Growth of Big Data AaaS
Big data consists of large unmanageable datasets. Current limits on data are in the range of terabytes, exabytes and zettabytes. As datasets grow in size, IT professionals have difficulties with capture, storage, analytics, visualizing and sharing. Internet search functionalities and business informatics are other major problems. Businesses need large datasets because trends can be identified easier with more information. Analyzed data allows business owners to make improvements to businesses. Improvements bring more productivity, efficiency, and profitability.
Primary challenges with big data originate from finding data, storing it, and accessing it. Analytics involve the manipulation of volumes of unstructured data, noise to signal conversion, algorithms and processing power. New tools must be created to address the challenges of big data. These tools include pattern recognition techniques, machine learning, improved processing power and parallelism and statistical analysis.
Big data AaaS is a completely hosted solution rather than simply a solution only on an in-house platform. When big data analytics is offered as a service, companies can share the expense of data analysis on large supercomputers. Big data AaaS is more affordable for businesses of all size. Not many companies offer big data AaaS, but the concept is growing in popularity. Most companies are still offering big data analysis platforms customized for individual companies. The processing power and expenses are simply too great for many small and midsized businesses (SMBs) to handle.
How Has Cloud Computing Changed Big Data Analytics?
Most SMBs cannot take advantage of big data because the processing power and storage required to process big is not affordable. Though most SMBs do not produce enough data to qualify for the category, SMB owners prefer to have the option available. For those companies with significant volume, cloud computing offers a viable solution.
Cloud computing is affordable and allows SMBs to use big data. Corporate enterprises can afford big data analytics, but many businesses do not leverage data completely. Corporations that accurately utilize data can predict future moves, make action plans and discover new information to indicate the direction of the company.
Currently, the U.S. Census Bureau, Google and World Bank have some of the largest sets of big data available. Traditionally, these organizations used computing farms and large storage arrays. Both solutions were expensive. Cloud computing was a welcomed solution for many organizations struggling with the expense and the processing capability of traditional solutions.
Since the amount of data generated increases globally every year by 40 percent, more companies will require a better solution. Otherwise, companies will continue to struggle with rising costs of new servers and upgrades to handle growth. Cloud computing is a viable and cost-effective solution.
Though cloud computing is a viable solution, many companies continue to raise concerns about data security and proprietary company information. Cloud computing companies are developing better encryption technology and other measures to protect company’s proprietary data.
Companies must look at the entire picture when evaluating big data analytics. The entire range of actions must be considered to provide a comprehensive solution of company data. When efficient solutions are provided for collection, storage, organization, analysis and sharing, enterprises have a competitive advantage over other enterprises without big data analytics solutions.
When big data analytics are offered as a service, improving business practices is easy and cost-effective. AaaS advantages include: Faster deployment, more computing power and less management. The biggest advantage is businesses only pay for the services used rather than having underutilized equipment, bandwidth or manpower.
Companies Offering Big Data Analytics and AaaS Solutions
Amazon’s Cluster Compute. Cluster Compute is a cloud-based supercomputer designed to handle massive amounts of data. Amazon is a leader in big data and analytics. The company has created demand for the cloud. Amazon has targeted customers and made better choices as a result of big data analytics.
Relevant recommendations have been made based upon big data analytics. These recommendations had significant impact and brought about sustainable change and profitability. When there is not enough data, recommendations have lead to mistakes.
IBM and Hewlett-Packard. Both IBM and Hewlett-Packard offer private cloud-based big analytics platforms. These types of platforms are not yet offered as AaaS. SMBs cannot take advantage of private big analytics platforms.
MapReduce. MapReduce supports large distributed data sets and processes both structured and unstructured data. The software is open source and allows enterprises to tailor each unique experience. The application facilitates the construction of scalable and reliable distributed systems.
Splunk Storm. This data analytics platform is designed to analyze incredibly large amounts of machine data. Multi-tenant solutions are the specialty of this company. When the costs are spread across multiple customers, big data analytics becomes more affordable. Over time, big data affordability will increase.
Aster Data. Aster Data offers an on-demand, cloud-based solutions for big data analytics. The service is provided is appliance-based.
1010 Data. 1010 Data offers enterprises a hosted big data analytics platform. This company is one of the few with a complete hosted solution for companies.
Consider Big Data Analytics as a Service in the Cloud
As technology improves, more companies will offer AaaS. Businesses will lose competitive edge without big data AaaS. Businesses are encouraged to consider big data AaaS to maintain a competitive advantage at a fraction of the cost.
Tags1010 Data AaaS Amazon Aster Data big data big data analytics Business Intelligence Christer Johnson cloud analytics cloud computing cloud computing jobs Cluster Compute data mining data scientist David Kuntz Direct Attach Storage EMC Forbes Hadoop Hewlett-Packard HP IaaS IBM Jake Levin Knewton MapReduce multi-tenant news.me Opera Solutions oracle ORCL PaaS Petabytes predictive analytics SaaS SMB Splunk IPO Splunk Storm Storage Array Network structured data unstructured big data US Census variety of data velocity volume of data