OLAP analysis techniques have existed for many years and enabled analyses such as customer profitability, product profitability, and other types of analysis. The explosion in the volume and variety of data has led to the fast adoption of Hadoop for storage, archival, and processing of large amounts of data. There is an increasing need to deliver analysis on this new data that’s originating from sources such as mobile, social, and IoT, but also tie it back to structured data sources such as ERP systems. This will enable enterprises to get contextual insights and enable precision decision-making. This is where OLAP analysis on big data comes into the equation.
The rise of mobile technology and Internet of Things (IoT) sensors in cars, industrial equipment, and medical devices etc. has resulted in an unlimited abundance of data. These data are increasingly semi-structured and unstructured in nature and are typically stored in Hadoop and NoSQL systems. New actionable insights from these fuel the business growth and competitive advantages for successful companies.
Businesses have turned to a new approach for data exploration and analysis through self-service data discovery. Organizations can assess data from multiple sources in multiple formats without IT involvement – spending less time, money and resources on gathering business-critical insights.
The MapR Data Exploration Quick Start Solution enables business analysts and data analysts to conduct analyses on larger and more diverse sets of data faster and formulate new hypothesis quicker than before. IT organizations can fulfill their promise of delivering analyses to business users faster and more efficiently.
Driving business value from Big Data has become a major business imperative for companies to remain competitive. One of the challenges organizations face today is taking their Hadoop applications and moving them into production work- flows as quickly as possible. To optimize the desired business impact of Hadoop, enterprise class job scheduling with traditional IT technologies and applications is required.
Data warehouse modernization takes many forms. Many users are diversifying their software portfolios, while others are even decommissioning current DW platforms in order to replace them with modern ones optimized for today’s requirements in big data, analytics, real time, and cost control. No matter what modernization strategy is in play, all require significant adjustments to the logical and systems architectures of the extended data warehouse environment.
The growing trend for big data—the dramatic volume growth in corporate and user-generated data—will create tremendous business opportunities for OEMs and service providers on the value/delivery chain for big data solutions and services. Big data growth will occur across all types of data and from boundless data sources.
Cisco and MapR Deliver Performance and Multi-tenancy to Help Tame Big Data
Big data provides an enormous wealth of information to your organization. But to gain the most benefit, you need to manage it efficiently. And you must make sure that all this data is separated and isolated so that each set of users can see and work on only the data that they are authorized to use.
Challenges of Multi-tenancy for Big Data