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Lately, the phrase "ETL is dead" has become more popular. But that statement is flatly false. It should rather have been "Batch ETL is growing unpopular". Companies now believe not only in the power of data but also in the power of current-ness of data. This means that a dashboard that reveals sales pattern for yesterday is less correct than one that shows sales pattern in the last 30 minutes.
Kafka is a scalable and distributed streaming and messaging platform is a great choice for building today's ETL pipeline.
In this big data kafka project, we will see this in theory as well as implementation. We will see how data ingestion and loading is done with Kafka connect APIs while transformation will be done with Kafka Streaming API. But this is not all.
The goal of this IoT project is to build an argument for generalized streaming architecture for reactive data ingestion based on a microservice architecture.
The goal of this apache kafka project is to process log entries from applications in real-time using Kafka for the streaming architecture in a microservice sense.
In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.