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8 Best Big Data Hadoop Analytics Tools in 2021

Most companies have big data but are unaware of how to use it. Firms have started realizing how important it is for them to start analyzing data to make better business decisions.

With the help of big data analytics tools, organizations can now use the data to harness new business opportunities. This is return will lead to smarter business leads, happy customers, and higher profits. Big data tools are crucial and can help an organization in multiple ways – better decision making, offer customers new products and services, and it is cost-efficient.

Let us further explore the top data analytics tools which are useful in big data:

1. Apache Hive

A java-based cross-platform, Apache Hive is used as a data warehouse that is built on top of Hadoop. a data warehouse is nothing but a place where data generated from multiple sources gets stored in a single platform. Apache Hive is considered as one of the best tools used for data analysis. A big data professional who is well acquainted with SQL can easily use Hive. The Query language used here is HIVEQL or HQL.

Pros:

  • Hive uses a different type of storage called ORC, HBase, and Plain text.
  • The HQL queries resemble SQL queries.
  • Hive is operational on compressed data which is intact inside the Hadoop ecosystem
  • It is in-built and used for data-mining.

2. Apache Mahout

The term Mahout is derived from Mahavatar, a Hindu word describing the person who rides the elephant. Algorithms run by Apache Mahout take place on top of Hadoop thus termed as Mahout. Apache Mahout is ideal when implementing machine learning algorithms on the Hadoop ecosystem. An important feature worth mentioning is that Mahout can easily implement machine learning algorithms without the need for any integration on Hadoop.

Pros:

  • Composed of matrix and vector libraries.
  • Used for analyzing large datasets.
  • Ideal for machine learning algorithms.

3. Apache Impala

Ideally designed for Hadoop, the Apache Impala is an open-source SQL engine. It offers faster processing speed and overcomes the speed-related issue taking place in Apache Hive. The syntax used by Impala is similar to SQL, the user interface, and ODBC driver like the Apache Hive. This gets easily integrated with the Hadoop ecosystem for big data analytics purposes.

Pros:

  • Offers easy-integration.
  • It is scalable.
  • Provides security.
  • Offers in-memory data processing.

4. Apache Spark

It is an open-source framework used in data analytics, fast cluster computing, and even machine learning. Apache Spark is ideally designed for batch applications, interactive queries, streaming data processing, and machine learning.

Pros:

  • Easy and cost-efficient.
  • Spark offers a high-level library that is used for streaming.
  • Due to the powerful processing engine, it runs at a faster pace.
  • It has in-memory processing.

5. Apache Pig

Apache Pig was first developed by Yahoo to make programming easier for developers. Ever since it offers the advantage of processing an extensive dataset. Pig is also used to analyze large datasets and can be presented in the form of dataflow. Now, most of these tools can be learned through professional certifications from some of the top big data certification platforms available online. As big data keep evolving, big data tools will be of the utmost significance to most industries.

Pros:

  • Known to handle multiple types of data.
  • Easily extensible.
  • Easy to program.

6. Apache Storm

Apache Storm is an open-source distributed real-time computation system and is free. And this is built with the help of programming languages like Java, Clojure, and many other languages. Apache Storm is used for streaming due to its speed. It can also be used for real-time processing and machine learning processing. Apache Storm is used by top companies such as Twitter, Spotify, and Yahoo, etc.

Pros:

  • The operational level is easy.
  • Fault tolerance.
  • Scalable

7. Apache Sqoop

If there is a command-line developed by Apache, that would be Sqoop. Apache Sqoop’s major purpose is to import structured data such as Relational Database Management System (RDBMS) like Oracle, SQL, MySQL to the Hadoop Distributed File System (HDFS). Apache Sqoop can otherwise transfer data from HDFS to RDBMS too.

Pros:

  • Sqoop controls parallelism.
  • Helps connect to the database server.
  • Offers feature to import data to HBase or Hive.

8. HBase

HBase is a non-distributed, column-based oriented, and non-relational database. It composes of multiple tables and these tables consist of many data rows. These data rows further have multiple column families and the column’s family each consists of a key-value pair. HBase is ideal to use when looking for small size data from large datasets.

Pros:

  • The Java API is used for client access.
  • It blocks the cache for real-time data queries.
  • Offers modularity and linear scalability.

Besides the above-mentioned tools, you can also use Tableau to provide interactive visualization to demonstrate the insights drawn from the data and MapReduce, which helps Hadoop function faster.

However, you need to take the right pick while choosing any tool for your project.

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Tags: analytics, big, certification, data, professional, top

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