Some organizations misunderstand the optimized way to use Hadoop and Spark together, primarily because of their complexity. But investing in both technologies enables a broad set of big data analytics and application development use cases. See what Niru Anisetti and Rohan Vaidyanathan have to say
Ever hear of the Big Data Dudes? If not, crawl out from under that rock and see what these intrepid big data and analytics heroes are up to in their latest analytics blockbuster, "Big Data Dudes and the Lost in Las Vegas Mystery."
IBM has identified a number of common problems that many businesses find themselves facing in their various stages of Apache Hadoop and Apache Spark adoption. As a result, IBM has developed a set of support services to help customers accelerate time-to-value outcomes and reduce risk when building
To serve citizens effectively and efficiently, public entities can draw from the private sector’s 360-degree view of the customer and apply analytics, big data, Hadoop, machine learning and Spark to create a single or 360-degree view of the citizen. See how this methodology can empower public
Many forward-thinking organizations want to investigate how big data analytics helps them outthink and outperform the competition. However, many also are challenged with finding the right talent to run the operations, keep the data secure and figure out how to leverage the myriad tools at their
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
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
SparkOscope helps Apache Spark developers take advantage of the job-level information available through the existing Spark Web UI; minimizes source code pollution; and extends the Spark Web UI with a palette of system-level metrics about the server, virtual machine or container related to each
The inability of lines of business to not serve requests because they have to wait for IT provisioning can lead to a proliferation of analytics silos that can cause a loss of control of data. See how the next big stage of analytics with integrated Apache Spark helps organizations understand the
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.
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.
IBM Insight at World of Watson 2016, 24–27 October 2016, at Mandalay Bay in Las Vegas, Nevada, is the only place to be for people who work with data. Take a look at this list of top-ten reasons you wont’ want to miss out on one of the most intriguing and innovative events of the year.
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.