Question

what are the Drivers for Cloud analytics and big data?

Answer 1

A Cloud Analytics strategy is one that:

• Supports the use of platform-as-a-service tools to deliver data-based analytics to discover key business trends

• Predicts positive and negative business outcomes and suggests how to achieve and/or avoid them

• Drives actionable results with data-backed decisions

Analytics is a cutting-edge industry, with lots of new tools evolving quickly. Cloud platforms, like Microsoft Azure, are also cutting edge and enable a level of agility that is not possible with on-premises installations. This fact allows analytics and cloud to fit together very well. For most of our customers, their analytics strategy and cloud strategy intersect at some level.

Some factors cloud analytics :

Many analytics tools are complex, with a large ecosystem of community-built packages. Large ecosystems require constant administration. Going to a cloud platform minimizes and/or removes administration requirements.

• As analytic models mature, the tools mature with them. Cloud technology moves quickly, with updates coming more often than on-premises architectures. New features arrive weekly or monthly.

• Analytic models are subject to change as business requirements are refined, or as activities happen in the real world. Using a cloud platform imbues your analytic model with the agility it needs to react to an ever-changing workload.

• Analytic solutions built using a cloud platform generally benefit from a faster time-to-market than those built on-premises using traditional infrastructure. Because these solutions minimize the focus of acquiring and configuring infrastructure, projects are often “off the ground”, quickly leading to realized business value during the first weeks of a new project.

• Scaling your solutions doesn't require a budget council meeting. Cloud is hyper-scale. When you need to grow your solution from 100GB to 100TB, you don’t need to ask for a large briefcase of money. Operational costs are easier to budget for.

• Data Analysts and Data Scientists tend to work on the cutting edge. Cloud platforms also tend to stay on the cutting edge, meaning your analyst teams are always able to work with the latest technology.

• Integration between development cycles and deployment cycles are built into the cloud platform. Often, the deployment process is built into the development process of cloud platforms. Your business analytics team will be able to manage their own release schedules and ensure that the right answers are always ready.

As data is increasing day by day and it is problematic when 90% of the data is unstructured so tan open source framework comes into picture Hadoop which helps in dealing with bulk amount of data. Hadoop was traditionally being used to process large batch jobs. But Apache Spark, Apache Drill, Impala, and others built upon this platform to make data more accessible in a fast and interactive way. there are 4-Vs in analytics (Volume, Variety, Veracity, Velocity).

Basically the idea of cloud analytics and Bigdata comes from a problem which google were facing in the early ages of their search engine to store the web pages information after crawling and perform analysis on that data the data from web pages increased up exponentially, it was a challenge to handle that amount of data with analysis result power to be in seconds, so technologies like cloud analysis and Hadoop comes up with this kinda problem.