How Big MNC's like Google, Amazon, Microsft etc stores , manages, and manipulate thousands of Terabytes of data with High Speedand High Efficiency

Big Data analytics have helped the organization to double its revenue in no time. An intelligent analysis of data is what you need if you wish to succeed in the coming years. Success is why almost all the top MNCs have adopted and started implementing big data practices for their databases.

Today we will see as to how these MNCs are using big data to their advantage. The blog covers the following topics-

Using Big Data Analytics to Boost Customer Acquisition and Retention

The utilization of big data enables organizations to watch different client related examples and patterns. Watching client conduct is essential to trigger devotion. Hypothetically, the more information that a business gathers, the more illustrations and patterns the company can probably recognize.

In the cutting-edge business world and the present innovation age, a business can, without much of a stretch, gather all the client information it needs. The client information implies it is straightforward the cutting-edge customer. Fundamentally, all that is essential is having a significant information investigation methodology to expand the knowledge available to you.

With an appropriate client information examination component set up, a business will have the capacity to infer primary conduct bits of knowledge that it needs to follow up on to hold the client base.

Use of Big Data Analytics to Solve Advertisers Problem and Offer Marketing Insights

Big data analytics can help change all business activities. The analytics incorporates the capacity to coordinate client desire, changing an organization’s product offering and guaranteeing that the showcasing efforts are incredible.

How about we face the stripped truth here. Organizations have lost millions spent in running ads that are not productive. For what reason is this incident? There is a high probability that they avoided the exploration stage.

Big Data Analytics for Risk Management

The extraordinary occasions and profoundly unsafe business condition calls for better chance administration forms. Fundamentally, a hazard of the executive’s plan is underlying speculation for any business paying little respect to this area.

Having the option to observe, in advance, a potential hazard and moderating it before it happens is necessary if the business is to stay beneficial. Business specialists will advise that a venture hazard the executives includes considerably more than guaranteeing your business has apt protection.

Big Data Analytics as a Driver of Innovations and Product Development

Another immense bit of leeway of enormous information is the capacity to help organizations improve and redevelop their items. Fundamentally, the vast information has turned into a road for making extra income streams through empowering advancements and item improvement.

Associations start by amending as much information as would be conceivable before planning new product offerings and re-structuring the current items. Each structure procedure needs to start from setting up what precisely fits the clients.

There are different channels through which an association can contemplate client needs. At that point, the business can recognize the best way to deal with it again by that need dependent on big data analytics.

Use of Big Data in Supply Chain Management

Big data offers provider systems more prominent exactness, clearness, and Insights. Through the utilization of enormous information investigation, providers accomplish logical insight over the supply chains. Fundamentally, through massive information investigation, providers can get away from the limitations confronted before.

Previously, the data was using the conventional undertaking management frameworks, and the store network the executive’s frameworks. These inheritance applications didn’t use enormous information investigation, and in this manner, providers brought about colossal misfortunes and were inclined to making mistakes.

Be that as it may, through present-day methodologies based on vast information, the providers can almost certainly use on more significant amounts of logical knowledge, which is essential for store network achievement.

Examples of how some MNCs are using Big Data Analytics

1. Amazon

Amazon.com founded in 1994 with headquarters in Washington. As of May 2017, it has a Market Capitalization of $427 billion and sales of $135.99 billion as per Forbes list. The total employee headcount as of May 2017 is 341,400.

Amazon is well known for its cloud-based platform. It also offers Big Data products and its main product is Hadoop-based Elastic MapReduce. DynamoDB Big Data database, the redshift, and NoSQL are data warehouses and are work with Amazon Web Services.

Big Data Analytics application can be built and deploy quickly using Amazon Web Services. These applications can be built virtually using AWS which provides fast and easy access to low cost IT resources. AWS helps to collect, analyze, store process, and visualize big data on the cloud.

Below is given a list of Analytics framework:

• Amazon EMR
• Amazon Elasticsearch Service
• Amazon Athena

The list given below is the real-time Big Data Analytics:

• Amazon Kinesis Firehose
• Amazon Kinesis Streams
• Amazon Kinesis Analytics

Amazon also provides Business Intelligence, Artificial Intelligence Internet of Things, Data Movement etc.

