Big Data Analytics in the Cloud

Data analysis is a do-or-die requirement for today's businesses and it is highly important to take wise decision on efficient data storage strategy when it comes to Big Data analytics.

Hadoop, a framework and set of tools for processing very large data sets, was originally designed to work on clusters of physical machines. That has changed. Now an increasing number of technologies are available for processing data in the cloud.

The journey to cloud computing, and to its adoption in the enterprise, has been a rather manic-depressive one. The Platform as a Service (PaaS) model seemed especially enticing, because it freed developers from having to worry about any environmental factors other than their own applications.  Cloud computing has, conversely, worked very well for startups. Companies like Netflixand Airbnb run their mission critical infrastructure entirely in the cloud, for example. The cloud’s low barrier to entry, and its ability to scale up and down, with little necessary advanced planning or notice, is perfect for new companies that wish to grow their costs only as their revenue grows.

 There has been a significant growth in the cloud platform service providers given to the demand.  Examples include Amazon’s Redshift hosted BI data warehouse, Google’s BigQuery data analytics service, IBM’s Bluemix cloud platform and Amazon’s Kinesis data processing service. “The future state of big data will be a hybrid of on-premises and cloud.

Given the rigorous demands that big data places on networks, storage and servers, it's not surprising that some customers would outsource the hassle and expense to the cloud. Although cloud providers say they welcome this new business opportunity, supporting big data analysis in the cloud is forcing them to confront various, albeit manageable, architectural hurdles. The challenges of supporting customers demanding big data analysis in the cloud don't end with storage. Cloud providers say it requires a more holistic approach to the network and overall cloud architecture. Inspite of these, the cloud based demand has reached a threshold and the survey below explains that.

In one Survey, respondents aren’t so cloud-averse after all. A majority – 53% – of them are either using the cloud now (28%) for big data analytics or are planning to do so (25%). It is also noted that many experts believe that 30 percent of all computing workloads will be run on public clouds alone by 2018.

There are several advantages of Big Data Analytics in cloud -

1. Cutting costs with big data in the cloud - This allows firms to cut costs in terms of purchasing equipment, cooling machines and ensuring security, while also allowing them to keep the most sensitive data on-premise.

2. Instant infrastructure 

3 Performance - highest levels of data management performance has been improving at a quite high rate thanks to industry-leading Intel processors.

Thus given to the data growth at very high rate than expected it is highly important to rely on cloud based big data analytics which allows hassle free services and high performance.

References

http://searchcloudcomputing.techtarget.com/tip/Google-IBM-Oracle-want-piece-of-big-data-in-the-cloud

http://research.gigaom.com/2014/11/big-data-analytics-in-the-cloud-the-enterprise-wants-it-now/