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Data Engineering: Incremental Data Loading Strategies
Outlining strategies and solution architectures to incrementally load data from various data sources.
The era of big data requires strategies to handle data efficiently and cost-effectively. Incremental data ingestion becomes the go-to solution when working with various and critical data sources generating data at a high velocity and low latency.
Years of serving as a data engineer and analyst working on integrating many data sources into enterprise data platforms, I managed to encounter one complexity after another when trying to incrementally ingest and load data into target data lakes and databases. Complexity shines when the data is of bits and pieces lying around the dust and in the corners of dear old legacy systems. Digging through those systems to find the golden interfaces, timestamps, and identifiers to hopefully enable seamless and incremental integration.
This is a common scenario where engineers and analysts are faced with when new data sources are needed for analytical use cases. Running a smooth data ingestion implementation is a craft, that many engineers and analysts aim to perfect. That is sometimes far-fetched and depending on the source systems, and the data they provide, things can get messy and complicated with…