2. Microsoft

It is US-based Software and Programming Company, founded in 1975 with headquarters in Washington. As per Forbes list, it has a Market Capitalization of $507.5 billion and $85.27 billion of sales. It currently employed around 114,000 employees across the globe.

Microsoft’s Big Data strategy is wide and growing fast. This strategy includes a partnership with Hortonworks which is a Big Data startup. This partnership provides HDInsight tool for analyzing structured and unstructured data on Hortonworks data platform (HDP)

Recently Microsoft has acquired Revolution Analytics which is a Big Data Analytics platform written in “R” programming language. This language used for building Big Data apps that do not require a skill of Data Scientist.

Microsoft and Hortonworks have three solutions based on HDP:

1) HDInsight: It is cloud-hosted service and uses Azure cluster to run on HDP. It can be integrated with Azure storage

2) HDP for Windows: It is a configurable Big Data cluster that can be installed on the Windows server. It can also be installed on a virtual machine or physical hardware in the cloud

3) Microsoft Analytics Platform System: It allows data in Hadoop to be queried and can be combined with relational data. Such data can be moved in or out of Hadoop

3. Google

Google is founded in 1998 and California is headquartered. It has $101.8 billion market capitalization and $80.5 billion of sales as of May 2017. Around 61,000 employees are currently working with Google across the globe.

Google provides integrated and end to end Big Data solutions based on innovation at Google and help the different organization to capture, process, analyze and transfer a data in a single platform. Google is expanding its Big Data Analytics; BigQuery is a cloud-based analytics platform that analyzes a huge set of data quickly.

BigQuery is a serverless, fully managed and low-cost enterprise data warehouse. So it does not require a database administrator as well as there is no infrastructure to manage. BigQuery can scan terabytes data in seconds and pentabytes data in minutes.

Google provides below listed Big Data Solutions:

1) Cloud DataFlow: It is a unified programming model and helps in data processing patterns which include ETL, batch computation, streaming analytics.

2) Cloud Dataproc: Google’s Cloud Dataproc is a managed Hadoop and Spark service which easily processes big data sets using open source tool in the Apache big data ecosystem.

3) Cloud Datalab: It is an interactive notebook that analyzes and visualizes data. It is also integrated with BigQuery and enables to access to key data processing services.

4. VMware

VMware founded in 1998 and headquartered is in Palo Alto, California. Around 20,000 employees are working and it has a Market Capitalization of $37.8 billion as of May 2017. Also as per Forbes data, it has sales of around $7.09 billion.

VMware is well known for its cloud and virtualization but nowadays it is becoming a big player in Big Data. Virtualization of Big Data enables simpler Big Data infrastructure management, delivers results quickly and very cost-effective. VMware Big Data is simple, flexible, cost-effective, agile and secure.

It has a product VMware vSphere Big Data Extension which enables us to deploy, manage and controls Hadoop deployments. It supports Hadoop distributions which include Apache, Hortonworks, MapR, etc. With the help of this extension, the resource can be used efficiently on the new and existing hardware.

5. SAP

SAP is the largest business software company founded in 1972 with headquarters in Walldrof, Germany. It has a Market Capitalization of $119.7 billion with total employee count as 84,183 as of May 2017.

As per the Forbes list, SAP has sales of $24.4 billion and a profit of around $4 B with 345,000 customers. It is the largest provider of enterprise application software and the best cloud company with 110 million cloud subscribers.

The SAP provides a variety of Analytics Tool but its main Big Data Tool is the HANA-in memory relational database. This tool integrates with Hadoop and can run on 80 terabytes of data.

SAP helps the organization to turn a huge amount of Big Data into real-time insight with Hadoop. It enables distributed data storage and advanced computation capabilities.

SAP Big Data provides the following listed products:

1) SAP Predictive Analytics

• It uses a predictive algorithm and machine learning to anticipate the future outcome and guide the business in the right direction
• Using this technique thousands of predictive models can be created, deployed and maintained
• It automates data preparation, deployment of predictive modeling

2) SAP IQ

• Formerly it is known as Sybase IQ. It transforms business and enhances the decision making with SAP IQ
• It is an extremely scalable and robust security

3) SAP BusinessObjects BI

• It analyzes a high volume of data with greater performance
• It proactively grabs the new business opportunity and responds to potential threats

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