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Developing a big data architecture today involves pulling together a lot of different technology pieces. The prevalence of unstructured data has forced the data warehouse as we knew it into a marriage of newer technologies -- Hadoop and NoSQL databases among them -- and older ones, such as columnar databases and in-memory processing tools.
This three-part guide offers IT managers, enterprise architects, data management teams and other readers insight on architectural strategies and advice on how to manage the architecture design and technology evaluation processes in today's big data environment. First, Jack Vaughan takes a look at one of the technology options in big data architectures: cloud computing. Vaughan uses one supermarket cooperative's success story to illustrate the potential benefits of a cloud-based big data platform. Next, Rick van der Lans digs deep into the steps required in designing, implementing and managing a big data architecture. While many IT professionals fear the death of the traditional data warehouse, van der Lans believes there is still a place for it in the world of big data. To close, Colin White and Claudia Imhoff identify the three components necessary for enterprise data warehouses to support next-generation analytics: an investigative computing platform, a data refinery and real-time analytics capabilities. Access >>>
Table of contents
- Supermarket co-op stocks up on big data system
- Data battle rides on strong technology, leadership
- BI architecture needs extensions to meet new demands
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