As we saw in the first part of this series, it is essential to monitor the health of our microservices, and to improve the tool we developed which helps us get metrics and forward them to an Elasticsearch instance. In this chapter we will pursue the question of how can we improve the processing of the result of the metrics endpoint.

In the previous post, we discussed how to read data in chunks from a relational database as a Spring Batch process using Spring Boot runner. This post will explain how to import that data into Elasticsearch.

Recently I had a customer project that required transferring large amounts of data from a relational database to the NoSQL database that is Elasticsearch in order to take advantage of its famous fast searching capabilities. Elasticsearch is part of the ELK stack that is released and maintained by Elastic.co. The abbreviation ELK stand for Elasticsearch, Logstash, and Kibana.

Stock price variabilities are nowadays even more affected by positive and negative news. New media channels like Twitter, Facebook, Finance Pages and News Pages, make the news rapidly available and accessible for everybody. It is an enormous amount of distributed data (Big Data), which could no longer be evaluated by a single individual. Powerful and lots of hardware along with suitable systems are needed to gather and interpret this mass of data. With Spring XD (Extreme Spring Data), such a system for big data interpretation and analytics is available. The aim of Spring XD is to facilitate…

Visualise the recording of our Webinar “Spring for the Elephant” (in German) and see how Hadoop and Spring are used to develop stunning BIG DATA Software applications. Apache Hadoop and its rapidly growing ecosystem (eg . As Pig , Hive , HBase , etc.) has enjoyed a lot of attention in recent time. We answer the following questions: