This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
Now that we’re into the swing of 2017, the time is ripe for the first CrowdChat of 2017 to explore the goals, challenges and strategies that CDOs and CIOs are focused on for their organizations. Get involved and share your thoughts in this kick-off IMB Big Data CrowdChat.
Why has IBM created its own distribution of Apache Hadoop and Apache Spark, and what makes it stand out from the competition? We asked Prasad Pandit, program director, product management, Hadoop and open analytics systems, at IBM to give us a tour of the reference architecture for IBM Open Platform
From self-service analytics to the cloud, chief data officers (CDOs) had a wealth of information at their fingertips on the first day of IBM Insight at World of Watson 2016. Catch the high points of some of Monday’s most relevant sessions for CDOs in this quick recap.
Data science seems to be experiencing a renaissance when it comes to advanced open source tools. Get a glimpse into creative application development with IPython Notebooks, Jupyter Notebooks, Apache Spark, the PixieDust open source library and more at IBM Insight at World of Watson 2016.
IBM extended Big SQL, which was formerly exclusive to the IBM Open Platform (IOP), to the Hortonworks Data Platform (HDP) in September 2016. I recently spoke with Berni Schiefer, an IBM fellow in the IBM Analytics group, to learn more about the offering and the ongoing IBM focus on SQL.
How can we ensure that metadata about all types of data is accurate, available, ubiquitous and universally accessible? Standards are certainly necessary, but we also need a new way to think about how metadata is created, managed and maintained.
Nancy Hensley, director of offering management for IBM Analytics speaks with Rob Thomas, vice president of development for analytics, at IBM, on the subject of business transformation, leading to a discussion of the data maturity curve.
The concluding week of September 2016 offered much excitement in New York City, the backdrop for Strata + Hadoop World 2016 and several key IBM announcements, including the launch of a cloud-based, self-service environment for data science teams. Enjoy some key highlights captured from this
Open data science initiatives can be a revolutionary force for innovation that spans diverse industries. And that force comes from the people in different roles and with various skill sets who use open source data science tools to develop and deploy new designs for working and living. Discover why
The productivity of data science teams—often challenged by access and formatting minutiae—can be enhanced by automating many of the manual tasks these teams need to process. Take a peek inside the mind of a data scientist, and see how acceleration of the data science development pipeline can boost
The importance of data science expertise, techniques and tools in a world rapidly employing advanced cognitive systems cannot be understated. Learn more about how business analysts, data scientists, data engineers, application developers and other professionals with analytical skills sets are using
Reimagine the data science experience as an open experience with this IDE, which aims to facilitate a full range of development tasks, from data acquisition and data mining to prototyping and programming. When you do, discover how you can use Apache Spark and R to pursue open analytics by building
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
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