Top 10 Big Data Analytics Tools

  • Ritesh Pathak
  • Jan 26, 2021
  • Big Data
  • Updated on: Jun 08, 2021
Top 10 Big Data Analytics Tools title banner

The increased use of technology in the past few years has also led to an increase in the amounts of data being generated per minute. Everything we do online generates some sort of data. 

 

A report series, Data Never Sleeps, by DOMO, covers the amount of data being generated every minute. In the eighth edition of the report, it shows that a solitary internet minute has over 400,000 hours of video streaming on Netflix, 500 hours of video streamed by users on Youtube, and almost 42 million messages shared through WhatsApp.

 

The number of internet users has reached 4.5 billion, nearly 63% (according to our calculation) of the total world population. The number is expected to increase in the coming years as we witness an expansion of technologies. 

 

These huge amounts of structured, semi-structured, unstructured data are referred to as big data. Businesses analyze and make use of these data to gain better knowledge about their customers. 

 

Big Data Analytics is a process that enables data scientists to make something out of the stack of big data generated. This analysis of big data is done using some tools that we reckon as big data analytics tools. 

 

In this blog, we will be discussing the top 10 big data analytics tools (in no particular order) that are being leveraged by data scientists.

 

 

1. R-Programming

 

R-Programming is a domain-specific programming language specifically designed for statistical analysis, scientific computing, and data visualization using R Programming. Ross Ihaka and Robert Gentleman developed it in 1993. 

 

It is among the top big data analytics tools because R-Programming software helps data scientists to create statistics engines that can provide better and precise insights due to relevant and accurate data collection.    

 

The tools exhibit some features that are: 

 

  • Effective data handling and storage facility

  • It provides tenacious and integrated tools for data analysis

  • Allows you to create statistic engines rather than opting for a pre-made approach

  • R integrated with its sister language Python gives faster, up-to-date, and accurate analytics

  • R produces plots and graphics that are ready for publication

 

 

2. Altamira LUMIFY

 

Lumify is a big data fusion, analysis, and visualization platform. Like all big data analytics tools, it too enables you to understand connections and explore the relationship between your data. 

 

Lumify is considered as a good big data analytics tool because it facilitates its users to get a set of analytics options that include graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces that can be shared in real-time.  

 

Lumify offers both 2D and 3D graph visualizations with automatic layouts. It also provides a plethora of options to analyze the links between different entities in a graph. 

 

Lumify comes with specific ingest processing and interface elements for textual content, images, and videos. The platform allows you to organize your work in different workspaces. 

 

The platform is built on proven,scalable big data technologies. It is secure, scalable, and backed by a motivated full-time development team.

 

 

3. Apache Hadoop

 

Apache Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.

 

Doug Cutting and Mike Cafarella worked together to come up with Hadoop in 2005. It was originally designed to distribute for the Nutch search engine project which was an open-source web crawler created in 2002. 

 

Apache Hadoop is a framework that consists of a software ecosystem. Hadoop Distributed File System or HDFS and MapReduce are the two primary components of Hadoop. 

 

The software produces a distributed storage framework and uses the MapReduce programming model for the processing of big data. 

 

Hadoop possesses a great ability to store and distribute big data sets across hundreds of inexpensive servers and hence is considered as a top big data analytics tool. Its users can even the size of the cluster by adding new nodes as per their requirements too without any downtime. 

 

 

4. MongoDB

 

MongoDB is a document-oriented NoSQL database used to store high volumes of data. MongoDB is well-known for its robustness and this makes MongoDB different from Hadoop

 

Unlike traditional rotational databases, MongoDB makes use of collections and documents rather than using rows and columns. These documents consist of key-value pairs which are considered as the basic unit of data in MongoDB. 

 

Also Read: NoSQL vs SQL

 

Each database in MongoDB contains collections that in turn contain documents. However, the size, content, and number of fields vary from document to document.

 

Developers get a chance to alter the document structure. The document structure is more in line with how programmers create their classes and objects in their respective programming languages. 

 

The data model available within MongoDB enables you to represent hierarchical relationships, to store arrays, and other more complex elements more easily.

 


The top 10 big data analytics tools are R-programming, Altamira lumify, Apache Hadoop, Mongo DB, Apache Spark, RapidMiner, Microsoft Azure, Zoho Analytics, Xplety, and Splice Machine.

10 Big Data Analytics Tools


 

5. RapidMiner

 

RapidMiner is a software platform that is built for analysts who like to integrate data prep, machine learning, and predictive model deployment. The icing on the cake here is that it is an open-source software tool, free of charge, for data and text mining. 

