As part of Sqoop, Hive, and Impala for Data Analysts (Formerly CCA 159), you will learn key skills such as Sqoop, Hive, and Impala.This comprehensive course covers all aspects of the certification with real-world examples and data sets.Overview of Big Data ecosystemOverview Of Distributions and Management ToolsProperties and Properties Files - General GuidelinesHadoop Distributed File SystemYARN and Map Reduce2Submitting Map ReduceJobDetermining Number of Mappers and ReducersUnderstanding YARN and Map Reduce Configuration PropertiesReview and Override Job PropertiesReviewing Map Reduce Job LogsMap Reduce Job CountersOverview of HiveDatabases and Query EnginesOverview of Data Ingestion in Big DataData Processing using SparkHDFS Commands to manage filesIntroduction to HDFS for Certification ExamsOverview of HDFS and PropertiesFilesOverview of Hadoop CLIListing Files in HDFSUser Spaces or Home Directories in HDFSCreating Directories in HDFSCopying Files and Directories into HDFSFile and Directory Permissions OverviewGetting Files and Directories from HDFSPreviewing Text Files in HDFSCopying or Moving Files and Directories within HDFSUnderstanding Size of File System and FilesOverview of Block Size and ReplicationFactorGetting File Metadata using hdfs fsckResources and ExercisesGetting Started with HiveOverview of Hive Language ManualLaunching and using Hive CLIOverview of Hive Properties
What you'll learn
Understanding of the Big Data ecosystem
Proficiency in using Sqoop for data transfer
Ability to write queries in Hive
Knowledge of Impala and its query engine
Experience with Hadoop Distributed File System (HDFS)
Familiarity with MapReduce job configuration
Competence in data processing using Spark
Course objectives
Equip students with practical skills in Sqoop, Hive, and Impala
Prepare students for real-world data analysis tasks
Familiarize students with the Hadoop ecosystem and data processing techniques