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Combining Cloud Integration and Big Data in the Cloud to Accelerate Analytics

An explosion in mobile devices and social media usage has been the driving force behind large brands using big data solutions for deep, insightful analytics.  In fact, a recent mobile consumer survey found that 71% of people used their mobile devices to access social media.

With social media becoming a major avenue for advertising, and mobile devices being the medium of access, there are numerous data points that global brands can cross-reference to get a more complete picture of their consumer, and their buying propensities. Analyzing these multitudes of data points is the reason behind the rise of big data solutions such as Hadoop.

Related: Informatica Cloud’s Marketplace connectors for Cloudera Hadoop CDH 4.1 and Hortonworks Hadoop HDP 1.1

However, Hadoop itself is only one Big Data framework, and consists of several different flavors. Facebook, which called itself the owner of the world’s largest Hadoop cluster, at 100 petabytes, outgrew its capabilities on Hadoop and is looking into a technology which would allow it to abstract its Hadoop workloads across several geographically dispersed datacenters.

When it comes to analytics projects that require intensive data warehousing, there is no one-size fits all answer for Big Data as the use cases can be extremely varied, ranging from short-term to long-term. Deploying Hadoop clusters requires specialized skills and proper capacity planning. In contrast, Big Data solutions in the cloud such as Amazon RedShift allow users to provision database nodes on demand and in a matter of minutes, without the need to take into account large outlays of infrastructure such as servers, and datacenter space. As a result, cloud-based Big Data can be a viable alternative for short-term analytics projects as well as fulfilling sandbox requirements to test out larger Big Data integration projects. Cloud-based Big Data may also make sense in situations where only a subset of the data is required for analysis as opposed to the entire dataset.

With cloud integration, much of the complexity of connecting to data sources and targets is abstracted away. Consequently, when a cloud-based Big Data deployment is combined with a cloud integration solution, it can result in even more time and cost savings and get the projects off the ground much faster.

We’ll be discussing several use cases around cloud-based Big Data in our webinar on August 22nd, Big Data in the Cloud with Informatica Cloud and Amazon Redshift, with special guests from Amazon on the event.

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One Response to Combining Cloud Integration and Big Data in the Cloud to Accelerate Analytics

  1. Pingback: Combining Cloud Integration and Big Data in the Cloud to Accelerate Analytics | Dan Gorman's Technology News

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