 

RapidMiner offers the most powerful and intuitive graphical user interface for the design of the analysis process. 

 

In addition to Windows operating systems, RapidMiner also supports Macintosh, Linux, and Unix systems.

 

The platform's features include built-in security controls, reduced need for writing the code, a visual workflow designer for Hadoop and Sparx. Radoop enables the user to adopt large datasets for training in Hadoop. It allows for team collaboration, centralized workflow management, it backs Kerberos, Hadoop impersonation, and sentry/ranger.

 

It also assembles the requests and reuses Spark containers for smart optimization of processes.

 

RapidMiner provides five products for data analysis, namely - RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Server, and RapidMiner Radoop.

 

 

6. Apache Spark

 

Apache Spark is one of the most powerful open source big data analytics tools. It is a data processing framework that can quickly possess very large data sets. 

 

It can also distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools.

 

Apache Spark features in-built for streaming, SQL, machine learning, and graph processing support and earns the site as the speediest and common generator for big data transformation. 

 

It helps to run an application in a Hadoop cluster, a hundred times faster in memory and ten times faster on a disc. It also offers over 80 high-level operators that help to build parallel apps faster. 

 

It offers high-level APIs in Java and also consists of 80 high-level operators for efficient query execution.

 

The platform offers a great degree of  flexibility and versatility since it works with different data stores like HDFS, OpenStack and Apache Cassandra.

 

 

7. Microsoft Azure

 

Microsoft Azure, formerly known as Windows Azure, is a public cloud computing platform handled by Microsoft. It provides a range of services that include computing, analytics, storage, and networking. 

 

Windows Azure provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization so that they can run their big data workloads.

 

Microsoft Azure offers reliable analytics with an industry-leading SLA, and enterprise-grade security and monitoring. It is also considered a high-productivity platform for developers and data scientists. 

 

The platform aims to offer information in real-time in a way that is easy to manage even when used on the most advanced applications.

 

There's no necessity of creating and allocating fresh IT infrastructure or virtual servers for the processing. Rather, generally adopted SQL queries can be used for extracting basic information, while other programming languages like JavaScript and C# can be added for more complex operations. 

 

 

8. Zoho Analytics

 

Zoho Analytics is a BI and Data analytics software platform that helps its users to visually analyze data, create visualizations, and get a better and in-depth understanding of raw data. 

 

It allows its users to integrate multiple data sources that may include business applications, databases, cloud drives, and more. It helps users generate dynamic, highly customizable, and actionable reports. 

 

Zoho Analytics is a user-friendly platform that makes it easy to upload and control data. Also, it enables the easy creation of multifaceted and custom dashboards. The software platform is easy to deploy and implement. 

 

The platform of Zoho Analytics can be accessed widely, be it via the data pros in the C suite to the sales reps that require data analytics trend lines for their operations.

 

Zoho Analytics also enables the users to generate a comment threat in the app, for facilitating collaboration between staffers and teams. The platform is an effective choice for businesses that are required to offer   convenient, accessible data analytics insight to staffers at every level.

 

 

9. Xplenty

 

Xplenty is a cloud-based ETL solution that provides simple visualized data pipelines. These pipelines allow data to flow automatically across sources and destinations. 

 

Xplenty has powerful on-platform transformation tools that allow you to clean, normalize, and transform data whilst adhering to compliance best practices. 

 

The platform exhibits some features that make it a user-friendly platform: 

 

  • Easy Data transformations 

  • Simple workflow creation to define dependencies between tasks

  • REST API for connecting to any data source

  • Salesforce to Salesforce integrations 

  • Cutting-edge data security and compliances 

  • Diverse data source and data destination options

 

 

10. Splice Machine

 

Splice Machine is a scale-out SQL Rotational Database Management System (RDBMS). It has ACID transactions, in-memory analytics, and in-database machine learning, combined. 

 

The big data analytics tools can scale from a few to thousands of nodes enabling applications at every scale. 

 

The Splice Machine optimizer automatically evaluates every query to the distributed HBase regions. It offers low latency row-based storage. 

 

Splice Machine’s dual model leverages columnar external tables on cost-effective storage on cloud block storage, HDFS, or local files as Parquet, ORC, or Avro files with append-only functionality.

 

Splice Machine analytical computation maintains ACID properties with a special integration to our underlying row-based storage. 

 

Above are just a handful of leading Big Data Analytics tools that are popular among users. We hope that this article has helped you in learning some more about popular data analysis tools.

0%

Comments