Apache Impala
CloudLabs
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Assignment
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Lifetime Access
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Course Overview
Our Impala training course prepares you to efficiently query your organization’s big data storage, whether HDFS or HBase, with ease and quick turnaround times. Learn how to perform analytics using SQL or any other BI tools used by your organization.With easy to follow step-by-step instructions, learn to install and configure Impala, start and stop services, and control data access. In our Impala training course, trainees also gain the skills to use DDL and DML syntax and query HIVE or Impala. With Cloudlabs, our virtual lab environment, practice creating and deleting tables, views and even delete DBs. You also learn the skills needed to operate on a partitioned table, enhance the performance of your query, and use different compression techniques.
At the end of the training, participants will be able to:
- Explain the architecture of Impala and explain its business use cases
- Install & configure Impala and integrate with your organization’s Hadoop ecosystem
- Configure Impala for data access and to manage metadata
- Query Impala or HIVE
- Partition tables, optimize performance
- Work with Hadoop clusters in existing file systems and types
Pre-requisite
- Knowledge of Apache Hadoop ecosystem and SQL is required.
- Basic understanding of database administration is good to have.
Duration
2 days
Course Outline
- What is Impala
- Benefits of Impala
- Exploratory Business Intelligence
- Impala Installation
- Starting and Stopping Impala
- Data Storage
- Managing Metadata Preview
- Controlling Access to Data Preview
- Impala Shell Commands and Interface
- Querying with Hive and Impala
- SQL Language Statements
- DDL Statements
- DML Statements
- CREATE DATABASE
- CREATE TABLE Preview
- CREATE TABLE – Examples Preview
- Internal and External Tables
- Loading Data into Impala Table
- ALTER TABLE
- DROP TABLE
- DROP DATABASE
- DESCRIBE Statement Preview
- EXPLAIN Statement Preview
- SHOW TABLE Statement
- INSERT Statement
- INSERT Statement – Examples
- SELECT Statement
- Data Type
- Operators Preview
- Functions
- CREATE VIEW in Impala
- Hive and Impala Query Syntax
- Data Storage and File Format
- Partitioning Tables Preview
- SQL Statements for Partitioned Tables
- File Format and Performance Considerations
- Choosing File Type and Compression Technique
- Working with Impala
- Impala Architecture Preview
- Impala Daemon
- Impala Statestore
- Impala Catalog Service
- Query Execution Flow in Impala
- User – Defined Functions Preview
- Hive UDFs with Impala
- Demo – UDF in Impala
- Improving Impala Performance