Top 15 Hadoop Analytics Tools for 2023 – Take a Dive into Analytics

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Explore different Hadoop Analytics tools for analyzing Big Data and generating insights from it.

Apache Hadoop is an open-source framework developed by the Apache Software Foundation for storing, processing, and analyzing big data.

The article enlists the top analytics tools used for processing or analyzing big data and generating insights from it.

Let us now explore popular Hadoop analytics tools.

hadoop analytics tools

Top Hadoop Analytics Tools for 2023

1. Apache Spark

It is a popular open-source unified analytics engine for big data and machine learning.

Apache Software Foundation developed Apache Spark for speeding up the Hadoop big data processing.

It extends the Hadoop MapReduce model to effectively use it for more types of computations like interactive queries, stream processing, etc.

Apache Spark enables batch, real-time, and advanced analytics over the Hadoop platform.

Spark provides in-memory data processing for the developers and the data scientists

Companies, including Netflix, Yahoo, eBay, and many more, have deployed Spark at a massive scale.

Features of Apache Spark:

  • Speed: Spark has the ability to run applications in Hadoop clusters 100 times faster in memory and ten times faster on the disk.
  • Ease of use: It can work with different data stores (such as OpenStack, HDFS, Cassandra) due to which it provides more flexibility than Hadoop.
  • Generality: It contains a stack of libraries, including MLlib for machine learning, SQL and DataFrames, GraphX, and Spark Streaming. We can combine these libraries in the same application.
  • Runs Everywhere: Spark can run on Hadoop, Kubernetes, Apache Mesos, standalone, or in the cloud.

2. MapReduce

MapReduce is the heart of Hadoop. It is a software framework for writing applications that process large datasets in parallel across hundreds or thousands of nodes on the Hadoop cluster.

Hadoop divides the client’s MapReduce job into a number of independent tasks that run in parallel to give throughput.

The MapReduce framework works in two phases- Map phase and the Reduce phase. The input to both the phases is the key-value pair.

Features of Hadoop MapReduce:

  • Scalable: Once we write a MapReduce program, we can easily expand it to work over a cluster having hundreds or even thousands of nodes.
  • Fault-tolerance: It is highly fault-tolerant. It automatically recovers from failure.
  • Distributed Processing: MapReduce makes it possible to process data in parallel on a group of computers. It provides for scalable and effective data processing by distributing the data and jobs among numerous nodes.
  • Support for Several Programming Languages: Different MapReduce implementations support various programming languages, enabling developers to do data processing tasks using the language of their choosing.

3. Apache Impala

Apache Impala is an open-source tool that overcomes the slowness of Apache Hive. It is a native analytic database for Apache Hadoop.

With Apache Impala, we can query data stored either in HDFS or HBase in real-time.

Impala uses the same metadata, ODBC driver, SQL syntax, and user interface as Apache Hive, thus providing a familiar and uniformed platform for batch or real-time queries.

We can integrate Apache Impala with Apache Hadoop and other leading BI tools to provide an inexpensive platform for analytics.

Features of Impala:

  • Security: It is integrated with Hadoop security, and Kerberos thus ensures security.
  • Expand the Hadoop user: With Impala, users using SQL queries or BI applications can interact with more data through metadata store from the source through analysis.
  • Scalability: Impala scales linearly, even in multi-tenant environments.
  • In-memory data processing: It supports in-memory data processing means that without any data movement, it easily accesses and analyzes the data stored on Hadoop DataNodes. Thus, it reduces cost due to reduced data movement, modeling, and storage.
  • Faster Access: It provides faster access to data when compared to other SQL engines.
  • Easy Integration: We can integrate Impala with BI tools like Tableau, Pentaho, Zoom data, etc.

4. Apache Hive

Apache Hive is a java based data warehousing tool designed by Facebook for analyzing and processing large data.

Hive uses HQL(Hive Query Language) similar to SQL that is transformed into MapReduce jobs for processing huge amounts of data.

It provides support for developers and analytics to query and analyze big data with SQL like queries(HQL) without writing the complex MapReduce jobs.

Users can interact with the Apache Hive through the command line tool (Beeline shell) and JDBC driver.

Features of Apache Hive:

  • Hive supports client-application written in any language like Python, Java, PHP, Ruby, and C++.
  • It generally uses RDBMS as metadata storage, which significantly reduces the time taken for the semantic check.
  • Hive Partitioning and Bucketing improves query performance.
  • Hive is fast, scalable, and extensible.
  • It supports Online Analytical Processing and is an efficient ETL tool.
  • It provides support for User Defined Function to support use cases that are not supported by Built-in functions.

5. Apache Mahout

Apache Mahout is an open-source framework that normally runs coupled with the Hadoop infrastructure at its background to manage large volumes of data.

