Advances in tools and the capability to work with cloud-based data sets are dramatically changing the nature of data science workloads. Take a look at one data scientist’s quest to learn more about performing data science analysis in the cloud.
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.
Despite big data’s hype, a significant number of organizations are still in a holding pattern—either locked in planning, hesitant to get started or wanting to avoid Apache Hadoop and Apache Spark projects. Complexity and a shortage of skills can exacerbate the situation. Increasingly, organizations
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
Apache Spark 2.0 and Apache SystemML are now available in IBM BigInsights for Apache Hadoop on Bluemix, accessible in the Basic plan under BigInsights for Apache Hadoop. Choose from among a wider array of capabilities than ever before as you take advantage of these additions to your cluster
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 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
Developers and data engineers’ horizons are broadening with the advent of technologies designed to help them build and deploy analytics applications quickly and easily. Take part in the open beta of the Basic Plan for IBM BigInsights on Cloud to find out what lies in your future as you build
Data scientists and others often encapsulate big data by its dimensions known as the four Vs: volume, variety, velocity and veracity. But when considering big data as a source for insight to enhance decision making, it may be best characterized by its three Cs—confidence, context and choice—with
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
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.