Impala is a MPP (Massive Parallel Processing) SQL query engine for processing huge volumes of data that is stored in Hadoop cluster. It is an open-source software which is written in C++ and Java. It provides high performance and low latency compared to other SQL engines for Hadoop.

In other words, Impala is the highest performing SQL engine (giving RDBMS-like experience) which provides the fastest way to access data that is stored in Hadoop Distributed File System.

Why Impala?

Impala combines the SQL support and multi-user performance of a traditional analytic database with the scalability and flexibility of Apache Hadoop, by utilizing standard components such as HDFS, HBase, Metastore, YARN, and Sentry.

With Impala, users can communicate with HDFS or HBase using SQL queries in a faster way compared to other SQL engines like Hive.
Impala can read almost all the file formats such as Parquet, Avro, RCFile used by Hadoop.

Impala uses the same metadata, SQL syntax (Hive SQL), ODBC driver, and user interface (Hue Beeswax) as Apache Hive, providing a familiar and unified platform for batch-oriented or real-time queries.

Unlike Apache Hive, Impala is not based on MapReduce algorithms. It implements a distributed architecture based on daemon processes that are responsible for all the aspects of query execution that run on the same machines.

Thus, it reduces the latency of utilizing MapReduce and this makes Impala faster than Apache Hive.

Advantages of Impala

Here is a list of some noted advantages of Cloudera Impala.

Using impala, you can process data that is stored in HDFS at lightning-fast speed with traditional SQL knowledge.
Since the data processing is carried where the data resides (on Hadoop cluster), data transformation and data movement is not required for data stored on Hadoop, while working with Impala.
Using Impala, you can access the data that is stored in HDFS, HBase, and Amazon s3 without the knowledge of Java (MapReduce jobs). You can access them with a basic idea of SQL queries.
To write queries in business tools, the data has to be gone through a complicated extract-transform-load (ETL) cycle. But, with Impala, this procedure is shortened. The time-consuming stages of loading & reorganizing is overcome with the new techniques such as exploratory data analysis & data discoverymaking the process faster.
Impala is pioneering the use of the Parquet file format, a columnar storage layout that is optimized for large-scale queries typical in data warehouse scenarios.

Features of Impala

Given below are the features of cloudera Impala −

Impala is available freely as open source under the Apache license.
Impala supports in-memory data processing, i.e., it accesses/analyzes data that is stored on Hadoop data nodes without data movement.
You can access data using Impala using SQL-like queries.
Impala provides faster access for the data in HDFS when compared to other SQL engines.
Using Impala, you can store data in storage systems like HDFS, Apache HBase, and Amazon s3.
You can integrate Impala with business intelligence tools like Tableau, Pentaho, Micro strategy, and Zoom data.
Impala supports various file formats such as, LZO, Sequence File, Avro, RCFile, and Parquet.
Impala uses metadata, ODBC driver, and SQL syntax from Apache Hive.

Relational Databases and Impala

Impala uses a Query language that is similar to SQL and HiveQL. The following table describes some of the key dfferences between SQL and Impala Query language.

Impala	Relational databases
Impala uses an SQL like query language that is similar to HiveQL.	Relational databases use SQL language.
In Impala, you cannot update or delete individual records.	In relational databases, it is possible to update or delete individual records.
Impala does not support transactions.	Relational databases support transactions.
Impala does not support indexing.	Relational databases support indexing.
Impala stores and manages large amounts of data (petabytes).	Relational databases handle smaller amounts of data (terabytes) when compared to Impala.

Hive, Hbase, and Impala

Though Cloudera Impala uses the same query language, metastore, and the user interface as Hive, it differs with Hive and HBase in certain aspects. The following table presents a comparative analysis among HBase, Hive, and Impala.

HBase	Hive	Impala
HBase is wide-column store database based on Apache Hadoop. It uses the concepts of BigTable.	Hive is a data warehouse software. Using this, we can access and manage large distributed datasets, built on Hadoop.	Impala is a tool to manage, analyze data that is stored on Hadoop.
The data model of HBase is wide column store.	Hive follows Relational model.	Impala follows Relational model.
HBase is developed using Java language.	Hive is developed using Java language.	Impala is developed using C++.
The data model of HBase is schema-free.	The data model of Hive is Schema-based.	The data model of Impala is Schema-based.
HBase provides Java, RESTful and, Thrift API’s.	Hive provides JDBC, ODBC, Thrift API’s.	Impala provides JDBC and ODBC API’s.
Supports programming languages like C, C#, C++, Groovy, Java PHP, Python, and Scala.	Supports programming languages like C++, Java, PHP, and Python.	Impala supports all languages supporting JDBC/ODBC.
HBase provides support for triggers.	Hive does not provide any support for triggers.	Impala does not provide any support for triggers.

All these three databases −

Are NOSQL databases.
Available as open source.
Support server-side scripting.
Follow ACID properties like Durability and Concurrency.
Use shardingfor partitioning.

Drawbacks of Impala

Some of the drawbacks of using Impala are as follows −

Impala does not provide any support for Serialization and Deserialization.
Impala can only read text files, not custom binary files.
Whenever new records/files are added to the data directory in HDFS, the table needs to be refreshed.

So, this brings us to the end of blog. This Tecklearn ‘Introduction to Impala , its features , advantages and disadvantages’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.

If you wish to learn Impala and build a career in Big Data or Hadoop domain, then check out our interactive, Big Data Hadoop-Architect (All in 1) Combo Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/bigdata-hadoop-architect-all-in-1-combo-course/

Big Data Hadoop-Architect (All in 1) Combo Training

About the Course

Tecklearn’s Big Data Hadoop-Architect (All in 1) combo includes the following Courses:

BigData Hadoop Analyst
BigData Hadoop Developer
BigData Hadoop Administrator
BigData Hadoop Tester
Big Data Security with Kerberos

Why Should you take BigData Hadoop Combo Training?

