Name: CCA 159 - Data Analyst using Sqoop, Hive and Impala
Rating: 4.6 (558 reviews)

What you'll learn

Data Ingestion using Apache Sqoop
Writing Queries using Apache Hive
Using Impala to execute Hive Queries
Prepare for CCA 159 Data Analyst Certification Exam

Curated for the Udemy Business collection

Requirements

A 64 bit Computer with at least 8 GB RAM is highly desired
Access to Multinode Cluster or our ITVersity Labs (Paid Subscription Required)
Setup Cloudera QuickStart VM in high end laptops (16 GB RAM and Quad Core) - Instructions Provided but Not Supported
Basic Computer Skills
Ability to write based SQL Queries and use Linux based environment

Description

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.

Who this course is for:

Any Big Data Professional or Aspirant who want to learn about Databases and Query Interfaces in Big Data
Any Business Intelligence Professional who want to understand Data Analysis in Big Data eco system
Any IT Professional who want to prepared for CCA 159 Data Analyst exam

Featured review

Mohamed Fyrose

47 courses

10 reviews

2 years ago

Very well organized and provides lots of practical knowledge. Good thing about this course is instructor is typing the queries and commands and troubleshooting the issues rather than showing powerpoint slide. I really enjoyed this and I am able to complete this course in short time. Very easy to follow up if you have ITVERSITY labs subscription.

19 sections • 233 lectures • 20h 31m total length

Preview04:16
Tools for preparation
02:32
Getting Details about the Exam
06:40
Signing up for the Exam
02:26

Preview05:57
Preview08:51
Hadoop Distributed File System - Quick Overview
03:54
Distributed Computing using YARN and Map Reduce 2 - Quick Overview
06:06
Submitting Map Reduce Job in YARN Framework
04:06
Determining Number of Mappers and Reducers
04:28
Understanding YARN and Map Reduce Configuration Properties
05:58
Reviewing and Overriding Map Reduce Job Run Time Properties
08:32
Reviewing Map Reduce Job Logs - using Resource Manager and Job History Server UI
06:34
Map Reduce Job Counters
05:11
Overview of Hive
03:40
Databases in Big Data and Query Engines
02:34
Overview of Data Ingestion Tools in Big Data
04:25

Preview01:47
Overview of HDFS and Properties Files
10:18
Overview of "hadoop fs" or "hdfs dfs" command
05:16
Listing Files in HDFS
06:42
User Spaces or Home Directories in HDFS
04:47
Creating Directory in HDFS
05:27
Copying Files and Directories into HDFS
08:02
File and Directory Permissions Overview
04:16
Getting Files and Directories from HDFS
04:37
Previewing Text Files in HDFS - cat and tail
03:46
Copying or Moving Files from one HDFS location to other HDFS location
05:41
Understanding Size of the File System and Data Sets - using df and du
05:01
Overview of Block Size and Replication Factor
05:41
Getting metadata of files using "hdfs fsck"
04:55
Resources and Exercises
03:57

Overview of Hive Language Manual
04:44
Launching and Using Hive CLI
06:28
Overview of Hive Properties - SET and .hiverc
07:28
Hive CLI History and .hiverc
04:55
Running HDFS Commands using Hive CLI
02:28
Understanding Warehouse Directory
03:56
Creating Database in Hive and Switching to the Database
03:42
Creating First Table in Hive and list the tables
06:52
Retrieve metadata of Hive Tables using DESCRIBE (EXTENDED and FORMATTED)
03:45
Role of Hive Metastore
05:04
Overview of beeline - Alternative to Hive CLI
04:41
Running Hive Queries using Beeline
04:50

Create tables in Hive - orders
09:35
Overview of Data Types in Hive
04:40
Adding Comments to Columns and Tables
03:05
Loading Data into Hive Tables from Local File System
09:29
Loading Data into Hive Tables from HDFS Location
05:51
Loading Data into Hive Tables - Overwrite vs. Append
03:26
Creating External Tables in Hive
04:40
Specifying Location for Hive Tables
06:44
Managed Tables vs. External Tables
03:51
Default Delimiters in Hive Tables using Text File Format
07:32
Overview of File Formats - STORED AS Clause
02:47
Differences between Hive and RDBMS
04:43
Truncating and Dropping tables in Hive
07:59
Resources and Exercises
04:11

