Hadoop and its ecosystem apps like Hortonworks, Cloudera, Kafka, Spark, Hbase, Tensorflow, Druid and others are popular tools used in modern data analytics, AI and ML projects. However, deploying these apps typically starts with weeks of careful infrastructure planning to ensure good performance, ability to scale to meet anticipated growth and continued fault tolerance, as well as high availability of services.

Post deployment, the rigidity of the infrastructure poses operational challenges in adjusting resources to meet changing needs, patching, upgrades, and performance tuning of the analytics apps.

Download to read how you can deploy Hadoop in a Kubernetes-based environment:

  • Deploy, manage, and consolidate across any stage of your Big Data pipeline
  • Self-service deployment of Hadoop and its services with 1-click
  • Infrastructure consolidation and cost savings

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