Hadoop is great for storing and processing large data volumes, but its limits become clear when integrating ever-increasing volumes of data. A new solution—described in detail at the upcoming Strata+Hadoop World conference—can help organizations overcome this limitation.
While some observers may argue that Apache Spark is causing the relevance of the Apache Hadoop community to wane, the fact of the matter is innovative Spark development depends on Hadoop platforms. Discover why Hadoop is stronger than ever as an open source information refinery that is expected to
Big Data & Analytics Heroes
Fern HalperResearch Director for Advanced Analytics at TDWI
Fern Halper discusses her thoughts on the biggest challenges for companies when they decide to get started with big data and analytics and explains why she believes tomorrow’s generation should acquire analytic skills, no matter the degree—because it’s going to be hard to escape the data deluge.
When customers or other key stakeholders expect to be able to connect with an organization instantaneously, extremely low latency, high throughput data and analytics flows and execution are absolutely essential. The advent of the Internet of Things is among several key drivers of the emergence of
Streaming analytics is becoming ubiquitous as data streams from a range of sources, including the Internet of Things, are now mainstream. Although streaming analytics is not a new technology, it is well suited for today’s real-time, low-latency business and consumer applications. And today’s data
An ever-changing business environment is forcing data professionals to rethink their work methods—but fortunately, help is available. Here are five trends that are making life easier for data professionals: the emergence of Apache Spark, opportunities for greater skill reuse, growth in online and
Apache Spark will become a core technology in the logical data warehouse (LDW), and its sweet spot is as the workbench of choice for data scientists who interactively and iteratively explore, build and tune statistical models for machine learning, graph and streaming analytics.
Big data without context is pretty much useless, especially when that context can fluctuate so widely—which is why the role of Hadoop in creating accurate analytics is crucial for deriving value from big data.
IBM is investing deeply in Spark in a wide range of long-term initiatives. Discover how IBM’s long history of joining powerful, innovative open-source projects allows it to create markets by contributing significant technological improvements and supporting business solutions.
An open-source software platform called Apache Spark is growing rapidly in popularity as an essential platform for rapidly modeling, exploring and analyzing data. Here are nine reasons why developers and data scientists are primed to #SparkInsight with Spark.
On Tuesday, I plunged right back into Spark Summit—which, if anything, was buzzing more vigorously with interesting content than it had been the day before. Not surprisingly, IBM’s Spark announcements were the talk of the show.
A growing body of fresh thinking is coming down the pike. Much of it will come from the droves of IBMer data scientists who participated in the recent and wildly successful internal Hack Spark Challenge, as well as ongoing IBM-sponsored hackathons, meetups and developer days focusing on Spark.
Speed seems to always be at least one of the key factors in the evolution of any technology. The in-memory, real-time processing capability of Spark is rapidly advancing fast-cycle big data processing that supports a broad range of workloads.
IBM made several, significant announcements signaling its commitment to providing an open, mature, innovative industry Apache Spark ecosystem to accelerate its adoption. Take a detailed look at why IBM is making a huge, strategic bet on Spark.