In this chapter, we iteratively assembled an end-to-end data flow. We started by setting up an Nginx web server to create access logs. We also configured cron to execute a
logrotate configuration periodically to safely rotate old logs to a spooling directory.
Next, we installed and configured a single-node Elasticsearch server and tested some insertions and deletions. Then we configured a Flume client to read input from our spooling directory filled with web logs, and relay them to a Flume collector using compressed Avro serialization. The collector then relayed the incoming data to our Elasticsearch server.
Once we saw data flowing from one end to another, we set up a single-node HDFS server and modified our collector configuration to split the input data feed and relay a copy of the message to HDFS, simulating archival storage. Finally, we set up a Kibana UI in front of our Elasticsearch instance to provide an easy-search function for nontechnical consumers.
In the next chapter, we...