CCA 159 - Data Analyst using Sqoop, Hive and Impala

CCA 159 Data Analyst is one of the well-recognized Big Data certification. This scenario based certification exam demands in depth knowledge of Hive, Sqoop as well as basic knowledge of Impala.

This comprehensive course covers all aspects of the certification with real world examples and data sets.

• Overview of Big Data eco system

• HDFS Commands

• Creating Tables in Hive

• Loading/Inserting data into Hive tables

• Overview of functions in Hive

• Writing Basic Queries in Hive

• Joining Data Sets and Set Operations in Hive

• Windowing or Analytics Functions in Hive

• Importing data from MySQL to HDFS

• Performing Hive Import

• Exporting Data from HDFS/Hive to MySQL

• Submitting Sqoop Jobs and Incremental Imports

• and more

Here are the objectives for the certification.

Provide Structure to the Data

Use Data Definition Language (DDL) statements to create or alter structures in the metastore for use by Hive and Impala.

• Create tables using a variety of data types, delimiters, and file formats

• Create new tables using existing tables to define the schema

• Improve query performance by creating partitioned tables in the metastore

• Alter tables to modify the existing schema

• Create views in order to simplify queries

Data Analysis

Use Query Language (QL) statements in Hive and Impala to analyze data on the cluster.

• Prepare reports using SELECT commands including unions and subqueries

• Calculate aggregate statistics, such as sums and averages, during a query

• Create queries against multiple data sources by using join commands

• Transform the output format of queries by using built-in functions

• Perform queries across a group of rows using windowing functions

Exercises will be provided to prepare before attending the certification. Intention of the course is to boost the confidence to attend the certification.

All the demos are given on our state of the art Big Data cluster. If you do not have multi node cluster, you can sign up for our labs and practice on our multi node cluster.