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 workarounds and scripts here and there to patch things up.
In this story, I will outline a comprehensive overview of solutions for implementing incremental data ingestion strategies. Taking into consideration data source characteristics, data format, and properties of the data being ingested. The coming sections will focus on strategies to optimize incremental data loading therefore avoiding duplicate data records, reducing redundant data transfer, and decreasing load on operational source systems. We discuss high-level solution implementations and explain its components with the expected data flows. We list incremental strategies depending on data sources from Databases to File Storage and how to approach solutions for each. Let’s dive in.