In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
In part one of the Capitalogix data science story, I focused on their strategic need for a data platform that supports speed, data variety and custom-built algorithms to find advantages for their business. A key success driver: they worked to make life better for the people on the front lines of
Owens-Illinois (O-I), the world’s largest manufacturer of glass containers, recently undertook a global migration from Oracle to Db2. Learn more about the migration and its success from O-I executives.
Big data doesn’t need to be a daunting challenge for small or midsized business (SMBs). Accessing, storing and exploring big data can be done by businesses of any size. An influx of data from sensors, streaming audio and video log files, web, and social media are increasing the volume, velocity,
A company only survives for 115 years by reinventing itself, questioning assumptions, and constantly looking for an edge. Owens-Illinois (O-I), the world’s largest manufacturer of glass containers, used worldwide by many leading food and beverage brands, recently began just such a reinvention.
The IBM Integrated Analytics System (IIAS), is a unique, cloud-ready appliance and machine learning platform wields the power of an in-memory, massively parallel processing database engine with embedded Spark. It also runs on market-leading IBM Big Data Servers and IBM FlashSystem 900 storage
Information analytics has never been a “one size fits all” proposition. That applies to the hardware and software technologies organizations employ, the information being parsed and the goals of specific projects.
Upon reading his own obituary in the newspaper, famed author Mark Twain is said to have remarked that reports of his death were greatly exaggerated. I can only imagine that if the data warehouse appliance were a 19th century American novelist, it might say the same thing. For a while now,
Dealing with slow technology is a major concern for anyone who needs quick access to analytic insights. For this reason, it’s vital to have a data warehouse appliance with sufficient speed that allows all users to make the most of its analytic power.