Average salary for a Hadoop Administrator ranges from approximately $104,528 to $141,391 per annum – Indeed.com
Average salary for a Spark and Hadoop Developer ranges from approximately $106,366 to $127,619 per annum – Indeed.com
Average salary for a Big Data Hadoop Analyst is $115,819– ZipRecruiter.com

What you will Learn in this Course?

Introduction

The Case for Apache Hadoop
Why Hadoop?
Core Hadoop Components
Fundamental Concepts

HDFS

HDFS Features
Writing and Reading Files
NameNode Memory Considerations
Overview of HDFS Security
Using the Namenode Web UI
Using the Hadoop File Shell

Getting Data into HDFS

Ingesting Data from External Sources with Flume
Ingesting Data from Relational Databases with Sqoop
REST Interfaces
Best Practices for Importing Data

YARN and MapReduce

What Is MapReduce?
Basic MapReduce Concepts
YARN Cluster Architecture
Resource Allocation
Failure Recovery
Using the YARN Web UI
MapReduce Version 1

Planning Your Hadoop Cluster

General Planning Considerations
Choosing the Right Hardware
Network Considerations
Configuring Nodes
Planning for Cluster Management

Hadoop Installation and Initial Configuration

Deployment Types
Installing Hadoop
Specifying the Hadoop Configuration
Performing Initial HDFS Configuration
Performing Initial YARN and MapReduce Configuration
Hadoop Logging

Installing and Configuring Hive, Impala, and Pig

Hive
Impala
Pig

Hadoop Clients

What is a Hadoop Client?
Installing and Configuring Hadoop Clients
Installing and Configuring Hue
Hue Authentication and Authorization

Cloudera Manager

The Motivation for Cloudera Manager
Cloudera Manager Features
Express and Enterprise Versions
Cloudera Manager Topology
Installing Cloudera Manager
Installing Hadoop Using Cloudera Manager
Performing Basic Administration Tasks Using Cloudera Manager

Advanced Cluster Configuration

Advanced Configuration Parameters
Configuring Hadoop Ports
Explicitly Including and Excluding Hosts
Configuring HDFS for Rack Awareness
Configuring HDFS High Availability

Hadoop Security

Why Hadoop Security Is Important
Hadoop’s Security System Concepts
What Kerberos Is and How it Works
Securing a Hadoop Cluster with Kerberos

Managing and Scheduling Jobs

Managing Running Jobs
Scheduling Hadoop Jobs
Configuring the Fair Scheduler
Impala Query Scheduling

Cluster Maintenance

Checking HDFS Status
Copying Data Between Clusters
Adding and Removing Cluster Nodes
Rebalancing the Cluster
Cluster Upgrading

Cluster Monitoring and Troubleshooting

General System Monitoring
Monitoring Hadoop Clusters
Common Troubleshooting Hadoop Clusters
Common Misconfigurations

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

Importing Relational Data with Apache Sqoop

Sqoop Overview
Basic Imports and Exports
Limiting Results
Improving Sqoop’s Performance
Sqoop 2

Introduction to Impala and Hive

Introduction to Impala and Hive
Why Use Impala and Hive?
Comparing Hive to Traditional Databases
Hive Use Cases

Modelling and Managing Data with Impala and Hive

Data Storage Overview
Creating Databases and Tables
Loading Data into Tables
HCatalog
Impala Metadata Caching

Data Formats

Selecting a File Format
Hadoop Tool Support for File Formats
Avro Schemas
Using Avro with Hive and Sqoop
Avro Schema Evolution
Compression

Data Partitioning

Partitioning Overview
Partitioning in Impala and Hive

Capturing Data with Apache Flume

What is Apache Flume?
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration

Spark Basics

What is Apache Spark?
Using the Spark Shell
RDDs (Resilient Distributed Datasets)
Functional Programming in Spark

Working with RDDs in Spark

A Closer Look at RDDs
Key-Value Pair RDDs
MapReduce
Other Pair RDD Operations

Writing and Deploying Spark Applications

Spark Applications vs. Spark Shell
Creating the SparkContext
Building a Spark Application (Scala and Java)
Running a Spark Application
The Spark Application Web UI
Configuring Spark Properties
Logging

Parallel Programming with Spark

Review: Spark on a Cluster
RDD Partitions
Partitioning of File-based RDDs
HDFS and Data Locality
Executing Parallel Operations
Stages and Tasks

Spark Caching and Persistence

RDD Lineage
Caching Overview
Distributed Persistence

Common Patterns in Spark Data Processing

Common Spark Use Cases
Iterative Algorithms in Spark
Graph Processing and Analysis
Machine Learning
Example: k-means

Preview: Spark SQL

Spark SQL and the SQL Context
Creating DataFrames
Transforming and Querying DataFrames
Saving DataFrames
Comparing Spark SQL with Impala

Hadoop Testing

Hadoop Application Testing
Roles and Responsibilities of Hadoop Testing Professional
Framework MRUnit for Testing of MapReduce Programs
Unit Testing
Test Execution
Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Big Data Testing

BigData Testing
Unit Testing
Integration Testing
Functional Testing
Non-Functional Testing
Golden Data Set

System Testing

Building and Set up
Testing SetUp
Solary Server
Non-Functional Testing
Longevity Testing
Volumetric Testing

Security Testing

Security Testing
Non-Functional Testing
Hadoop Cluster
Security-Authorization RBA
IBM Project

Automation Testing

Query Surge Tool

Oozie

Why Oozie
Installation Engine
Oozie Workflow Engine
Oozie security
Oozie Job Process
Oozie terminology
Oozie bundle

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