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Please answer the following questions 1. Is there an "authority" or designated body on the exact...

Please answer the following questions

1. Is there an "authority" or designated body on the exact meaning of "Big Data" and its solutions?

2. Commercial entities such as Google, Amazon, IBM, Oracle, Yahoo and others have their own often self-serving definitions that lead directly to their specific solutions and products. Select three (3) commercial companies that offer "big data solutions". Describe and critique their definitions of Big Data in a brief, 3-4-page report. Please cite all the references properly that you mention in your report (see the FAQ).

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What is Big Data: Big Data is not synonymous with lots and lots of data. Useful Big Data resources adhere to a set of data management principles that are fundamentally different from the traditional practices followed for small data projects. The areas of difference include: data collection; data annotation (including metadata and identifiers); location and distribution of stored data; classification of data; data access rules; data curation; data immutability; data permanence; verification and validity methods for the contained data; analytic methods; costs; and incumbent legal, social, and ethical issues. Why is use Big Data: - The big disruption from Big Data is not just the ability to capture and analyze more data than in the past, but to do so at price points that are an order of magnitude cheaper. As prices come down, consumption Big data solution offer: - There are many companies which are providing the big data solution with the better management tools. So, we have selected the three following company are below. 1. Google 2. Microsoft 3. AWS

Google:- Google is providing the cloud based the big data platform where we can use this platform find the bigdata solution.it is offer the access data storage, processing, and analytics on a more scalable, flexible, cost-effective, and even secure basis than can be achieved with an on-premises deployment. All these steps are required when data volumes are growing exponentially-to make storage and processing resources available as needed, as well as to get value from that data. Google provide the below platform to achieve the big data solution : - Big query: - it has the fully managed low cost serverless data warehouse which scale for Large storage requirement when if, and With Big-Query, you get a columnar and ANSI SQL database that can analyze petabytes of data in seconds. REST-based APls integrate with existing applications and developers can even create new apps using familiar programming languages like Java and Ruby.

Cloud Dataflow: - it is use for the transferring the large amount with in the environment in or out. Data-prep: it is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. Data-studio: - centrally management of data and also provide tools like spark Hadoop etc. Data-Transfer:-it is use for transferring of data in or out. AWS: Aws providing the data management challenges with velocity and variety of data cannot be solved with traditional databases It is providing the fully integrated management console for the build, secure, and deploy your big data applications and theres no hardware to procure, and no infrastructure to maintain and scale, so you can focus your resources on uncovering new insights. Also adding the new capabilities constantly. To achieving the big data solution using the different-different services in AWS. AWS RedShift: - using the redshift deploying the data warehouse for the large set of data. It is store data into the format of the column Data pipeline: - it has the capabilities to Transfer the large amount of data inside or outside the environment. EMR:-they are providing the tools or cluster of the Hadoop for analysis of data. For data stream has the apache kafka which is fully managed by AWS and also have the service for Amazon Kinesis for near real-time Big Data processing, for ingesting, transferring, and compressing data Amazon Athena: - ad-hoc query analytics and it has another service like Glue to automate extract, transform, and load (ETL) workloads

AZURE: Data-lake: -it is providing the solution for the data warehouse and storing the large or complex amount of data. HDINSIGHT: - it tools which is available in azure that provide the streaming technologies like the HBASE STORM SPARK etc. Data-lake: - it is an analytic tool which do the analysis of data in azure All the above the differ from each other on basis of compute colitis and pricing tier IREFRENCE] [https://cloud.google.com/] [https://azure.microsoft.com/en-in/ [https://docs.aws.amazon.com/index.html#lang/en_us]

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