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Big Data Technologies, The Buzz-word which you get to hear much in the recent days. In this article, We shall discuss the groundbreaking technologies which made Big Data spread its branches to reach greater heights.
Big Data Technology can be defined as a Software-Utility that is designed to Analyse, Process and Extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with.
We need Big Data Processing Technologies to Analyse this huge amount of Real-time data and come up with Conclusions and Predictions to reduce the risks in the future.
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Big Data Technology is mainly classified into two types:
Firstly, The Operational Big Data is all about the normal day to day data that we generate. This could be the Online Transactions, Social Media, or the data from a Particular Organisation etc. You can even consider this to be a kind of Raw Data which is used to feed the Analytical Big Data Technologies.
A few examples of Operational Big Data Technologies are as follows:
So, with this let us move into the Analytical Big Data Technologies.
Analytical Big Data is like the advanced version of Big Data Technologies. It is a little complex than the Operational Big Data. In short, Analytical big data is where the actual performance part comes into the picture and the crucial real-time business decisions are made by analyzing the Operational Big Data.
Few examples of Analytical Big Data Technologies are as follows:
Top big data technologies are divided into 4 fields which are classified as follows:
Now let us deal with the technologies falling under each of these categories with their facts and capabilities, along with the companies which are using them.
Hadoop Framework was designed to store and process data in a Distributed Data Processing Environment with commodity hardware with a simple programming model. It can Store and Analyse the data present in different machines with High Speeds and Low Costs.
Companies Using Hadoop:
The NoSQL Document Databases like MongoDB, offer a direct alternative to the rigid schema used in Relational Databases. This allows MongoDB to offer Flexibility while handling a wide variety of Datatypes at large volumes and across Distributed Architectures.
Companies Using MongoDB:
RainStor is a software company that developed a Database Management System of the same name designed to Manage and Analyse Big Data for large enterprises. It uses Deduplication Techniques to organize the process of storing large amounts of data for reference.
Companies Using RainStor:
Hunk lets you access data in remote Hadoop Clusters through virtual indexes and lets you use the Splunk Search Processing Language to analyse your data. With Hunk, you can Report and Visualize large amounts from your Hadoop and NoSQL data sources.
Presto is an open source Distributed SQL Query Engine for running Interactive Analytic Queries against data sources of all sizes ranging from Gigabytes to Petabytes. Presto allows querying data in Hive, Cassandra, Relational Databases and Proprietary Data Stores.
Companies Using Presto:
RapidMiner is a Centralized solution that features a very powerful and robust Graphical User Interface that enables users to Create, Deliver, and maintain Predictive Analytics. It allows creating very Advanced Workflows, Scripting support in several languages.
Companies Using RapidMiner:
Elasticsearch is a Search Engine based on the Lucene Library. It provides a Distributed, MultiTenant-capable, Full-Text Search Engine with an HTTP Web Interface and Schema-free JSON documents.
Companies Using Elasticsearch:
Apache Kafka is a Distributed Streaming platform. A streaming platform has Three Key Capabilities that are as follows:
This is similar to a Message Queue or an Enterprise Messaging System.
Companies Using Kafka:
Splunk captures, Indexes, and correlates Real-time data in a Searchable Repository from which it can generate Graphs, Reports, Alerts, Dashboards, and Data Visualizations. It is also used for Application Management, Security and Compliance, as well as Business and Web Analytics.
Companies Using Splunk:
KNIME allows users to visually create Data Flows, Selectively execute some or All Analysis steps, and Inspect the Results, Models, and Interactive views. KNIME is written in Java and based on Eclipse and makes use of its Extension mechanism to add Plugins providing Additional Functionality.
Companies Using KNIME:
Companies Using Spark:
R is a Programming Language and free software environment for Statistical Computing and Graphics. The R language is widely used among Statisticians and Data Miners for developing Statistical Software and majorly in Data Analysis.
Companies Using R-Language:
BlockChain is used in essential functions such as payment, escrow, and title can also reduce fraud, increase financial privacy, speed up transactions, and internationalize markets.
BlockChain can be used for achieving the following in a Business Network Environment:
Companies Using Blockchain:
Tableau is a Powerful and Fastest growing Data Visualization tool used in the Business Intelligence Industry. Data analysis is very fast with Tableau and the Visualizations created are in the form of Dashboards and Worksheets.
Companies Using Tableau:
Mainly used to make creating Graphs faster and more efficient. API libraries for Python, R, MATLAB, Node.js, Julia, and Arduino and a REST API. Plotly can also be used to style Interactive Graphs with Jupyter notebook.
Companies Using Plotly:
TensorFlow has a Comprehensive, Flexible Ecosystem of tools, Libraries and Community resources that lets Researchers push the state-of-the-art in Machine Learning and Developers can easily build and deploy Machine Learning powered applications.
Companies Using TensorFlow:
Apache Beam provides a Portable API layer for building sophisticated Parallel-Data Processing Pipelines that may be executed across a diversity of Execution Engines or Runners.
Companies Using Beam:
Docker is a tool designed to make it easier to Create, Deploy, and Run applications by using Containers. Containers allow a developer to Package up an application with all of the parts it needs, such as Libraries and other Dependencies, and Ship it all out as One Package.
Companies Using Docker:
Apache Airflow is a WorkFlow Automation and Scheduling System that can be used to author and manage Data Pipelines. Airflow uses workflows made of Directed Acyclic Graphs (DAGs) of tasks. Defining Workflows in code provides Easier Maintenance, Testing and Versioning.
Companies Using AirFlow:
Kubernetes is a Vendor-Agnostic Cluster and Container Management tool, Open Sourced by Google in 2014. It provides a platform for Automation, Deployment, Scaling, and Operations of Application Containers across Clusters of Hosts.
Companies Using Kubernetes:
With this, we come to an end of this article. I hope I have thrown some light on to your knowledge on Big Data and its Technologies.
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