Installing and configuring Hadoop clusters: This skill involves setting up and configuring multiple machines to work together as a distributed cluster, with Hadoop software installed on each node. It requires knowledge of system administration, networking, and understanding the Hadoop ecosystem.
Core Hadoop architecture (HDFS; YARN; MapReduce): This skill covers the fundamental components of Hadoop, including the Hadoop Distributed File System (HDFS) which provides reliable storage, YARN for resource management and job scheduling, and MapReduce for processing large datasets in parallel. Understanding this architecture is crucial for designing and optimizing Hadoop applications.
Writing efficient Hive and Pig queries: This skill involves writing queries in Hive and Pig, which are high-level languages used for processing and analyzing data in Hadoop. Writing efficient queries is important for achieving optimal performance and extracting valuable insights from large datasets.
Publishing data to clusters: This skill requires the ability to import data from external sources into Hadoop clusters, ensuring data is properly formatted and optimized for storage. It also involves managing data replication, compression, and security to ensure data integrity and availability within the cluster.
Handling streaming data: This skill involves processing and analyzing real-time streaming data in Hadoop. It requires knowledge of technologies like Apache Kafka or Apache Storm to ingest and process data as it is generated, enabling real-time analytics and decision-making.
Working with different file formats: This skill involves working with various file formats, such as CSV, JSON, Avro, or Parquet, in Hadoop. It requires understanding file formats and their specific advantages and trade-offs for different use cases, as well as knowledge of tools to process and transform data in different formats.
Troubleshooting and monitoring: This skill is about identifying and resolving issues in Hadoop clusters, such as performance bottlenecks, data inconsistencies, or configuration problems. It involves using various monitoring tools and diagnostic techniques to ensure the smooth operation of the Hadoop environment.