When processing data in near-real time, which challenge must data engineers address?

In the context of near-real-time data processing, the challenge of velocity is crucial. Velocity refers to the speed at which data is generated, processed, and analyzed. In near-real-time systems, data is typically streamed continuously rather than collected in batches, necessitating the ability to process and react to this influx of information almost instantaneously.

Data engineers must ensure that their systems can handle this rapid flow, which involves not only the ability to ingest large streams of data but also to perform computations and generate insights without significant delays. Technologies like stream processing frameworks (e.g., Apache Kafka, Apache Flink) are often employed to achieve the high throughput and low latency required by near-real-time applications.

While volume, variety, and integrity are also important considerations in data engineering, they do not capture the core challenge of managing the speed of data processing that characterizes near-real-time environments. Volume relates to the amount of data, variety addresses the different types of data, and integrity focuses on the accuracy and consistency of the data, all of which are important but secondary to the pressing need for speed in this specific context.