The Elastic stack and Apache Kafka share a tight-knit relationship in the log/event processing realm. A number of companies use Kafka as a transport layer for storing and processing large volumes of data. In many deployments we've seen in the field, Kafka plays an important role of staging data before making its way into Elasticsearch for fast search and analytical capabilities. I'd like to shine more light on how to set up and manage Kafka when integrating with the Elastic Stack. Specifically, we'll discuss our experiences operating Kafka and Logstash under high volume.
We will create a single data pipeline to process huge data with the help of kafka,logstash,apache monitor and kibana. Once we implement this we just need to put input files into source folder/directory and data will automatically get ingested into Elasticsearch and we can search/visualize data on ES web interface that is called Kibana.
Who is the target audience?
- Any one including students, professional who wants to learn real time data ingestion in elasticsearch. It's a very powerful approach to process any kind of data like log files etc and ingest into ES and then we can easily search on Kibana
- It's a very good tutorial and we can learn how we can ingest data on real time basis in Elasticsearch with the help of Apache Monitor,Kafka,Logstash,Elasticsearch and Kibana. We will create a single data pipeline which will ingest data into ES and then we can search/visualize data on Kibana
- Students would be able to learn real time data ingestion in elasticsearch through very popular tools ES,Kafka,Logstash,Kibana and Apache Monitor. It's a very powerful approach to process any kind of data like log files etc and ingest into ES and then we can easily search on Kibana. Students can also learn all the tools individually (setup,configuration etc) like kafka,es,kibana and then we will integrate all these tools to create single data pipeline