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
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.
Watch this live chat with our second group of teams and participants from the #Hadoop4Good challenge to learn why they chose their projects, hear some of their challenges and delve deeper into the story behind their applications.
Watch this live chat with the teams and participants from the #Hadoop4Good challenge to learn why they chose their projects, hear some of their challenges and delve deeper into the story behind their applications.
IBM invited developers and data enthusiasts to take a deep dive into real world civic issues using big data and IBM Bluemix's analytics for Hadoop service. They analyzed one of our curated datasets or brought their own and used Hadoop to create clickable and interactive data visualizations
Two things before I begin:
I’ll begin this posting with a call for inputs. Below I will list a few of the most common Hadoop/Netezza co-existence deployment patterns we have seen to date. But I would like to hear from others. As you see the continuing deployment of Hadoop in the enterprise and