Analyzing streams of big data in real time can have a big impact on competitive advantage. In a world of bewildering stream processing engine choices, explore the use-case-dependent alternatives that can provide well-suited business outcomes, courtesy of expertise from Roger Rea and Jacques Roy.
Internet of Things data, devices and technologies are evolving into a core platform that is expected to impact business flexibility and more. Take a look at some key comprehensive best practices for Internet of Things–enabled application development that can put speed and agility into your business
Holden Karau is a software engineer at IBM, an active open source contributor and coauthor of Learning Spark (O'Reilly Media, February 2015) and the soon to be released High Performance Spark (O'Reilly Media, March 2017). In this podcast, Karau examines how to effectively search logs from Apache
Nick Pentreath is a principal engineer at IBM, a member of the Apache Spark project management committee (PMC) and author of Machine Learning with Spark (Packt Publishing, December 2014). In this podcast, Pentreath covers the basics of feature hashing and how to use it for all feature types in
Today’s businesses need a culture of collaboration that empowers knowledge workers to glean cognitive insights from data that help transform and modernize operations. See how cloud-based platforms and solutions enable data scientists and other experts to exploit artificial intelligence, machine
Emily Curtin is a software engineer at The Weather Company (now IBM) working on the data engineering platform team. Robbie Strickland is vice president, engines and pipelines, IBM Watson Data Platform, at IBM. In this podcast, they give a technical overview of how Parquet works and how recent
In a recent CrowdChat discussion, a group of Hadoop and Spark subject matter experts from the IBM Analytics group discussed using cloud-based Hadoop and Spark services as a lever for business agility. From their contributions we drew ten hot topics and themes for experts in all areas of the big
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