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
IBM hosted an exciting event for data and analytics leaders and practitioners this week in New York. At the IBM DataFirst Launch Event, we unveiled new solutions, tools, and approaches for organizations to transform themselves through cognitive computing.
IBM today announced exciting news about their Open Platform initiative with Apache Hadoop. And at its DataFirst Launch Event tonight, IBM will provide an early access view of the new IBM Project DataWorks with Watson, along with an introduction to DataFirst Services from IBM. IBM today also
Going forward, the businesses that truly disrupt their industries will be those who empower all of their personnel with open platforms, tools, and methodologies for data-driven app development. In that regard, this week’s announcement from IBM and our partners represent a key industry milestone.
When we look at all the uses of data organizations can embark upon, they fall into four main exercise groups of increasing benefit. Let’s go through each in turn and assess where your organization is in its journey to a healthier business.
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
Chris Snow, a data and application architect, enjoys helping customers with their data architectures and is working extensively on an open source app project in his spare time. Hear what Snow has to say about his IT experience spanning several industries, his current efforts with customers and his
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
Many organizations can capitalize on big data solutions and technologies to make use of expanded volumes of data for enhancing the critical decisions that drive successful business outcomes. And yet, a number of these enterprises can be inhibited from moving big data initiatives forward for a
And they said resilience—continuous data access in the face of outages, failures and downtime—across distributed data sources is impossible. Yet the recent IBM BigInsights release offers this capability in its IBM Big Replicate technology. Get an inside look at resilience in an interview with Jim
Thanks to the democratization of data, rising numbers of businesses are making highly insightful decisions that are producing beneficial real-world results. Catch a few highlights from a podcast that takes a deep dive into how open source analytics processing engines and high volumes of data are
Spark’s built-in machine-learning library (MLlib) provides a key differentiator from predecessor open source technologies and leverages Spark’s distributed, in-memory execution model. Take a look at some practical applications for specific Spark machine-learning algorithms in three advanced