Apache Impala Introduction Tutorial

This is the introductory lesson of the Impala tutorial, which is part of the ‘Impala Training Course.’ This lesson will give you an overview of the tutorial, its prerequisites, and the value it will offer to you.

What is Apache Impala?

Apache Impala is a massively parallel processing (MPP) SQL (Pronounce as Sequel) query execution engine that runs on the Hadoop platform.

Using Impala:

  • You can run a query, evaluate the results immediately, and fine-tune the query, if necessary. This engine was introduced in October 2012 with a public beta test distribution, and the final version was made available in May 2013.
  • Analysts and data scientists use Impala to analyze Hadoop data via SQL or other business intelligence tools.

Using Impala’s MPP style execution along with other Hadoop processing MapReduce frameworks, you can perform interactive, ad-hoc and batch queries together in the Hadoop system.

Objectives of Impala Tutorial

By the end of this Impala tutorial, you will be able to:

  • Describe Impala and its role in Hadoop Ecosystem

  • Explain how to query data using Impala SQL

  • Discuss partitioning of Impala tables and explain its benefits

  • List the factors affecting the performance of Impala

  • Describe the complete flow of a SQL query execution in Impala.

Let us take a look at the lessons covered in the Impala Tutorial in the next section.

​Let us explore the benefits of an Impala Tutorial to professionals and organizations in the next section.

Benefits of Apache Impala Tutorial for Professionals and Organizations

The Impala Tutorial is beneficial for the professionals who want to manage and query large and complex data in real time using SQL and familiar scripting languages.

Value to Professionals:

Professionals with knowledge of Impala can interactively query data on big data in Apache Hadoop. Impala professionals will be in high demand in all the leading organizations worldwide, as Impala makes analytics for any data accessible to analysts.

Value to Organizations:

Organizations are adopting Impala to improve their speed and accuracy of analyzing big data. Many organizations have already invested a huge amount of money and time in creating a vast pool of SQL developers, Database professionals, and data warehouse specialists. These people can be trained to manage big data using Impala.

Let us understand the difference between Impala and Hive Querying, in the next section.

Impala vs Hive SQL for Hadoop Platform

It is important to know how Impala is different from Hive. In some cases, Impala SQL and Hive SQL use similar SQL statements and clause names. However, the semantics of Impala SQL statements are different from Hive SQL as shown below:

Impala SQL

Hive SQL

  • For query hints, Impala uses different syntax and names, such as [SHUFFLE] and [NONSHUFFLE].

  • Hive does not use syntax


  • Does not expose MapReduce specific features of SORT BY, DISTRIBUTE BY, or CLUSTER BY.

  • Exposes MAPReduce specific features.

  • For Impala Queries, FROM clause is not mandatory.

  • Require queries to include a FROM clause

  • Does not implicitly cast between string and numeric or Boolean values.

  • Implicit casts between string and numeric or Boolean values.

  • Performs implicit casts among the numeric types

  • Does not perform implicit casts among the numeric types.

  • Performs implicit casts from string to timestamp and has a restricted set of literal formats for the TIMESTAMP data type and the from_unixtime format string.

  • Does not perform implicit casts from string to timestamp.

Let us understand what the prerequisites for Impala are, in the next section.

Prerequisites for Impala Training

Fundamental Knowledge of programming language and Hadoop components is the basic prerequisite. However, participants are expected to know SQL commands as well.

Let us explore the Impala Tutorial Overview in the next section.

Apache Impala Tutorial Overview

This Impala Tutorial aims to provide:

  • a detailed introduction of Impala and its components

  • knowledge on Impala’s role in the Big Data Ecosystem, Structure Language Query statements, and partitioning tables.

  • an overview of the superior performance of Impala, against other popular SQL-on-Hadoop systems.

  • knowledge of how to execute a query in Impala.

In the next section, we will discuss the objectives of this Impala Tutorial.

Willing to take up a Course in Impala? Check out our course here!

Lessons Covered in this Apache Impala Tutorial

The table shown below gives you the snapshot of the four lessons covered in this Impala tutorial.

Lesson No

Chapter Name

What You’ll Learn

Lesson 1

Introduction to Impala

In this chapter, you’ll be able to:

● Describe Impala

● Explain the main benefits of Impala

● Describe the steps to install Impala

● Demonstrate how to get started with Impala

● Describe the functions of different Impala shell commands

Lesson 2

Querying with Hive and Impala

In this chapter, you’ll be able to:

● Discuss the SQL, DDL, and DML statements of Impala

● Explain how to query data using Impala SQL

● Recall how to use different SQL statements to perform CRUD operations in Impala

● Explain how to load data into Impala tables

● Differentiate between SQL statements in Hive and Impala

Lesson 3

Data Storage and File Format

In this chapter, you’ll be able to:

● Describe partitioning of Impala tables

● Explain the benefits of partitioning

● Describe how file format can affect performance in Impala

● List the various file formats that are supported in Impala.

Lesson 4

Working with Impala

In this chapter, you’ll be able to:

● Describe the Impala architecture

● Explain the functions of the three main architecture components

● Describe the complete flow of a SQL query execution in Impala

● Provide an overview of using user-defined functions in Impala

● List the factors that improve Impala performance.


In this introductory lesson, we covered the definition of Apache Impala, how it benefits the professionals and organizations, and gow Impala is different from Hive SQL.


With this, we come to an end about what this Impala Tutorial covers. In the next lesson, we will focus on ‘Introduction to Impala.’

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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