Introduction to Data Modeling in Hadoop

Introduction to Data Modeling in Hadoop

• start the course
• define data management
• recognize important data modeling concepts in Hadoop
• identify important issues for storing data in Hadoop
• recognize important considerations when designing HDFS schema
• recognize important points when designing HDFS schema
• identify basic concepts of data movement in Hadoop
• list important factors that need to be considered for importing data into Hadoop
• identify tools and methods for moving data into Hadoop
• recognize characteristics of a data stream
• define how data lakes enable batch processing
• define data security management and its major domains
• define Kerberos
• define basics of authentication in Hadoop using Kerberos
• identify central issues in processing and management of big data
• identify important points in Hadoop data modeling

Introduction to Hadoop

Introduction to Hadoop

• start the course
• recognize what Big Data is, sources and types of data, evolution and characteristics of Big Data, and use cases of Big Data
• identify Big Data infrastructure issues, and explain benefits of Hadoop
• recognize basics of Hadoop, history, milestones, and core components
• set up a virtual machine
• install Linux on a virtual machine
• recognize basic and most useful UNIX commands
• identify Hadoop components
• define HDFS components
• recognize how to read and write in HDFS
• use HDFS
• recognize basics of YARN
• define basics of MapReduce
• identify how MapReduce processes information
• use code that runs on Hadoop
• define Pig, HIVE, and HBase
• define Sqoop, Flume, Mahout, and Oozie
• recognize storing and modeling data in Hadoop
• identify available commercial distributions for Hadoop
• recognize Spark and its benefits over traditional MapReduce
• filter information in Hadoop