Introduction to Partitioning and Bucketing in Hive
02:35
Creating Tables using orc File Format - order_items
06:42
Inserting Data into order_items using stage table
05:04
Can we use LOAD Command to get data into order_items with orc file format?
09:28
Creating Partitioned Tables in Hive - orders_part with order_month as key
05:51
Adding Partitions to Tables in Hive
04:44
Loading into Partitions in Hive Tables
08:40
Inserting Data into Partitions in Hive Tables
04:20
Inserting data into Partitioned Tables - Using dynamic partition mode
06:56
Creating Bucketed Tables - orders_buck and order_items_buck
03:33
Inserting Data Into Bucketed Tables
04:35
Bucketing with Sorting
04:13
Overview of ACID Transactions in Hive
04:19
Create Tables for ACID Transactions
08:04
Inserting individual records into Hive Tables
07:56
Updating and Deleting data in Hive Bucketed Tables
07:50

Overview of Functions
03:05
Validating Functions
03:23
String Manipulation - Case Conversion and Length
04:19
String Manipulation - substr and split
07:41
String Manipulation - trimming and padding Functions
06:27
String Manipulation - Reverse and Concatenating multiple strings
06:32
Date Manipulation - Getting Current Date and Timestamp
02:17
Date Manipulation - Date Arithmetic
05:00
Date Manipulation - trunc
02:41
Date Manipulation - Extracting information using date_format
05:13
Date Manipulation - Extracting information using year, month, day etc
02:38
Date Manipulation - Dealing with Unix Timestamp
04:38
Overview of Numeric Functions
07:21
Type Cast Functions for Data Type Conversion
03:47
Handling null values using nvl
01:52
Query Example - Get Word Count
05:45

Overview of SQL
05:40
Hive Query - Execution Life Cycle
04:38
Reviewing Logs for Hive Queries
05:09
Projecting Data using SELECT and Overview of FROM Clause
04:18
Using CASE and WHEN as part of SELECT Clause
03:29
Projecting DISTINCT Values
04:45
Filtering Data using WHERE Clause
04:11
Boolean Operations such as OR and AND using multiple fields
07:03
Boolean OR vs. IN
03:50
Filtering data using LIKE Operator
03:31
Basic Aggregations using Aggregate Functions
03:41
Performing basic aggregations such as SUM, MIN, MAX etc using GROUP BY
08:05
Filtering post aggregation using HAVING
03:34
Global Sorting using ORDER BY
04:28
Overview of DISTRIBUTE BY
05:22
Sorting Data with in groups using DISTRIBUTE BY and SORT BY
12:06
Overview of CLUSTER BY
04:55

Durga Viswanatha Raju Gadiraju

Technology Adviser and Evangelist

4.4 Instructor Rating
9,993 Reviews
212,358 Students
16 Courses

13+ years of experience in executing complex projects using vast array of technologies including Big Data and Cloud.

ITVersity, Inc. - a US based organization to provide quality training for IT professionals and we have the track record of training hundreds of thousands of professionals globally.

Building IT career for people with required tools such as high quality material, labs, live support etc to upskill and cross skill is paramount for our organization.

At this time our training offerings are focused on following areas:

* Application Development using Python and SQL

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CCA 159 - Data Analyst using Sqoop, Hive and Impala

What you'll learn

Requirements

Description

Who this course is for:

Featured review

Course content

Instructors

CCA 159 - Data Analyst using Sqoop, Hive and Impala

What you'll learn

Requirements

Description

Who this course is for:

Featured review

Course content

Introduction4 lectures • 16min

Using Cloudera QuickStart VM8 lectures • 1hr 13min

Using ITVersity labs6 lectures • 20min

Overview of Big Data eco system13 lectures • 1hr 10min

Overview of HDFS Commands15 lectures • 1hr 20min

Apache Hive - Getting Started12 lectures • 59min

Apache Hive - Managing Tables in Hive14 lectures • 1hr 19min

Apache Hive - Managing Tables in Hive - Partitioning and Bucketing16 lectures • 1hr 35min

Apache Hive - Overview of Functions16 lectures • 1hr 13min

Apache Hive - Writing Basic Queries17 lectures • 1hr 29min

Instructors