When creating real-time data platforms, data streaming is a low-latency, high-throughput method of moving data. Where batch processing methods necessarily introduce delays in order to gather a batch worth of data, stream processing methods act on steam events as they occur, with as little delay as possible. In this blog and associated repo, we will discuss how streaming data can be compatible with Cassandra, with Spark Structured Streaming as an intermediary. Cassandra is designed for high-volume interactions and, thus, a great resource for streaming workflows. For simplicity and speed, we are using DataStax’s AstraDB in this demo.
Introduction
Streaming data is normally incompatible with standard SQL and NoSQL databases since they can consist of differently structured data with messages only differentiated by timestamp. With advances in database technologies and continuous development, many databases have evolved to better accommodate streaming data use cases. Additionally, there are specialized databases, such as time-series databases and stream processing systems, that are designed explicitly for handling streaming data with high efficiency and low latency.