Spark’s built-in machine-learning library (MLlib) provides a key differentiator from predecessor open source technologies and leverages Spark’s distributed, in-memory execution model. Take a look at some practical applications for specific Spark machine-learning algorithms in three advanced
Deriving actionable insight from data and analytics is shifting to unified, cloud-based platforms that can be used by a variety of analysis personas. Take a look at a national retail chain scenario demonstrating how a comprehensive portfolio of end-to-end analytics in the cloud can provide the
Machine learning is finding its way into a variety of applications. Discover an open source machine learning platform that combines the data processing power of Spark with powerful machine learning algorithms courtesy of the H2O platform to tackle challenges technologists face when applying machine
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
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.
With BigInsights having established itself as a leader and with IBM focused on a Cloud First Strategy, we saw the opportunity to help customers reduce these capital and management costs, to enable them to focus on running the analytics for business advantage while providing BigInsights on a dynamic
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
As a foundation for data lakes and refineries, NoSQL databases provide access, processing and storage to structured and unstructured data for high-performance statistical modeling and exploration. Take a look at the multitude of advantages of NoSQL databases and opportunities to bridge them to open
Performing programmatic actions on data across services is quite possible in today’s technology ecosystem. And now, the transfer of data across services such as the dashDB data warehouse and deploying it in new environments is also possible. However, the questions often asked by customers center on
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.
Spark’s momentum is building, and it is rapidly emerging as the central technology in analytics ecosystems within organizations. See why Spark’s technical advancements around iterative processing combined with its easy overall environment and tool set for developers make it a true operating system
In the past few years, we’ve seen an explosion in the number and variety of organizations that are adopting big data technologies such as Hadoop and Spark and the recent trend to leverage data services in the cloud. How are enterprises coping?