10 Most Effective Big Data Technologies

© Copyright Comviva Technologies Limited. 2016
All you Need to know about:
Network Interoperability
Big Data Technologies

 Big data is simply large amount of data
 Data can be structured or unstructured
 Data is analysed to gain insights
 Insights help in making better business decisions
 Helps management to take tactical decisions regarding the future of their business
What is Big Data?

 Big data technologies help organizations take out meaningful information out of
 gigabytes of data.
 They are important tools to organize and analyse large volumes of data.
 Many big data technologies are available today, invented by the tech leaders of the
 world.
Big Data Technologies

 A part of advanced analytics, which makes predictions about what will happen in
future.
 Analysis is done through technology.
 Uses various techniques like statistics, modelling, artificial intelligence, machine
learning, data mining and others.
 All these methods are used to predict the uncertainties of business.
 Helps organizations to improve business performance and mitigate risk.
Top 10 Big Data Technologies
1. Predictive Analytics

 Often known as Non-SQL, Non-relational or not, only SQL database.
 Provides a platform for businesses to store and retrieve data by finding relationships
between various data groups.
 First introduced in the late 1960s.
 Gained popularity in the 21st century, when companies like Facebook, Google,
Amazon started using them.
 Simple in design and provides better control over available solutions.
2. NoSQL Databases

 Collects information and knowledge from various sources like databases, file
systems, streams, APIs, etc
 Delivers useful insights based on a specific context or need
 Information is collected through extensive search in these platforms and intelligent
search results are delivered. Simple and usable technology
3. Search and Knowledge Discovery

 An event processing engine where the event can be fed in real-time from a single
stream of data or multiple streams.
 Data is then filtered and analyzed to get multiple outputs which can be used in
business.
 Data can be fed from multiple sources also like applications, devices, sensors,
operation system, websites, etc
4. Stream Analytics

 An infrastructure software that slides between applications and data sources.
 Processes enormous amounts of data very fast to deliver real time results.
 Extremely good for high-performance transactions, real-time streaming,
comprehensive data processing and a variety of other tasks.
 In most cases, distributes data between dynamic random access memory (DRAM),
Flash and SSD of a computer system.
5. In-memory Data Fabric

 A technology where data is stored in various layers which are often similar in structure
 Ensures higher performance and lower redundancy
 In most cases, location agnostic which help to improve reliability and reduce the
complexity of different layers of data.
6. Distributed File Stores

 A technique by which data is retrieved and manipulated
 Does not take into account the technical details about the data like where is it located
or how was it found.
 Data is processed in real time or near real time basis.
7. Data Virtualization

 Data are combined using technical and business intelligence.
 Processed from different sources to bring out meaningful information.
 Information output is often termed as ‘trusted’ because of the way the same is
processed.
 This method uses solutions like Amazon Elastic Map Reduce (EMR), Apache Hive,
Apache Spark, Couch base, Hadoop etc.
8. Data Integration

 Manipulates data and makes it suitable for further processing and analysis.
 Manipulation is done through various tasks, both manual and automated.
 The preparation work is tedious, time-consuming and routine.
 Main intention is to reduce the burden of shaping and cleaning a diverse set of high
volume data for the analysts.
9. Data Preparation

 Refers to cleansing a large volume of data to ensure the output is of high quality.
 The high-quality data can then be used for operations, decision making, and planning.
10. Data Quality

 Big Data is here to stay.
 It’s growing faster than ever.
 By 2020, 1.7 megabytes of new information will be created every second of
every human on earth (Prediction).
 Total accumulated data today is 44 zettabytes. It will grow ten times by 2020.
 Every second 40,000 search queries are made on Google.
Future Roadmap

Disclaimer
Copyright © 2015: Comviva Technologies Ltd, Registered Office at A-26, Info City, Sector 34, Gurgaon-122001, Haryana, India.
All rights about this document are reserved and shall not be , in whole or in part, copied, photocopied, reproduced, translated, or reduced to any manner
including but not limited to electronic, mechanical, machine readable ,photographic, optic recording or otherwise without prior consent, in writing, of Comviva
Technologies Ltd (the Company).
The information in this document is subject to changes without notice. This describes only the product defined in the introduction of this documentation. This
document is intended for the use of prospective customers of the Company Products Solutions and or Services for the sole purpose of the transaction for which
the document is submitted. No part of it may be reproduced or transmitted in any form or manner whatsoever without the prior written permission of the
company. The Customer, who/which assumes full responsibility for using the document appropriately. The Company welcomes customer comments as part of the
process of continuous development and improvement.
The Company, has made all reasonable efforts to ensure that the information contained in the document are adequate, sufficient and free of material errors and
omissions. The Company will, if necessary, explain issues, which may not be covered by the document. However, the Company does not assume any liability of
whatsoever nature , for any errors in the document except the responsibility to provide correct information when any such error is brought to company’s
knowledge. The Company will not be responsible, in any event, for errors in this document or for any damages, incidental or consequential, including monetary
losses that might arise from the use of this document or of the information contained in it.
This document and the Products, Solutions and Services it describes are intellectual property of the Company and/or of the respective owners thereof, whether
such IPR is registered, registrable, pending for registration, applied for registration or not.
The only warranties for the Company Products, Solutions and Services are set forth in the express warranty statements accompanying its products and services.
Nothing herein should be construed as constituting an additional warranty. The Company shall not be liable for technical or editorial errors or omissions contained
herein.
The Company logo is a trademark of the Company. Other products, names, logos mentioned in this document , if any , may be trademarks of their respective
owners.
Thank You
Visit us at www.mahindracomviva.com

10 Most Effective Big Data Technologies

10 Most Effective Big Data Technologies

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to 10 Most Effective Big Data Technologies

Similar to 10 Most Effective Big Data Technologies (20)

More from Mahindra Comviva

More from Mahindra Comviva (20)

Recently uploaded

Recently uploaded (20)

10 Most Effective Big Data Technologies

Editor's Notes