We all knew that Big Data refers to voluminous data gathered from different sources such as mobile phones, social media feeds, IoT devices, databases, servers, and applications, etc. But this data is of no use until and unless it is properly...
We all knew that Big Data refers to voluminous data gathered from different sources such as mobile phones, social media feeds, IoT devices, databases, servers, and applications, etc. But this data is of no use until and unless it is properly manipulated so that it can help to make decisions out of it.
So, to make this data meaningful in a way, certain scientific tools and methodologies have been implemented to extract valuable information from it. The overall process of analyzing data sets about the information with the support of specialized tools and technologies is referred to as Big Data analytics.
Big Data Analytics is used to process a large amount of data sets to uncover hidden patterns, market trends, customer preferences and many other useful information that can be helpful for organizations to make decisions to enhance their business.
With Big data analysis, it is possible to process the data very quickly and efficiently, which was not possible with more traditional business intelligence solutions.
Now in this article, we will focus our discussions towards a few important Big data analytics tools which are trending now in the IT industry. But before that, we want to introduce you to a set of online courses containing different courses related to Big Data concept.
Here is the list of top Big data analytics tools:
1. Apache Hadoop:
Apache Hadoop is a big analytics tool based on java, a free software framework. It facilitates effective storage of huge amount of data in a storage place known as cluster. The special feature of this framework is that it runs in parallel on a cluster and also has the ability to process huge data across all nodes in it. Features: • It brings flexibility in data processing • It allows for faster data processing.
HPCC is Big data analytics tool developed by LexisNexis Risk Solutions. It stands for High- Performance Computing Cluster. This technique is more advanced and enterprise-ready. It uses a high-level programming language called Enterprise Control Language (ECL), which is based on C++.
Features: • It is highly efficient in that it can accomplish Big Data tasks with less code • It has the ability to automatically optimize code for parallel processing.
KNIME stands for Konstanz Information Miner. It is an open-source tool that is used for Enterprise reporting, integration, research, CRM and data mining, etc. It supports many platforms such as Linux, Windows operating systems and many more. It is considered as a good alternative to SAS.
Features: • It has rich algorithms set • It automates a lot of manual work.
Datawrapper is an open-source platform for data visualization. Its major customers are newsrooms that are spread all over the world. Some of its notable customers are The Times, Fortune, and Twitter, etc.
Features: • It is a device friendly. It works very well on all types of devices such as mobile, tablet or desktop. • It has great customization and export options.
It is an open-source Big Analytics tool. Its primary features include full-text search, 2D and 3D graph visualization, link analysis between graph entitles, integration with mapping systems, and real-time collaboration through a set of projects or workspaces.
Features: • It is scalable • It supports cloud-based environment. Works well with Amazon’s AWS. Here we have provided 5 prominent tools that are being used in Big Data analytics field. However, you can find a list of many more such tools here.
Big Data Analytics tools are playing a very important role in the Data Science and Big Data fields. There are a number of Big Data Analytics tools available that are used by different companies. Presently in the IT industry, there is a huge scope for the IT professionals with good knowledge of any of these tools.
Considering this growth, if you are looking to learn Big Data Analytics tools, then visit these online courses that can be of great help to you.
We hope the above discussion helped our readers to know some of the Big Data Analytics tools. We like you to send your thoughts in the comment section below.
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