Use open-source tools to supercharge the data science lifecycle, giving data science teams a boost as they work to provide compelling results in the complex team environments that mark modern corporations. Learn how you can make open data science an ongoing part of your business environment when
Whether organizations want to extract customer data beyond names and addresses from unstructured data sources; pull specific dates, times or monetary amounts; predict trends from sentiment data; or engage in many other uses, text analytics is the way to go. Learn the details of text analytics, and
Data science takes collaborate teams of data scientists engaging in productive, open data development initiatives that can ensure strong workflow, governance, security and management. See why open environments are revolutionizing the data science landscape.
As Spark continues to mature into mainstream adoption in the data science community, the open data analytics stack and open source tools grow more robust, giving data scientists rich core workbenches to develop evermore innovative applications.
With BigInsights having established itself as a leader and with IBM focused on a Cloud First Strategy, we saw the opportunity to help customers reduce these capital and management costs, to enable them to focus on running the analytics for business advantage while providing BigInsights on a dynamic
A growing number of businesses and industries are finding innovative ways to apply graph analytics to a variety of use-case scenarios because it affords a unique perspective on the analysis of networked entities and their relationships. Gain an understanding of how four different types of graph
Do you want to win the race to insight and beat your competition? If so, it’s time to rev up your analytics strategy. Explore how analytics platforms fueled by trusted information, designed for hybrid environments, and built on open technology can put you in the winner’s circle.
Businesses can benefit enormously from analysis-derived rules that enable understanding why certain events occur and the corresponding actions to take. Learn more about a widely used six-phase methodology for building predictive analytics models that can reveal hidden rules for meaningful business
Spark just seems to be getting big play everywhere in the technology arena. What is Spark? And do you need it? Get a good glimpse into its in-memory execution capabilities, some of its key components, its integrations and its availability as a service.
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
The open source Hadoop framework accommodates distributed storage and processing of large data sets on clusters of computers through the use of programming models. If that description sounds complex, then dig into this breakdown of Hadoop components to gain an understanding of just how flexible
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
This short series of blogs for the business user is designed to turn key technologies into easy to understand concepts to help explain why they are needed in a modern digital enterprise. When looking at consumer and business transactions in today’s online world, many people may ask, “Why big data