Because incremental loads only move the delta, you can expect more consistent performance over time. If you run a full load, the time required to process is monotonically increasing because today’s load will always have more data than yesterday’s. Incremental load performance is usually steady over time. Data validation and change verification can also take less time with less data to review. Fractional load processes will add or modify less data, reducing the amount of data that might need to be corrected in the event of an anomaly. Any load process has the potential of failing or otherwise behaving incorrectly and leaving the destination data in an inconsistent state. If you touch half as much data, the run time is often reduced at a similar scale.īecause they touch less data, the surface area of risk for any given load is reduced. Assuming no bottlenecks, the time to move and transform data is proportional to the amount of data being touched. They typically run considerably faster since they touch less data. Incremental data loads have several advantages over full data load. Using an incremental load process to move and transform data has several benefits and a few drawbacks as well. In some cases, the new or changed data cannot be easily identified solely in the source, so it must be compared to the data already in the destination for the incremental data load to work properly. The selection of data to move is often temporal, based on when the data was created or most recently updated. The selectivity of the incremental design usually reduces the system overhead required for the ETL process. This differs from the conventional full data load, which copies the entire set of data from a given source. ![]() An incremental load pattern will attempt to identify the data that was created or modified since the last time the load process ran. What is an Incremental Load?Īn incremental load is the selective movement of data from one system to another. In this post, I’ll share what an incremental load is and why it is the ideal design for most ETL processes. This pattern of incremental loads usually presents the least amount of risk, takes less time to run, and preserves the historical accuracy of the data. When moving data in an extraction, transformation, and loading ( ETL) process, the most efficient design pattern is to touch only the data you must, copying just the data that was newly added or modified since the last load was run.
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