At the core of many big data architectures is Apache Hadoop and Apache Spark. Organizations adopting these technologies for their big data journey are nevertheless at different levels of maturity. Hear what Prasad Pandit had to say in an interview with Andrea Braida about how IBM is evolving its
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
Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver the main talk of the Spark & Machine Learning Meetup in Brussels, "Creating an end-to-end Recommender System with Apache Spark and Elasticsearch."
At the recent Spark & Machine Learning Meetup in Brussels, Holden Karau of the Spark Technology Center delivered a lightning talk called "A very brief introduction to extending Spark ML for custom models."
At the Spark & Machine Learning Meetup in Brussels on October 27, 2016, Pierre Borckmans of Real Impact Analytics delivered a lightning talk called "Writing Spark applications, the easy way: How to focus on your data pipelines and forget about the rest."
Discover some of the latest and greatest developments in the world of Apache Spark in this retrospective on Spark Summit Europe 2016. When you do, find out how modern developers are connecting their data sources to Spark in exciting ways, and learn how the cloud is providing access to Spark-powered
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
Apache Spark, sometimes called the “analytics operating system,” is empowering organizations of all kinds through machine learning by helping them create unprecedented value from their data. Discover eight ways that Apache Spark’s machine learning capabilities are driving the modern business.
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.