Hadoop Big Data Performance Management


Rohit Choudhary


July 30, 2018

Hadoop Big Data Performance Management

Application Monitoring is a priority area for enterprises to effectively run their operations. The leaders such as Dynatrace, AppDynamics, New Relic, Splunk have allowed companies to manage their applications and web-scale successfully.

BigData based upon open source technologies such as — Spark, Kafka, Hive, HDFS powers large scale business critical applications. Big Data infrastructure management and optimization is a top priority for the CTO/CIO today. However, BigData does not have reliably integrated native APM & optimization solution. Enterprises are already spending significant amounts of their Big Data budget on getting the right kind of tools for monitoring the clusters. However, given that applications and platform adoption is ahead of the reliability curve that APM implementation is an afterthought.

However, APM and optimization can no longer can be an afterthought, it has to be planned well in advance in order to get the right amount of business benefits, along with de-risking because of excessive use of open source technology, which many a times get harder to manage, without lack of sufficient monitoring tools.

When you are running a large hardware infrastructure the costs start rise to very high levels before you know it. However, the reasons for that are not necessarily the lack of resources in your already present infrastructure, it’s also because there’s spare capacity that’s already available but the company is unable to manage and allocate the resources where it’s already needed. Hence it’s important to understand the goals of the company’s bigdata setup before you decide to invest in any new hardware or software.