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
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.
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
Open source is a disruptor that never quits, and it is seemingly penetrating and transforming every aspect of established data, analytics and application ecosystems. Give this podcast, recorded at IBM InterConnect 2016, a listen to learn how open source initiatives are transforming machine learning.
Open source is a disruptor that never quits. It seems to be penetrating and transforming every aspect of established data, analytics and application ecosystems. In this podcast, recorded at IBM InterConnect 2016, listen to David Taieb, a cloud data services developer advocate at IBM, share his
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.
Open source is a disruptor that never quits, and it seems to be penetrating and transforming every aspect of established data, analytics and application ecosystems. In this podcast, recorded at IBM InterConnect 2016, listen to Kamille Nixon, portfolio marketing manager at IBM, share her expert
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
https://www.ibm.com/cloud/db2-warehouse-on-cloudApache 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
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
In a podcast recorded at IBM InterConnect 2016, Roger Strukhoff, executive director, Tau Institute for Global ICT Research, shares his expert perspective on how open source initiatives are transforming the Internet Of Things.
In this episode of the Open for Data podcast, join Joe McKendrick in exploring the role of cloud computing in reshaping the global business environment — and find out how open source initiatives are driving that change.
Open source initiatives are reshaping the face of data science, bringing data professionals together to achieve previously infeasible goals. In this podcast, discover how the data science landscape is changing, transforming established business approaches to data.
Keep the insights coming from IBM InterConnect 2016 with this episode of the Open for Data podcast, in which Adrian Bowles of RTInsights shares his perspective on what open source initiatives mean for big data analytics.
IBM InterConnect 2016 may now be history, but the premier cloud and mobile conference was center stage for a historical abundance of announcements concerning cloud data services, open analytics, cognitive Internet of Things, and more.