Use open-source tools to supercharge the data science lifecycle, giving data science teams a boost as they work to provide compelling results in the complex team environments that mark modern corporations. Learn how you can make open data science an ongoing part of your business environment when
Whether organizations want to extract customer data beyond names and addresses from unstructured data sources; pull specific dates, times or monetary amounts; predict trends from sentiment data; or engage in many other uses, text analytics is the way to go. Learn the details of text analytics, and
Data science takes collaborate teams of data scientists engaging in productive, open data development initiatives that can ensure strong workflow, governance, security and management. See why open environments are revolutionizing the data science landscape.
As Spark continues to mature into mainstream adoption in the data science community, the open data analytics stack and open source tools grow more robust, giving data scientists rich core workbenches to develop evermore innovative applications.
A growing number of businesses and industries are finding innovative ways to apply graph analytics to a variety of use-case scenarios because it affords a unique perspective on the analysis of networked entities and their relationships. Gain an understanding of how four different types of graph
Businesses can benefit enormously from analysis-derived rules that enable understanding why certain events occur and the corresponding actions to take. Learn more about a widely used six-phase methodology for building predictive analytics models that can reveal hidden rules for meaningful business
Does your data science need a shot in the arm? Attend Datapalooza, a one-of-a-kind globetrotting data science festival that aims to bring together data scientists from all walks of life for two and a half days of education and application. Find out when Datapalooza will be coming to your area, then
Spark just seems to be getting big play everywhere in the technology arena. What is Spark? And do you need it? Get a good glimpse into its in-memory execution capabilities, some of its key components, its integrations and its availability as a service.
Evolving Internet of Things technology is paving the way for ongoing innovation—from smart thermostats and smart cars to other smart devices—through continuous access to its data streams. And while ongoing access to that data inspires the magic behind Internet of Things solutions, it also offers an
Metadata and governance might not have a long history of setting hearts ablaze, but those who are recognizing their importance to self-service are looking to metadata to help organizations fully leverage a wide range of assets across the business. Discover why metadata and governance are taking a
The open source Hadoop framework accommodates distributed storage and processing of large data sets on clusters of computers through the use of programming models. If that description sounds complex, then dig into this breakdown of Hadoop components to gain an understanding of just how flexible
Apache Spark not only excels at data warehousing, in-memory environments for building data marts and other functions, it also is well suited for pulling data from a wide range of sources and transforming and cleansing that data in an Apache Hadoop cluster. And then there is Spark’s complementary
Over the past two years, several new entrants to the market and existing players have been rapidly building offerings, and the use cases for streaming analytics are pervasive. The Forrester Wave™: Big Data Streaming Analytics, Q1 2016, reports “Streaming analytics are critical to building