Big Data Analytics and Some Important Analytics Tools
Big data analytics is the process used for interpreting large data which includes market trends, hidden patterns, customer requirements and other details which deems useful for organizations to make clear business decisions. The term used for the collection of large data sets that are very huge and complex is called big data. This type of data is often difficult to process. The use of traditional tools or applications is not possible with big data. The size of this data exceeds in Petabytes. Since this type of data has a huge variety, it brings multiple challenges in reference to its complexity and volume that is associated with big data types. According to recent surveys, it is observed that nearly 80% of the data that is created is available in an unstructured way. The first and foremost important challenge is how to structure and store that data. Certain tools are used for this purpose. The following list of tools will help us analyze Big Data.
Some of the good tools for big data analytics are as follows:
One of the most popular open-source cross-platform sources that utilizes a document-oriented program. It makes use of JSON-documents along with schemas that make it a prominent data analytics tool
It is an open-source platform that helps in the distribution and storage of large data. It is more based on computer clusters which are constructed on commodity hardware. Apache Hadoop is free software with framework based on Java. This tool can help store a very large amount of data in the form of a cluster.
This tool offers a larger insight into the hypothesis generated. The USP of this tool is one doesn’t require any programming skills and can publish the same on the web for free. Moreover, the visuals can be embedded into blogs and shared on social media.
• Require Excel or .txt to read the document.
• There is a limitation to data size.
• All information is public offers a little scope of privacy.
These big data analytics tools help in analyzing and manipulating the information through the use of visual programming. It is also used for integrating data mining and AI as it supports all types of programming language. Furthermore, one needs no write blocks or codes. You can instead drop and drag connection points in between the activities. However, this tool renders poor data visualization.
This is a free open-source network analysis and visualization software tool. It is considered the best data analytics tool that includes advanced network metrics, renders access to social media and supports automation. It’s easy to import data, analyze and visualize graph which can be integrated into Microsoft Excel 07 to 16 models.
Microsoft HDInsight is a big data solution provided by Microsoft and powered by Apache Hadoop. This is available as a service in the cloud. The HDInsight makes use of Windows Azure Blob storage as its default file system. It helps to provide high availability at low cost.
The use of traditional SQL is traditionally opted to handle large amount of structured data. On the other hand, the NoSQL (Not Only SQL) is needed to handle unstructured data. NoSQL databases are used to store unstructured data which have no particular scheme. There are multiple open-source NoSQL DBs which are available to analyze the Big Data.
Hive is a distributed data management for Hadoop. This software can be basically used for Data mining purpose. It runs on top of Hadoop.
Sqoop is a tool that is used to connect Hadoop with various relational databases that are used to transfer data.