- Modern Big Data Processing with Hadoop
- V. Naresh Kumar Prashant Shindgikar
- 369字
- 2025-04-04 17:12:20
Best practices Hadoop deployment
Following are some best practices to be followed for Hadoop deployment:
- Start small: Like other software projects, an implementation Hadoop also involves risks and cost. It's always better to set up a small Hadoop cluster of four nodes. This small cluster can be set up as proof of concept (POC). Before using any Hadoop component, it can be added to the existing Hadoop POC cluster as proof of technology (POT). It allows the infrastructure and development team to understand big data project requirements. After successful completion of POC and POT, additional nodes can be added to the existing cluster.
- Hadoop cluster monitoring: Proper monitoring of the NameNode and all DataNodes is required to understand the health of the cluster. It helps to take corrective actions in the event of node problems. If a service goes down, timely action can help avoid big problems in the future. Setting up Gangalia and Nagios are popular choices to configure alerts and monitoring. In the case of the Hortonworks cluster, Ambari monitoring, and the Cloudera cluster, Cloudera (CDH) manager monitoring can be an easy setup.
- Automated deployment: Use of tools like Puppet or Chef is essential for Hadoop deployment. It becomes super easy and productive to deploy the Hadoop cluster with automated tools instead of manual deployment. Give importance to data analysis and data processing using available tools/components. Give preference to using Hive or Pig scripts for problem solving rather than writing heavy, custom MapReduce code. The goal should be to develop less and analyze more.
- Implementation of HA: While deciding about HA infrastructure and architecture, careful consideration should be given to any increase in demand and data growth. In the event of any failure or crash, the system should be able to recover itself or failover to another data center/site.
- Security: Data needs to be protected by creating users and groups, and mapping users to the groups. Setting appropriate permissions and enforcing strong passwords should lock each user group down.
- Data protection: The identification of sensitive data is critical before moving it to the Hadoop cluster. It's very important to understand privacy policies and government regulations for the better identification and mitigation of compliance exposure risks.