Big Data refers to large, complex datasets that traditional systems can’t handle. It’s important because analyzing this data helps businesses make better decisions, discover trends, and innovate.
Key components are HDFS for storage, MapReduce for processing, and YARN for resource management. Other tools like Hive (SQL queries) and HBase (NoSQL) help with data access.
They install, configure, monitor, and manage Hadoop clusters, ensuring data availability, security, and performance. They also handle troubleshooting, backups, and recovery.
A High Availability (HA) setup with active and standby NameNodes ensures automatic failover in case of failure, minimizing downtime and ensuring continued operation.
DataNodes store the actual data blocks in HDFS and handle client read/write requests. They regularly report block information to the NameNode.
Monitoring tools like Ambari, Cloudera Manager, and Ganglia provide metrics on performance, resource usage, and alerts to detect and resolve issues.
The replication factor determines how many copies of data blocks are stored across the cluster, providing fault tolerance and data redundancy. The default is three.
Securing a Hadoop cluster involves using Kerberos for authentication, encrypting data, implementing access controls, and regularly monitoring for vulnerabilities.
ZooKeeper manages coordination between distributed systems, ensuring services like leader election, synchronization, and configuration management in Hadoop clusters.
HDFS is a file system for storing large datasets, while HBase is a NoSQL database on top of HDFS, designed for real-time data access with random reads and writes.
Optimizations include tuning YARN settings, increasing HDFS block size, and using compression to reduce data size. Load balancing and regular monitoring help improve performance.
Tools like DistCp and HDFS snapshots are used for backups. Snapshots capture the system’s state at a given time, and DistCp copies data across clusters for disaster recovery.
The Secondary NameNode merges the NameNode’s edit logs with the FsImage to prevent logs from growing too large. It is not a backup but helps with recovery.
Challenges include managing scalability, ensuring data security, optimizing performance, and troubleshooting issues in real-time for large, distributed clusters.
YARN manages cluster resources and job scheduling, allowing multiple applications to run on the same cluster, improving resource utilization and efficiency.
Performance issues are identified through monitoring tools that detect bottlenecks in CPU, memory, or disk I/O. Log files and task distribution help diagnose problems.
Data locality ensures that processing tasks run on nodes where the data is stored, reducing network transfer and improving job performance.
HA is configured by setting up active and standby NameNodes with ZooKeeper managing failover, ensuring uninterrupted service if the active NameNode fails.
MapReduce processes data in batches, writing to disk between steps, while Spark processes in-memory, offering faster performance, especially for iterative tasks.
Hadoop Federation allows multiple independent NameNodes to manage separate namespaces, improving scalability for very large clusters.
