Why is Spark so badly needed by the data science community? Primarily, it offers an open platform for fast, powerful data access that is vital for organizations because they are increasingly using a wide variety of technologies to deliver analytics, and they are tied to a variety of workloads.
The idea that data-at-rest (historical) and data-in-motion (immediate) are mutually exclusive no longer applies, thanks to a new toolset that handles both. Discover how organizations can have it all: the ability to stream data in real time as well as process historical data to highlight patterns in
As a strategic sponsor, IBM was represented in full force at Strata + Hadoop World 2015 in New York, New York. Day one proved to be a buzz of activity that included IBM data science experts getting a hands-on lab course on practical data science underway, IBM spokespeople discussing offerings and
Weather’s power over us is undeniable. But now big data analytics can help enterprises take advantage of forecasts to anticipate how the weather is going to influence consumers, and then measure, monitor, predict and execute strategy against a nonstop flow of weather data.
Attend IBM Insight 2015 to watch the semifinalists in the Hack the Weather hackathon present their projects to a panel of judges, and learn how to use weather-driven analysis to address your own analytics-related business challenges.
Data scientists may be of a different breed from other analytics team members, but they are essential for bringing to the table curiosity about data and an unquenchable thirst for finding patterns and relationships in that data. Discover how combining the roles of data scientist, business analyst,
As one principle of the buffalo theory demonstrates, open source projects are applying a process of natural selection through the manner in which they tackle performance bottlenecks and other obstacles that can prevent further technological advancement. By continually identifying and addressing the
Why are people talking about Apache Spark? It’s because many organizations are using the myriad features of this open source engine to boost their predictive analytics processing. The result? Better, deeper and faster data analyses with reduced coding time and effort.
If your organization is trying to make the most of its analytics capabilities, don’t miss these IBM Insight sessions—a presentation and a super session offering a deep look into the world of real-time analytics and discussing how to inject analytics into real-time operational systems.
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
For startups that want to thrive, leveraging data science is imperative. However, it has been difficult to do so because fragmented data science approaches have prevented integration of data analysis into applications. But now Apache Spark brings together the data science workflow to enable cross-
A document classification model can join together with text analytics to categorize documents dynamically, determining their value and sending them for further processing. Learn how a quick, efficient solution can create business advantage.
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