In a world built on sensors, businesses that want to act on data at scale require analytics solutions capable of gaining insights from data in real time, allowing them to make the right decision at the right time.
Game show contestant, medical science researcher and now a legal assistant, IBM Watson continues to expand its cognitive talents into a wide range of professions. Discover why even Watson’s gaffs offer tremendous potential for enhancing tough legal decisions.
So you want to enter the data science field, or maybe you are already a data scientist looking to expand your horizons. Several routes into the profession can provide the core skills, knowledge and best practices necessary to become a developer in the era of cognitive computing. And events such as
At developerWorks Recipes from IBM, novices and experienced developers can access and contribute powerful Internet of Things recipes. This step-by-step tutorial offers a head start on IoT or other applications that connect hardware, run analytics, use machine learning and more.
Find out how Day 3 of the conference offered insight into how data scientists have benefited from the latest approaches to web-scale analytics, including open sourcing of the System ML machine learning library to help the Spark community.
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.
The current Insight Economy demands a never-ending stream of data-driven discovery. Enter the data scientist—professionals who answer this demand by tapping into big data analytics to uncover emerging trends that help transform organizations.
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
Time series data can contain highly valuable insights—if organizations can detect and classify the events within it. An approach that combines stream processing and machine learning holds the key to analyzing large, fast data streams.
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.