Analyze your way to business success. Learn more about data analytics and visualization from speakers like Marc Altshuller, General Manager, IBM Business Analytics, at Fast Track Your Data - live from Munich or join online June 22, 2017. Register now.
Imagine what you could build if you could leverage all the data that you couldn’t access before? Learn about hybrid data management from speakers like Nancy Hensley, Director, Growth & Strategy, IBM Analytics, at Fast Track Your Data - live from Munich or join online June 22, 2017. Register
Context-aware stream computing helps you become more responsive to emerging opportunities. By using innovative technologies to understand the context of data and analyze data in real time, you can put data to work.
IBM Analytics VP of Marketing Jeff Spicer sits down with Data Scientist and evangelist Dez Blanchfield to recap IBM InterConnect 2017 and give his insights into a few of the announcements from this year's event.
Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?
Data science is a team sport that involves specialists with complementary skills and aptitudes. Successful data science initiatives leverage high-performance team collaboration. Like the fictional sleuth and his partner, IBM’s customers in the data science community must have the right mix of
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
This white paper discusses the advantages of using the PySpark API, which enables the use of Python to interact with the Spark programming model. It starts with a basic description of Spark and then describes PySpark, its benefits, and when it is appropriate to use instead of "pandas" open source