Streaming ETL and beyond
This first application will be an example of processing live streaming data with windowing and real-time visualization. The data source will be Twitter, processing of the tweet stream will compute the top hashtags in a time window as well as some counts that can be visualized as time series. The pattern is applicable to many similar use cases: data is continuously consumed from a streaming source and aggregated. Traditionally, results of such computation will land in a storage system (files, databases, and so on). Such processing can be broadly categorized as extract-transform-load (ETL) in streaming fashion. However, the focus here will be on stream processing that goes beyond the realm of general purpose ETL tools and can support streaming analytics use cases.
Stream processing needs a source of data, so every pipeline will involve the E of ETL with connector(s) to extract or ingest data (with Kafka being a common streaming source and files for batch use cases)...
