The combination of Jupyter Notebooks, Apache Hadoop and Apache Spark has become a killer app for data practitioners. It unlocks the ability to explore, visualize and experiment with both structured and unstructured data sets with great ease and efficiency. We spoke recently with Chris Snow at IBM
Automation can be a great solution for highly manual processes, but its implementation has its detractors. Can robotic process automation be successful in providing an artificial intelligence solution that includes machine learning for further streamlining typically manually intensive processes?
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.
Historical application of vector mathematics and the study of unstructured text data can be an important approach to understanding and actualizing the value of data. See how mathematical exploration of text data can unearth insight that translates into enhanced decision making.
Hear from Nancy Hensley, director of offering management for IBM Analytics who speaks with Rob Thomas, VP of development for analytics on the subject of business transformation and a discussion of the data maturity curve.
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.
Although formerly exclusive to the IBM Hadoop Platform, the extension of Big SQL to the Hortonworks Data Platform (HDP) meets the challenge of complex data warehousing queries on Hadoop. See what Paul Yip, worldwide product strategy for Hadoop and Spark at IBM, has to say about what this transition
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
Many marketing concerns have seen the light when it comes to the application of big data analysis as a means of outthinking the competition. Discover three best practices for implementing big data analytics for good data science in marketing initiatives.
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
Has your business adopted a hybrid analytics architecture as part of its quest to compete? Listen as Martin Fleming, IBM’s chief analytics officer and chief economist, explains why doing data analytics on the cloud is creating opportunities for modern businesses, and be sure to take notes as he
Hear Roger Rea, senior offering manager for IBM Streams, at IBM, share his thoughts in this podcast on why streaming analytics has become essential for enterprises and how it is transforming their businesses.
The evolving Internet of Things is fueling a rise in the adoption of streaming analytics across a growing number of industries. Learn more about the rising adoption of real-time streaming analytics in industries and top use cases cited in the recent “Bloor Market Report on Streaming Analytics 2016.”
The transformation of the streaming analytics market demonstrates how business process automation is being driven by innovative open source projects and an ever-increasing world of sensor-derived data. See how IBM fared among 14 commercial providers of streaming analytics in a recent Bloor Market