How do businesses address the challenges of growing volume and variety of data? How can I introduce new data sources and workloads into my architecture? How do I achieve better time to value and agility in my infrastructure?
If you're wrestling with these and other related questions, I recommend you look into a newly released paper called, “Architecting a Big Data Platform for Analytics” by industry analyst Mike Ferguson of Intelligent Business Strategies. In this paper, Ferguson explores the requirements and considerations for building an enterprise big data analytic ecosystem. Considering more than 20 years of market maturity, the paper addresses the challenges facing businesses with multiple data warehouses and data marts, including:
- Growth in workload complexity
- Types of big data
- Recognizing different big data analytical workloads
- Technology options for end-to-end big data analytics
- Integrating big data into your traditional environment
- An enterprise strategy for big data analytics
- And how all of this affects building out a successful architecture
Central to the paper are descriptions of IBM’s end-to-end solutions to consider when building a big data analytics platform; one that includes capabilities for analyzing structured, unstructured and streaming data to help today’s business gain critical insight.
Furthermore, the paper highlights the recognition that workload characteristics vary greatly and require different types of systems optimized to fit specific needs, such as IBM’s newly announced PureData Systems.