Summary

In this chapter, we explained what a data stream is and gave related examples, as well as looking at the real-time use cases related to data streams. We got readers acquainted and introduced setup and quick execution for different real-time data ingestion tools like Flume, NiFi, Logstash, and Fluentd. We also explained where these data ingestion tools stand in terms of reliability and scalability. Then, we tried to compare the data ingestion tools so that the reader could pick the tools as per the need for their use case, after comparing pros and cons. They can run the examples by running the code bundled in JAR easily on standalone as well as in cluster mode. In the end, we gave the reader a real-time problem to solve using data ingestion tools along with pseudo code, so that we could focus on coding the example rather than finding right solution.

As we are now aware of different types of data streaming tools, in the next chapter we will focus on setting up Storm. Storm is an open source distributed, resilient, real-time processing engine. Setting up includes download, installation, configuration, and running an example to test whether setup is working or not.