Authorised Cloudera Data Analyst Training | 4 Days
Training
In Bangalore
Description
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Type
Training
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Level
Beginner
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Location
Bangalore
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Duration
4 Days
Xebia is an official training partner of Cloudera, the leader in Apache Hadoop-based software and services.
This four days hands-on data analyst training, focusing on Apache Pig and Hive and Cloudera Impala, will teach you to apply traditional data analytics and business intelligence skills to Big Data.
Learn the tools data professionals need to access, manipulate, and analyze complex data sets using SQL and familiar scripting languages.
Facilities
Location
Start date
Start date
About this course
The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools,
How to apply the fundamentals of familiar scripting languages to the Hadoop cluster with Apache Pig.
You will have hands-on experience in:
Joining multiple data sets and analyzing disparate data with Pig,
Organizing data into tables, performing transformations, and simplifying complex queries with Hive,
Making multi-structures data accessible with Hive.
You will have the skills to:
Perform real-time interactive analyses on massive data sets stored in HDFS or HBase using SQL with Impala,
Pick the best analysis tool for a given task in Hadoop
Enable real-time interactive analysis of the data stored in Hadoop via a native SQL environment with Cloudera Impala.
This course is best suited to data analysts, business analysts, developers and administrators who have experience with SQL and basic UNIX or Linux commands.
Prior knowledge of Java and Apache Hadoop is not required.
Reviews
Subjects
- Apache Hadoop
- Distributed Data Processing: YARN
- MapReduce
- And Spark
- Data Processing and Analysis: Pig
- Hive
- And Impala
- Sqoop
Teachers and trainers (1)
Xebia Xebia
Trainer
Course programme
Course Outline: Introduction
- Hadoop Fundamentals
- The Motivation for Hadoop
- Hadoop Overview
- Data Storage: HDFS
- Distributed Data Processing: YARN, MapReduce, and Spark
- Data Processing and Analysis: Pig, Hive, and Impala
- Data Integration: Sqoop
- Other Hadoop Data Tools
- Exercise Scenarios Explanation
Introduction to Pig
- What Is Pig?
- Pig’s Features
- Pig Use Cases
- Interacting with Pig
Basic Data Analysis with Pig
- Pig Latin Syntax
- Loading Data
- Simple Data Types
- Field Definitions
- Data Output
- Viewing the Schema
- Filtering and Sorting Data
- Commonly-Used Functions
Processing Complex Data with Pig
- Storage Formats
- Complex/Nested Data Types
- Grouping
- Built-In Functions for Complex Data
- Iterating Grouped Data
Multi-Dataset Operations with Pig
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Set Operations
- Splitting Data Sets
Pig Troubleshooting and Optimization
- Troubleshooting Pig
- Logging
- Using Hadoop’s Web UI
- Data Sampling and Debugging
- Performance Overview
- Understanding the Execution Plan
- Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive and Impala
- What Is Hive?
- What Is Impala?
- Schema and Data Storage
- Comparing Hive to Traditional Databases
- Hive Use Cases
Querying with Hive and Impala
- Databases and Tables
- Basic Hive and Impala Query Language Syntax
- Data Types
- Differences Between Hive and Impala Query Syntax
- Using Hue to Execute Queries
- Using the Impala Shell
Data Management
- Data Storage
- Creating Databases and Tables
- Loading Data
- Altering Databases and Tables
- Simplifying Queries with Views
- Storing Query Results
Data Storage and Performance
- Partitioning Tables
- Choosing a File Format
- Managing Metadata
- Controlling Access to Data
Relational Data Analysis with Hive and Impala
- Joining Datasets
- Common Built-In Functions
- Aggregation and Windowing
Working with Impala
- How Impala Executes Queries
- Extending Impala with User-Defined Functions
- Improving Impala Performance
Analyzing Text and Complex Data with Hive
- Complex Values in Hive
- Using Regular Expressions in Hive
- Sentiment Analysis and N-Grams
- Conclusion
Hive Optimization
- Understanding Query Performance
- Controlling Job Execution Plan
- Bucketing
- Indexing Data
Extending Hive
- SerDes
- Data Transformation with Custom Scripts
- User-Defined Functions
- Parameterized Queries
Choosing the Best Tool for the Job
- Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
- Which to Choose?
Authorised Cloudera Data Analyst Training | 4 Days