4. 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.
5. 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
6. 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.
