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How big data is related to cloud computing

Big data is a large amount of data that is generated in the internet by using smart phones and other social networks or any other searches in our day to day life and this data is quite large for traditional computing systems to handle and this massive amount of data within the given time frame, what we term as big data. While referring the term big data we usually use the term big data to refer to the data that is either in gigabytes or terabytes or petabytes or exabytes or anything that is larger than this in size, this does not defines the term Big data completely because even a small amount of data can be referred to as big data depending on the context it is being used.

BigQuery:BigQuery is a fully managed data warehouse that removes setup hassles and runs queries rocket fast, fast enough to analyze terabytes of the data in seconds, petabytes in minutes. It works on a pay-as-you-go basis, that is you only pay for the bytes you processed. Google BigQuery encrypts, replicates and deploys your data across multiple data centres for maximum durability and service up time. Some of the benefits of BigQuery are you can control where you store your data, sharing and collaboration are easy as well, you decide who can access your data.
Google Cloud DataStore:Datastore is a service from Google that is highly scalable NoSQL database for your web and mobile applications. It is a set of various storage services offered by Google for different domain scenarios, it is a restful online file storage web service for storing and accessing data on Google’s infrastructure. Google Cloud storage allows worldwide storage and retrieval of any amount of data at any time.
Google Cloud Dataproc: Google Cloud Dataproc is a fast, easy to use, fully managed cloud service for running Apache Spark and Apache. Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or even days now take seconds or minutes instead. It also provides you a powerful and complete platform for data processing and machine learning.

Management Tools

Google Stackdriver:Provides performance and diagnostics data in the form of monitoring, logging, and tracing, error reporting, and alerting it to public cloud users. Stackdrivers moitors the clouds service layers in a single SaaS solutions. The benfits of Stackdriver is monitors multi cloud, Identify trends and prevent issues, lowers monitoring headaches, fix problems faster and reduces monitoring noise.
Google Cloud Console App: Cloud Console is powerful, all in one graphical tool to manage your Google Cloud Platform resources, regardless of their data centre location. It is a mobile application that enables customers to manage the key Google Cloud services from Google.

Identity and Security

Cloud Data Loss Prevention API: DLP is a technology that is active where information is being stored moved or used here at DP RMS we combine information rights managment with DLP for incredible data security. It provides a fast and scalable classification for sensitive data elements like credit card numbers, names, passport numbers, and more. DLP will scan all files and protect those documents with IRL to verify that the sensitive information is not getting into the wrong hands.

Cloud IAM: It refers to the ability to manage user identities and their access to IT resources from the cloud securely. It helps you control who is authenticated and authorized to use the services and resources. Cloud IAM is also called as identity management (IdM).

Cloud AI

Cloud Machine Learning Engine: Machine learning gives you the ability the chance to view your data in a whole new way. We use machine learning to optimize all our products, from real time traffic in maps, spam detection in Gmail and facial recognition in photos. The core activities in machine learning include data gathering, model building, training, evaluation and parameter tuning.

Cloud AutoML: A Machine Learning product that enables developers to provide their data sets and obtain access to quality trained models by Google’s transfer learning and Neural Architecture Search. It makes the work easier and it takes only few days instead of weeks to complete works. With AutoML tables, data scientists, analysts and developers can automatically build and deploy state of the art machine learning models on structured data by simply choosing a target for prediction.

IoT(Internet Of Things)

Internet of things referred to the communication capability of internet enabled devices that connect other internet enabled things in the internet. They collect and share data over the internet the way they designed to work. ioT is a huge network and it is growing every day.

Cloud IoT Core:   IOT digitizes physical assets sensors,devices, machines gateways and the network it connects people to things and things to things in real time. It permits utilization of other Google Cloud services for collecting, processing, analysing, and visualizing IoT data in real time.
Cloud IoT Edge:   Edge computing is being about placing workloads as close to the edge to where the data’s being created and where actions are being taken as possible. Edge computing supports to reduce the challenges of transmitting massive amount of data to the cloud and storing massive amount of data in the cloud.

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