Hadoop Training and Certification in Kuala Lumpur, Malaysia

Hadoop training provides learners with a deep understanding of the Hadoop ecosystem, focusing on distributed data processing and storage. It covers key components such as HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce for parallel data processing. Participants gain hands-on experience in setting up and configuring Hadoop clusters, managing large datasets, and performing analytics. The training also delves into related tools like Hive, Pig, and HBase, which are often used for querying, scripting, and managing data in Hadoop environments. By completing this course, professionals are equipped to design and deploy scalable, high-performance big data solutions in enterprise environments.

Introduction to Hadoop and Cluster Setup

• Introduction to Hadoop and Big Data Concepts:

• What is Big Data?

• Introduction to Hadoop and its role in handling Big Data.

• Overview of the Hadoop ecosystem and its key components (HDFS, YARN, MapReduce, etc.).

• Hands-on Exercise

• Hadoop Architecture and Components:

• HDFS (Hadoop Distributed File System): Architecture, blocks, replication, and storage management.

• YARN (Yet Another Resource Negotiator): Resource management and job scheduling.

• MapReduce: Distributed data processing using the MapReduce model.

• Hands-on Exercise

• Setting Up a Hadoop Cluster:

• Overview of the cluster setup process.

• Configuring Hadoop on a multi-node cluster.

• Installing and configuring Hadoop and its components (HDFS, YARN, etc.).

• Hands-on Exercise

Managing Hadoop Components and Data Storage

• HDFS Management:

• Understanding HDFS file structure, blocks, and directories.

• Managing files in HDFS (copying, moving, deleting files).

• Monitoring HDFS health and performance.

• Hands-on Exercise

• YARN Resource Management:

• Understanding YARN’s role in job scheduling and resource allocation.

• Configuring YARN ResourceManager and NodeManager.

• Managing and submitting jobs using YARN.

• Hands-on Exercise

• MapReduce Jobs and Job Monitoring:

• Overview of MapReduce job execution and how Hadoop processes data in parallel.

• Monitoring the progress and status of MapReduce jobs.

• Hands-on Exercise

Monitoring, Troubleshooting, and Security

• Cluster Monitoring and Performance Tuning:

• Using Hadoop’s web interfaces for monitoring (ResourceManager, JobHistory, NameNode).

• Monitoring cluster health, node status, and job performance.

• Analyzing logs for performance bottlenecks.

• Hands-on Exercise

• Troubleshooting Common Issues in Hadoop:

• Identifying and resolving common issues in Hadoop clusters (e.g., NameNode failures, DataNode crashes, slow job execution).

• Handling job failures and re-running jobs.

• Hands-on Exercise

• Security in Hadoop:

• Introduction to Hadoop security features (Kerberos authentication, access control lists).

• Configuring security in Hadoop clusters.

• Hands-on Exercise

• Best Practices for Hadoop Administration:

• Optimizing Hadoop performance (memory management, job optimization).

• Backup and recovery strategies in Hadoop.

• Hands-on Exercise