The name Mahout is derived from the Hindi word “Mahavat,” which means the rider of an elephant.

As Apache Mahout runs algorithms on the top of the Hadoop framework, thus named as Mahout.

We can use Apache Mahout for implementing scalable machine learning algorithms on the top of Hadoop using the MapReduce paradigm.

Apache Mahout is not restricted to the Hadoop based implementation; it can run algorithms in the standalone mode as well.

Apache Mahout implements popular machine learning algorithms such as Classification, Clustering, Recommendation, Collaborative filtering, etc.

Features of Mahout:

  • It works well in the distributed environment since its algorithms are written on the top of Hadoop. It uses the Hadoop library to scale in the cloud.
  • Mahout offers a ready-to-use framework to the coders for performing data mining tasks on large datasets.
  • It lets the application to quickly analyze the large datasets.
  • Apache Mahout includes various MapReduce enabled clustering applications such as Canopy, Mean-Shift, K-means, fuzzy k-means.
  • It also includes vectors and matrix libraries.
  • Apache Mahout exposed various Classification algorithms such as Naive Bayes, Complementary Naive Bayes, and Random Forest.

6. Pig

Pig is developed by Yahoo as an alternative approach to make MapReduce job easier.

It enables developers to use Pig Latin, which is a scripting language designed for pig framework that runs on Pig runtime.

Pig Latin is SQL like commands that are converted to MapReduce program in the background by the compiler.

It works by loading the commands and the data source.

Then we perform various operations like sorting, filtering, joining, etc.

At last, based on the requirement, the results are either dumped on the screen or stored back to the HDFS.

Features of Pig:

  • Extensibility: Users can create their own function for performing specific purpose processing.
  • Solving complex use cases: Pig is best suited for solving complex use cases that include multiple data processing having multiple imports and exports.
  • Handles all kinds of data: Structured and Unstructured can be easily analyzed or processed using Pig.
  • Optimization Opportunities: In Pig, the execution of the task gets automatically optimized by the task itself. Thus programmers need to focus on semantics rather than efficiency.
  • It provides a platform for building data flow for ETL (Extract, Transform, and Load), processing, and analyzing massive data sets.

7. HBase

HBase is an open-source distributed NoSQL database that stores sparse data in tables consisting of billions of rows and columns.

It is written in Java and modeled after Google’s big table.

HBase is used when we need to search or retrieve a small amount of data from large data sets.

For example: If we are having billions of customer emails and we need to find out the customer name who has used the word replace in their emails, then we use HBase.

There are two main components in HBase. They are:

  • HBase Master: HBase Master negotiates load balancing across the region server. It controls the failover, maintains, and monitors the Hadoop cluster.
  • Region Server: Region Server is the worker node that handles the read, write, update, and delete requests from the clients.

Features of HBase:

  • Scalable storage
  • It Supports fault-tolerant feature
  • Support for real-time search on sparse data
  • Support easily consistent read and writes
  • Column-Family-Based Data Model: HBase groups data into logical groups of columns called column families. Data is saved in rows within these column families, each of which can have many columns. This data architecture enables effective data retrieval and flexible schema creation.
  • HBase Coprocessors: Coprocessors are specialised code modules that may run on region servers. They are supported by HBase. Coprocessors enhance query speed by enabling developers to incorporate unique data processing algorithms near to the data.

8. Apache Storm

A storm is an open-source distributed real-time computational framework written in Clojure and Java.

With Apache Storm, one can reliably process unbounded streams of data (ever-growing data that has a beginning but no defined end).

We can use Apache Storm in real-time analytics, continuous computation, online machine learning, ETL, and more.

Among many, Yahoo, Alibaba, Groupon, Twitter, Spotify uses Apache Storm.

Features of Apache Storm:

  • It is scalable and fault-tolerant
  • Apache Storm guarantees data processing
  • It can process millions tuples per second per node
  • It is easy to set up and operate

9. Tableau

Tableau is a powerful data visualization and software solution tool in the Business Intelligence and analytics industry.

It is the best tool for transforming the raw data into an easily understandable format with zero technical skill and coding knowledge.

Tableau allows users to work on the live datasets and to spend more time on data analysis and offers real-time analysis.

It offers a rapid data analysis process, which results in visualizations that are in the form of interactive dashboards and worksheets. It works in synchronization with the other Big Data tools.

Features of Tableau:

  • With Tableau, one can make visualizations in the form of Bar chart, Pie chart, Histogram, Gantt chart, Bullet chart, Motion chart, Treemap, Boxplot, etc
  • Tableau is highly robust and secure.
  • Tableau offers a large option of data sources ranging from on-premise files, relational databases, spreadsheets, non-relational databases, big data, data warehouses, to on-cloud data.
  • It allows you to collaborate with different users and share data in the form of visualizations, dashboards, sheets, etc. in real-time.

