Buyers and practitioners in the data market have faced a gut-wrenching choice: work with big data and tolerate the latency of batch-mode processing, or work interactively with relatively small data sets. To be sure, each of these two use cases has been a sweet spot for available technologies. Hadoop has excelled at storing huge volumes of data cheaply and processing it with the batch-oriented MapReduce algorithm; relational databases work interactively, but on smaller data volumes with more expensive storage economics.
Are organizations locked into this dichotomy? Can we have all our data and process it interactively too? Might we make Hadoop not just a cheaper data warehouse but also a radically-improved operational database? And, if so, what technologies are needed to make that happen?
Join Gigaom Research and our sponsor MapR for Beyond Batch Hadoop: Operational and Real-Time, a free analyst webinar, on Thursday, April 2, 2015 at 10 a.m. PT.
What Will Be Discussed:
- How big data technologies are used to handle high speed data streams and real-time processes
- Commodity storage economics and operational workloads
- “Fast Data,” beyond streaming data platforms
- Operational workloads with structured and semi-structured data
- Enterprise security, identity and operational big data
Who Should Attend:
- CIOs, CTOs
- Data managers/developers
- IT decision makers
- Business strategists and decision makers