Description
Hadoop is a powerful open-source framework that allows for the processing and storage of large data sets across clusters of computers. It's designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop skills involve understanding its core components like MapReduce for processing large data sets, HDFS for high-throughput access to application data, and YARN for job scheduling. Proficiency also includes working with tools in the Hadoop ecosystem such as Hive, Pig, and Spark for data analysis, Sqoop and Flume for data loading, and HBase for NoSQL databases. Advanced skills include optimizing performance, securing clusters, and implementing complex business solutions.
Expected Behaviors
Fundamental Awareness
At the fundamental awareness level, an individual is expected to have a basic understanding of Big Data and its importance. They should be familiar with Hadoop and its ecosystem, including concepts like MapReduce and HDFS (Hadoop Distributed File System), and YARN (Yet Another Resource Negotiator).
Novice
A novice is expected to know how to install and configure Hadoop. They should be able to write basic MapReduce programs and load data using Sqoop and Flume. Basic data analysis using Hive and Pig should be within their skill set, as well as managing and monitoring Hadoop clusters.
Intermediate
An intermediate user should be proficient in advanced MapReduce programming and data processing using Spark. They should have an in-depth understanding of Hive and Pig for complex data analysis, and be comfortable working with HBase. Implementing ETL (Extract, Transform, Load) operations should also be within their capabilities.
Advanced
Advanced users are expected to optimize Hadoop MapReduce job performance and use Spark at an advanced level. They should be capable of designing and implementing real-time data processing using Storm, securing Hadoop clusters with Kerberos, and implementing complex business solutions using Hadoop ecosystem tools.
Expert
Experts should master Hadoop architecture and be able to design Hadoop applications. They should be proficient in writing, tuning, and optimizing Spark applications, and have a deep understanding of machine learning algorithms with Mahout. Managing and recovering from node failures, and designing, architecting, and implementing end-to-end Hadoop-based big data solutions should be within their expertise.