Learn Hadoop with Free Lessons & Tips

The main components of a Hadoop application are: 1. *Input*: - Data sources (e.g., files, databases, streams) - Data ingestion tools (e.g., Flume, Sqoop) 2. *Storage*: - HDFS (Hadoop Distributed File System) for storing data - HBase (optional) for NoSQL database capabilities 3. *Processing*: - MapReduce (MR) for batch processing and data transformation - Apache Spark (optional) for in-memory processing and machine learning - Apache Flink (optional) for stream processing 4. *Processing Framework*: - YARN (Yet Another Resource Negotiator) for resource management and job scheduling 5. *Data Processing Tools*: - Pig for high-level data processing and analysis - Hive for data warehousing and SQL-like queries 6. *Output*: - Data output formats (e.g., CSV, JSON, Avro) - Data output destinations (e.g., HDFS, HBase, external databases) 7. *Security and Governance*: - Authentication and authorization mechanisms (e.g., Kerberos, LDAP) - Data encryption and access control 8. *Monitoring and Maintenance*: - Monitoring tools (e.g., Ambari, Cloudera Manager) - Logging and debugging mechanisms These components work together to form a comprehensive Hadoop application, enabling scalable, flexible, and efficient data processing and analysis. read less