Big data is the big opportunity for VARs in today’s market, but understanding the nuances of big data implementation can be challenging. An estimated 55 percent of big data projects failed in 2013 thanks to various obstacles to big data implementation: data acquisition, storage, processing, security, lack of skills, and improperly defined project parameters. In the past, we have offered a list of big data white papers that would help you better understand the issues driving big data adoption. We wanted to offer another list of big data white papers to help you better understand some the issues behind big data architecture, which can translate into big profits.
1. “The Critical Role of the Network in Big Data Applications” – Cisco and IDC
IDC notes that data creation is growing at an average of 66 percent per year, and those organizations that are best able to make real-time business decisions using that data will have a competitive advantage. “As Big Data efforts grow in scope and importance, the network (both within the datacenter and across the WAN) will play a critical role in enabling quick, sustainable expansion while also ensuring these systems are linked to existing mission-critical transaction and content environments.” In this big data white paper, IDC discusses strategies to move beyond existing data warehouse and business intelligence environments to enable new data modeling and integration of new data channels. Specifically, today's big data-enabled network needs:
- The ability to assess mixed data (both structured and unstructured) from multiple sources.
- The ability to handle unpredictable content with no apparent structure.
- A new kind of software/storage/computing infrastructure that can access, validate, and analyze large volumes of data.
This big data white paper discusses network optimization strategies, including how to create a unified fabric.
2. “Big Data In the Enterprise – Network Design Considerations” – Cisco
According to estimates, the average enterprise will have to handle 50 times more data by 2020 than it handles today; growth exceeding Moore’s Law. This big data white paper presents architecture information based on real network traffic patterns in a Hadoop framework. It also discusses new trends affecting the enterprise such as mobile data access, the ubiquitous access of data from multiple channels, and changes in the network ecosystem to accommodate an open source framework (Apache Hadoop) and unified network int4egraiton.
3. “Architecting a Big Data Platform for Analytics” – IBM and Intelligent Business Strategies
Michael Ferguson of Intelligent Business Strategies penned this big data white paper focusing on tackling new data sources and increased workloads in the enterprise. In the past, data warehouses have been designed to the same pattern: capture, clean, transform, and integrate data from various sources before storing it. Ferguson offers his views on the evolution of enterprise big data analytics and the need to create a new kind of ecosystem that advances analytics and accommodates new data sources such as mobile and collaborative business intelligence.
4. “Customer Analytics: The Role of Integrated Systems” – IBM and IDC
This big data white paper discusses the need for analytics for sales, marketing, and customer service decision makers. It examines how organizations can improve customer analytics to extract better, actionable intelligence. It also explores how enterprise networks can be workload optimized to enable new kinds of customer analytics.
5. “Introduction to Big Data Infrastructure and Networking Considerations” – Juniper Networks
For more specific information about creating a Hadoop architecture, this big data white paper provides a starting point for designing with Hadoop. It is written as a primer in big data and Hadoop and is an excellent place to start if you are still new to big data. Topics covered include bog data platforms, traffic patterns, scalability, switching fabric design, and more.
6. “Unleashing the Business Value of Data Defined Storage” – Tarmin and Ingram Micro
Data-defined storage is a new data-centric methodology to integrate applications, data, and storage in a new management architecture that opens new possibilities for big data analytics. For example, in environments that require strict data control for compliance, such as health and financial services, data-defined storage makes it possible to index and classify data to streamline data access, including for regulatory compliance reporting as well as big data analytics. This big data white paper talks about how to apply data-defined storage to improve overall efficiency, reduce business risk, and improve decision-making.
New big data integration and design strategies are emerging all the time and we are committed to helping you stay abreast of the latest big data management tools and techniques. Have you read any good white papers lately? Tell us what you have found in your research, and tell us what we can share to keep you better informed to make the most of big data opportunities.