Top 10 Big Data Tools for Analysis

“ Information is the oil of the 21st century, and analytics is the combustion engine. ”
– Peter Sondergaard

In the old days, people generally traveled using a horse cart or bullock cart. But it is not feasible to use such carts in today’s world. Right ??? and Why ???….absolutely because of the growing population and also the time required in horse or bullock cart is high.

Similarly, in technology world, data is generated at a high rate and it is impossible to store these massive amounts of data in a traditional way.

Thus there is a need for some efficient, modern and feasible way for the storage of such a large amount of data.

Big data tools for analysis are used to solve the problem of handling and managing data. These tools perform data analysis tasks that are both time and cost-effective.

Also, these tools helped in exploring the business insights and enhanced the effectiveness of business.

Now let us look at 10 most efficient and versatile Big Data Analytics Tools for 2020.

Big Data Tools for Data Analysis

1. Tableau

The primary objective of Tableau is to focus on business intelligence. It is the best efficient data visualization tool.

In tableau, users do not have to write a program in order to create maps, charts, etc. For live data in the visualization, tableau explored the web connector to connect the database or API.

Features of Tableau :

Tableau provides a central location to delete, manage schedules and tag, and change permissions.
It does not require complicated software setup.
In this real-time collaboration is available.
Without any integration cost, it can blend various datasets like relational datasets, structured datasets, etc.

2. Cloudera Distribution for hadoop

If you are searching for a highly secure Big data platform, then Cloudera is the best option for your project. It is the fastest, modern and most accessible platform.

Using this cloudera hadoop, you will easily get any data across any environment within a single and scalable platform.

Features of Cloudera Hadoop:

Provides real-time insights for monitoring and detection.
Delivers an enterprise-grade and hybrid cloud solution.
Develops and trains the data model.
Spins up and terminates clusters, and only pays for what is needed.

3. Teradata

Teradata is a tool used for developing large scale data warehousing applications. It is a well known relational database management system.

It generally offers end to end solutions for data warehousing. Its development is based on the MPP (Massively Parallel Processing Architecture ).

Features of Teradata :

Teradata can connect network-attached systems or mainframes.
Its significant components are a node, parsing engine, the message passing layer, and the access module processor (AMP).
It is highly scalable.
It supports industry-standard SQL in order to interact with the data.

4. R – Programming

R Programming language is used for statistical computing, graphics and for big data analysis. It provides a wide variety of statistical tests.

Features of R programming tool:

R programming tools provide an effective data handling and storage facility.
It provides a coherent and integrated collection of big data tools for data analysis.
It also provides graphical facilities for data analysis which display either on-screen or on hardcopy.

5. Spark

Apache Spark is one of the most powerful open-source big data analytics tools. It is used by many organizations to process large datasets. It offers high-level operators that make it easy to build parallel apps.

Features of Spark:

It offers Fast Processing
Has the ability to integrate with Hadoop and existing Hadoop Data
Using Spark an application can be run in a Hadoop cluster, up to 100 times faster in memory, and ten times faster on disk.

6. Lumify

Lumify is a platform that involves big data fusion, analysis, and visualization. It is a free and open source tool for analytics.

It supports the cloud-based environment and also works well with Amazon’s AWS.

Features of Lumify:

Lumify’s primary features include the full-text search, 2D and 3D graph visualizations, link analysis between graph entities, automatic layout, integration with mapping systems, geospatial layouts, multimedia analysis, real-time collaboration through a set of projects or workspaces.
It is usually built on proven, scalable big data technologies.
It is secure, scalable, and supported by a dedicated full-time development team.

7. Talend

Talend simplifies and automates big data integration. Its graphical wizard generates native code. It also allows big data integration, check data quality, and master data management.

Features of Talend:

Talend Big Data Platform generates native code which simplifies using MapReduce and Spark.
It accelerates time to value for big data projects.
It also simplifies ETL & ELT for big data.

8. Microsoft HDInsight

Azure HDInsight is a Spark and Hadoop service in the cloud. Standard and Premium are the two data cloud offerings provided by Azure HDInsight.

For running the Big data workloads of the organization it also provides an enterprise-scale cluster.

Features of HDInsight:

Offers enterprise-grade security and monitoring.
Protects data assets and extends on-premises security and governance controls to the cloud.
Provides a high-productivity platform for developers and scientists.

9. Skytree

Skytree is a big data analytics tool that helps data scientists to build more accurate models faster. It also offers accurate predictive machine learning models that are easy to use.

Features of Skytree:

Helps to develop Highly Scalable Algorithms.
Allows data scientists to visualize and understand the logic behind Machine Learning decisions.
Solves robust predictive problems with data preparation capabilities.

10. Pentaho

Pentaho is a software that can access, prepare and can analyze any data from any source. It is a best and trendy choice for data integration, orchestration, and business analytics platform.

The main motto of this too is to turn Big data analytics into big insights.

Features of Pentaho:

Pentaho generally supports a wide range of Big data sources.
No such coding is required and it can deliver the data effortlessly to your business.
It generally permits to check data with easy access to analytics, like charts, visualizations, etc.
It can also access and integrate data for data visualization effectively.

Conclusion

“ We chose it because we deal with huge amounts of data. Besides, it sounds really cool. ”

– Larry Page

In the world of modern technology, Big Data tools for analysis is a competitive edge.

Using Big Data technique, a vast number of potential information is generated. Organizations use this information for their further decision making. It also provides a cost-effective and robust way to process and manage data.

Big data analytics tools are serving a great purpose in the technological world. Undoubtedly, in future more efficient and enhanced tools will be introduced by Big Data.

We believe that you will surely learn something new and exciting from this article. Do share it on social media with your friends to spread the knowledge.