GigaSpaces states 80% of IT executives in a survey are considering moving their big data analytics to one or more cloud delivery models. For some organizations which already have an internal private cloud, big data analytics can be a part of their offerings in-house, or else they can host it externally through the help of an IaaS provider.
Why cloud infrastructure makes sense for big data?
- Big data by nature demands a cost effective infrastructure set up, and cloud offers the best opportunity to meet its demands
- Big data may mix internal and external sources to draw upon data. In this scenario, it would work to use a cloud infrastructure solution so as to enable analysis of data at its resident location. This also allows for segregating confidential information internally and analyzing different data using the public cloud
- Big data analytics requires data services varying greatly on the usage scenario and requirements. Hence, it would be most suited for the processing of these great volumes of data to be performed on the cloud
The new age open source technologies changed the culture of collaborative software development, and allowing for data to be stored, managed and analyzed in ways which were not possible with the traditional technologies such as the RDBMS. NoSQL is one such technology which has become immensely important with big data and its analysis. One of the issues with large enterprises is that these new age technologies do not meet enterprise application requirements. It is essential for large enterprises to have a dependable, fully- tested solution which can be immediately deployed.
Hadoop as a Service
IaaS offers exceptional performance, assured availability, manageability and scalability. Enterprises involved in big data analytics cannot maintain hardware and software required for such performance nor do they prefer to take up the costs associated with it. It makes complete economic sense to look for a solution wherein this solution comes to the cloud. Hadoop, an open source solution, developed originally at Yahoo in 2005, employing distributed data storage and processing was put forth primarily to crawl and index the internet. Today, hadoop is a solution to process large scale data in a distributed manner, and the go-to technology for big data analytics.
Hadoop can be assimilated with cloud, either in the Platform as a Service (PaaS) offering or Software as a Service (SaaS). Many providers offer hadoop integrated cloud offerings, but managing and utilizing these big data solutions is still a major concern for many businesses.
Amazon ElasticMapReduce offers a basic hadoop offering in a PaaS environment which can be used in financial analysis, data warehousing, web indexing, bioinformatics, machine learning etc.
HDInsight, a data service from Windows Azure, brings hadoop to the cloud coupled with PowerPivot, Power View, Power Map and other Microsoft BI tools.
Google Cloud Platform allows for per minute billing optimized for scale and speed, which is a big deal when working with hadoop.
Qubole, a software as a service offering, breaks down the complexity of setting up jobs using hadoop by providing a simple web interface for this purpose, allowing for hadoop being accessible to a wider range of users.
Our team is can transfer day-to-day related responsibilities as a method to improve production workloads, building, deploying, and supporting any Hadoop platform and solutions. Coupling the Hadoop delivery and our managed services, allows businesses to accelerate delivery and build customizations as per business requirements. Our experts can help offset the lack of skills in-house and create a road-map for successful implementation and support for your big data infrastructure as a service requirement.
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