Hive vs Impala – SQL War in the Hadoop Ecosystem

Hive vs Impala – SQL War in the Hadoop Ecosystem

Apache Hive is an effective standard for SQL-in-Hadoop. Hive is a front end for parsing SQL statements, generating logical plans, optimizing logical plans, translating them into physical plans which are executed by MapReduce jobs. Apache Hive is designed for the data warehouse system to ease the processing of adhoc queries on massive data sets stored in HDFS and ease data aggregations.

Impala is an open source SQL query engine developed after Google Dremel. Cloudera Impala is an SQL engine for processing the data stored in HBase and HDFS. Impala uses Hive megastore and can query the Hive tables directly. Unlike Hive, Impala does not translate the queries into MapReduce jobs but executes them natively.

For the complete list of big data companies and their salaries- CLICK HERE

However, both Apache Hive and Cloudera Impala support the common standard HiveQL.

Hive vs. Impala


Hive vs Impala SQL War in the Hadoop Ecosystem

  • Hive is slow but undoubtedly a great option for heavy ETL tasks where reliability plays a vital role, for instance the hourly log aggregations for advertising organizations. Impala is an open source SQL engine that can be used effectively for processing queries on huge volumes of data. Impala is faster and handles bigger volumes of data than Hive query engine.

  • Query expressions in Hive are generated during compile time whereas Impala generates run time code for big loops through LLVM that helps in optimizing the code.
  • Hive translates queries to be executed into MapReduce jobs under the hood involving overheads initially whereas Impala responds quickly through massively parallel processing.

Learn Hadoop to become a Microsoft Certified Big Data Engineer.

Impala is faster than Apache Hive but that does not mean that it is the one stop SQL solution for all big data problems. Impala is memory intensive and does not run effectively for heavy data operations like joins because it is not possible to push in everything into the memory. This is when Hive comes to the rescue. If an application has batch processing kind of needs over big data then organizations must opt for Hive. If they need real time processing of ad-hoc queries on subset of data then Impala is a better choice.

Click here to know more about our IBM Certified Hadoop Developer course



Relevant Projects

Tough engineering choices with large datasets in Hive Part - 2
This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.

Web Server Log Processing using Hadoop
In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.

Data Warehouse Design for E-commerce Environments
In this hive project, you will design a data warehouse for e-commerce environments.

Airline Dataset Analysis using Hadoop, Hive, Pig and Impala
Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.

Tough engineering choices with large datasets in Hive Part - 1
Explore hive usage efficiently in this hadoop hive project using various file formats such as JSON, CSV, ORC, AVRO and compare their relative performances

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Hadoop Project-Analysis of Yelp Dataset using Hadoop Hive
The goal of this hadoop project is to apply some data engineering principles to Yelp Dataset in the areas of processing, storage, and retrieval.

Data Mining Project on Yelp Dataset using Hadoop Hive
Use the Hadoop ecosystem to glean valuable insights from the Yelp dataset. You will be analyzing the different patterns that can be found in the Yelp data set, to come up with various approaches in solving a business problem.

Design a Hadoop Architecture
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

Explore features of Spark SQL in practice on Spark 2.0
The goal of this spark project for students is to explore the features of Spark SQL in practice on the latest version of Spark i.e. Spark 2.0.