Analysing big data gives companies insights that can have immediate and dramatic impact on their business, and this is why so many companies are now doing it. Having those insights gives you the edge over your competitors and provides a more complete picture of your business. However, to carry out big data analytics you need the computing resources in place for it to be done effectively. In this post, we’ll explain why we think the best solution is cloud analytics.
Unified Vision of the Business
One thing that causes problems for many companies is when different elements within the organization have disparate perceptions of what is going on. They are all working from their own data sets and no-one has the big picture.
A crucial advantage of using cloud analytics is its ability to consolidate big data from all sources and communication channels that a company employs. The capacity that cloud offers allows everything to feed in: you can gather large-scale data from all your internal apps, devices, social networks and data subscriptions – something which would be difficult to achieve in-house.
Using a cloud-based data management platform lets you easily blend data from a range of sources, enabling it to be matched, merged and cleansed – the result being far more accurate results that enable you to have a unified vision of your business. This vision can then be shared across the organization so that everyone has the big picture.
AWS Analytics products
Amazon Athena runs interactive queries directly against data in Amazon S3.
Amazon EMR deploys open source, big data frameworks like Apache Hadoop, Spark, Presto, HBase, and Flink.
Amazon Redshift fully manages petabyte-scale data warehouse to run complex queries on collections of structured data.
Google Cloud Analytics Products
Google BigQuery Google's fully manages low cost analytics data warehouse.
Google Cloud Dataflow unifies programming models and manages services for executing a range of data processing patterns including streaming analytics, ETL, and batch computation.
Google Cloud Dataproc manages Spark and Hadoop service, to process big datasets using the open tools in the Apache big data ecosystem.
Google Cloud Composer fully manages workflow orchestration service to author, schedule, and monitor pipelines that span across clouds and on-premises data centers.
Google Cloud Datalab is an interactive notebook (based on Jupyter) to explore, collaborate, analyze and visualize data.
Google Data Studio turns data into dashboards and reports that can be read, shared, and customized.
Google Cloud Dataprep is a data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis.
Google Cloud Pub/Sub is a serverless, large-scale, real-time messaging service that allows you to send and receive messages between independent applications.
Related Azure services and Microsoft products
HDInsight provisions cloud Hadoop, Spark, R Server, HBase, and Storm clusters.
Data Lake Analytics distributes analytics service that makes big data easy.
Machine Learning Studio easily builds, deploys, and manages predictive analytics solutions.
Summary
Cloud analytics is a cloud-based solution which enables businesses to carry out analysis or intelligence procedures through integrated cloud models, such as hosted data warehouses, SaaS business intelligence (BI) and cloud-powered social media analytics. It uses a range of analytical tools and techniques to help companies extract information from massive data and present it in a way that is easily categorised and readily available via a web browser.
QUESTION I : What stops IBM Watson to be included in the Wikipedia list as a good Cloud Based Analytics system?
QUESTION II : From client server systems to cloud systems has been a long journey. What's next please?
REFERENCE : Cloud Analytics Wikipedia, The Innovative Productivity of Cloud Analytics