Performing programmatic actions on data across services is quite possible in today’s technology ecosystem. And now, the transfer of data across services such as the dashDB data warehouse and deploying it in new environments is also possible. However, the questions often asked by customers center on
Spark’s momentum is building, and it is rapidly emerging as the central technology in analytics ecosystems within organizations. See why Spark’s technical advancements around iterative processing combined with its easy overall environment and tool set for developers make it a true operating system
Apache Spark not only excels at data warehousing, in-memory environments for building data marts and other functions, it also is well suited for pulling data from a wide range of sources and transforming and cleansing that data in an Apache Hadoop cluster. And then there is Spark’s complementary
An open ecosystem thrives on a mature core platform. It also depends on partnering arrangements that incentivize solution providers to continue developing standards-based interoperability around the shared environment. Take a deeper dive into recent announcements of new open ecosystem milestones
Organizations that don’t take the time to plan a strategy for implementation of a big data solution can fall into traps that impact long-term business goals. Discover five key steps organizations can take to implement a strategy for big data solutions that capitalize on Spark technology.
Stream computing combines data streams with an increasingly broad range of applications designed to help businesses solve problems of all kinds. Learn more about how you can capture data streams and infuse them into your applications.
Here are the quick-hit ponderings that I posted on various LinkedIn big data discussion groups this past week. I opened up three new themes–enterprise content warehouse, business process optimization, and big BI–and further developed the established themes of big data's optimal deployment model and