Outline strategies and solution architectures for incrementally loading data from various data sources.
The era of big data requires strategies to process data efficiently and cost-effectively. Incremental data ingestion becomes the go-to solution when working with a variety of critical data sources that produce data at high speed and low latency.
Over the years as a data engineer and analyst, I've worked on integrating many data sources into enterprise data platforms, but when I try to incrementally ingest and load data into a target data lake or database, I keep getting hit with one error after another. and was able to face complexity. Complexity shines when data is bits and pieces lying around in the dust or in the corners of old legacy systems. Explore these systems to find golden interfaces, timestamps, and identifiers to enable seamless, gradual integration.
This is a common scenario faced by engineers and analysts when an analytical use case requires a new data source. Running a data ingestion implementation smoothly is an art, and many engineers and analysts aim to perfect it. It can be outlandish, and depending on the source system and the data it provides, it can require scripts with workarounds and patches here and there, making things messy and complicated.
This story provides a comprehensive overview of solutions for implementing an incremental data ingestion strategy. Consider the characteristics of the data source, data format, and properties of the data being ingested. The next section focuses on strategies to optimize incremental data loads, avoid duplicate data records, reduce redundant data transfers, and reduce the load on production source systems. Describes the high-level solution implementation and describes its components and expected data flows. We list incremental strategies depending on your data source, from databases to file storage, and how to approach each solution. Let's dive in.