Big Data Analytics Software

Introduction to Big Data Analytics Software

Big data is the buzzword. It is the most preferred and highly in-demand job. Today, in this Big Data Analytics Software article, we shall be talking about what big data is, why it is important, how it is done, and most importantly, we shall focus on what tools and software are available in the market to do big data analytics.

Big data is the name given to data that is really huge in size. Typically, data in the size of more than a few terabytes is called big data. You can understand big data as the POS machine-generated data by Walmart’s various stores across global in a day or over a week. There are four characteristics features of big data: – High Volume, High Velocity, High Variety, and High Veracity. What it means is that those data which is of huge size, is generated at high speed and contains a lot of internal variations in terms of data type, data format, etc. can be classified as big data.

Big data is also called distributed computing.

Because huge data is generated every day and there is a huge potential of insights that can be extracted from such data to gain business value, the scope of big data is growing, and hence it is so much in demand.

Important Concepts of Big Data Analytics Software

How to handle and process big data is a common question. This occurs in the minds of young professionals who want to start learning big data technologies and senior VP and directors of engineering of large corporations who want to analyze the potential of big data and implement the same in their organization.

Data injection, data storage, processing, and insights generation are the usual workflow in the big data space. First, data is injected from the source system to the big data ecosystem (Hadoop, for example), and the same can be done through a data injection system such as AVRO or Scoop. After that, the injected data needs to be stored somewhere; HDFS is what is used for that most commonly. Processing can be done via Pig or Hive, and analysis and insights generation can be carried out by Spark. But other than that, there are several other components of the Hadoop ecosystem which provide one or other important functionality.

An entire Hadoop framework is provided by many distributors such as Cloudera, Horton work, IBM, Amazon, etc.

Apache Hadoop is the most common platform for Hadoop. Hadoop is the collection of open-source software utilities. It solves problems that involve handling and processing a massive amount of data through a network of computers called clusters.

Hadoop applications are run using the MapReduce paradigm. In MapReduce, the data is processed on different CPU nodes in parallel. Hadoop framework can develop applications that run on clusters of computers and are highly fault-tolerant.

Hadoop Architecture

Hadoop architecture has four modules: –

1. Hadoop common: 

• Java libraries and utilities required by other Hadoop modules
• provide file system and OS level abstractions
• contains the essential Java files and scripts that are required to start and run Hadoop.

2. Hadoop YARN:

•  framework for job scheduling
• cluster resource management.

3. Hadoop Distributed File System (HDFS):

• provides high-throughput access to application data.

4. Hadoop MapReduce: 

• YARN-based system for parallel processing of large data sets.

Big Data Analytics Software

Following are a few Big Data Analytics Software: –

• Amazon Web Services: Probably the most popular Big data platform, AWS is super cool. It is cloud-based and provides data storage, computing power, databases, analytics, networking, etc. These services reduce operational cost, faster execution, and greater scalability.
• Microsoft Azure: Azure is great for improving productivity. Integrated tools and pre-built templates make everything simple and fast. It supports a spectrum of operating systems, programming languages, frameworks, and tools.
• Horton works data platform: Based on open-source Apache Hadoop, it is trusted by all and provides a centralized YARN. It is a state of an art system that provides a versatile range of software.
• Cloudera Enterprise: It is powered by Apache Hadoop. From analytics to data science, it can do everything under a secure and scalable environment and provides limitless possibilities.
• MongoDB: It is the next-generation database based on NoSQL format. It uses a document data model, which is similar to JSON.

Examples of Big Data Analytics Software

In this section, we provide a wide range of Big data Analytics software.

Conclusion

From above, we can understand that there is a wide range of available tools and technology in the field of big data analytics. One point that needs to be kept in mind is that some of the technologies mentioned above is propriety and hence available only after a subscription, while some others are open source and hence completely free.  For AWS, for example, a subscription needs to be taken where payment is charged at an hourly rate. Cloudera and Horton work, on the other hand, are free.  Hence one needs to choose wisely which tools or technology to opt for. Usually, a paid, licensed software is good for developing enterprise-level software as it comes with a support and maintenance warranty; hence there are no last-time surprises, while open source is good for learning and initial development purposes. However, it does not mean that open source technologies are not meant for production-level software development; these days lots of lots of software are built using open source technologies.

Recommended Articles

This has been a guide to the Concepts of Big Data Analytics Software. Here we have discussed the important concept with different Big Data Analytics Software like Amazon Web services, Microsoft Azure, Cloudera Enterprise etc. You may also look at the following article to learn more –

1. Big Data Analytics Tools
2. 5 Challenges and Solutions of Big Data Analytics
3. Big Data Techniques
4. Is Big Data a Database?