Top Big Data Technologies that you Need to know

Last updated on Jun 08,2023 72.6K Views
Tech Enthusiast working as a Research Analyst at Edureka. Curious about learning... Tech Enthusiast working as a Research Analyst at Edureka. Curious about learning more about Data Science and Big-Data Hadoop.
Become a Certified Professional

Top Big Data Technologies that you Need to know

edureka.co

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.

 

What is Big Data Technology?

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.

Become a Big Data expert with this Data Architect Certification course online.

Now let us have a look at the Categories in which the Big Data Technologies are classified:

 

Types of Big Data Technologies:

Big Data Technology is mainly classified into two types:

  1. Operational Big Data Technologies
  2. Analytical Big Data Technologies

 

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.

Get a better understanding of the technologies from the Big Data Hadoop Course.

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. You can get a better understanding with the Azure Data Engineering certification.

Few examples of Analytical Big Data Technologies are as follows:

 

 

Let us have a look at the top Big Data Technologies being used in the IT Industries.

 Learn more about Big Data and its applications from the Azure Data Engineering Training in Delhi.

Top Big Data Technologies

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. You can even check out the details of Big Data with the Data Engineer Course.

Let us get started with Big Data Technologies in Data Storage.

 

Data Storage

Hadoop

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:

 

MongoDB

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

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

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.

 

 

Now, let us move into Big Data Technologies used in Data Mining.

 

Data Mining

Presto

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:

 

Rapid Miner

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

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:

With this, we can now move into Big Data Technologies used in Data Analytics.

 

Data Analytics

Kafka

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

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

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:

Spark

Spark provides In-Memory Computing capabilities to deliver Speed, a Generalized Execution Model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development.

 

Companies Using Spark:

 

R-Language

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

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:

With this, we shall move into Data Visualization Big Data technologies

 

Data Visualization

Tableau

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:

 

Plotly

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:

now let us discuss the Emerging Big Data Technologies

 

Emerging Big Data Technologies

TensorFlow

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:

 

Beam

 

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

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:

 

Airflow

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

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.

For details, You can even check out tools and systems used by Big Data experts and its concepts with the Data engineer online course.

Now that you have understood Big data and its Technologies, check out the Hadoop training in Delhi by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. The Edureka’s Data architecture course helps learners become expert in HDFS, Yarn, MapReduce, Pig, Hive, HBase, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain.

BROWSE COURSES
"PMP®","PMI®", "PMI-ACP®" and "PMBOK®" are registered marks of the Project Management Institute, Inc. MongoDB®, Mongo and the leaf logo are the registered trademarks of MongoDB, Inc.