10. R

R is an open-source programming language written in C and Fortran.

It facilitates Statistical computing and graphical libraries. It is platform-independent and can be used across multiple operating systems.

R consists of a robust collection of graphical libraries like plotly, ggplotly, and more for making visually appealing and elegant visualizations.

R’s biggest advantage is the vastness of its package ecosystem.

It facilitates the performance of different statistical operations and helps in generating data analysis results in the text as well as graphical format.

Features of R:

  • R provides a wide range of Packages. It has CRAN, which is a repository holding 10,000 plus packages.
  • R provides the cross-platform capability. It can run on any OS.
  • R is an interpreted language. It does not require any compiler to compile the code. Thus, R script runs in very little time.
  • R can handle structured as well as unstructured data.
  • The graphics and charting benefits that R provides are unmatchable.

11. Talend

Talend is an open-source platform that simplifies and automates big data integration.

It provides various software and services for data integration, big data, data management, data quality, cloud storage.

It helps businesses in taking real-time decisions and become more data-driven.

Talend offers various commercial products like Talend Big Data, Talend Data Quality, Talend Data Integration, Talend Data Preparation, Talend Cloud, and more.

Companies like Groupon, Lenovo, etc. use Talend.

Features of Talend:

  • Talend simplifies ETL and ELT for Big Data.
  • It accomplishes the speed and scale of Spark.
  • It handles data from multiple sources.
  • Being an open source software, it is backed up by a huge community.
  • Talend automates tasks and further maintains them for you.

12. Lumify

Lumify is an open-source, big data fusion, analysis, and visualization platform that supports the development of actionable intelligence.

With Lumify, users can discover complex connections and explore relationships in their data through a suite of analytic options, including full-text faceted search, 2D and 3D graph visualizations, interactive geospatial views, dynamic histograms, and collaborative workspaces shared in real-time.

Features of Lumify:

  • Lumify’s infrastructure allows attaching new analytic tools that will work in the background to monitor changes and assist analysts.
  • It is Scalable and Secure.
  • Lumify provides support for a cloud-based environment.
  • Lumify enables us to integrate any open Layers-compatible mapping systems like Google Maps or ESRI, for geospatial analysis.


KNIME stands for Konstanz Information Minner.

It is an open-source, scalable data-analytics platform for analyzing big data, data mining, enterprise reporting, text mining, research, and business intelligence.

KNIME helps users to analyze, manipulate, and model data through Visual programming. KNIME is a good alternative for SAS.

Various Companies, including Comcast, Johnson & Johnson, Canadian Tire, etc. use KNIME.

Features of KNIME:

  • KNIME offers simple ETL operations.
  • One can easily integrate KNIME with other languages and technologies.
  • KNIME offers over 2000 modules, a broad spectrum of integrated tools, advanced algorithms.
  • KNIME is easy to set up and doesn’t have any stability issues.

14. Apache Drill:

It is a low latency distributed query engine inspired by Google Dremel.

Apache Drill allows users to explore, visualize, and query large datasets using MapReduce or ETL without having to fix to a schema.

It is designed to scale to thousands of nodes and query petabytes of data.

With Apache Drill, we can query data just by mentioning the path in SQL query to a Hadoop directory or NoSQL database or Amazon S3 bucket.

With Apache Drill, developers don’t need to code or build applications.

Features of Apache Drill:

  • It allows developers to reuse their existing Hive deployments.
  • Make UDF creation easier through the high performance, easy to use Java API.
  • Apache Drill has a specialized memory management system that eliminates garbage collections and optimizes memory allocation and usage.
  • For performing a query on data, Drill users are not required to create or manage tables in the metadata.

15. Pentaho

Pentaho is a tool with a motto to turn big data into big insights.

It is data integration, orchestration, and a business analytics platform that provides support ranging from big data aggregation, preparation, integration, analysis, prediction, to interactive visualization.

Pentaho offers real-time data processing tools for boosting digital insights.

Features of Pentaho:

  • We can use Pentaho for big data analytics, embedded analytics, cloud analytics.
  • Pentaho supports Online Analytical Processing (OLAP)
  • One can use Pentaho for Predictive Analysis.
  • It provides a User-Friendly Interface.
  • Pentaho provides options for a wide range of big data sources.
  • It allows enterprises to analyze, integrate, and present data through comprehensive reports and dashboards.


In this article, we have studied various 15 Hadoop analytics tools for 2023 such as Apache Spark, MapReduce, Impala, Hive, Pig, HBase, Apache Mahout, Storm, Tableau, Talend, Lumify, R, KNIME, Apache Drill, and Pentaho.

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