Summary
In this chapter, we dived into a comprehensive exploration of streaming data engineering tasks and real-world use cases. We began by understanding our use case and data and planning the development of our data engineering process specific to the requirements and context of our use case. We learned how to ingest streaming data from sources such as Kafka, ensuring that data is continuously and reliably streamed into our pipeline. This included handling critical aspects such as configuring Kafka consumers, securing the data transfer, and managing data loss.
The next step was transforming the data. We discovered how to take raw data from our source, process it, and shape it into a more usable format. This transformation involved not only converting data types but also performing operations such as deduplication, aggregation, and timestamp manipulation. These transformations are essential for preparing data for analytical or reporting purposes.
After transforming the data...
