Hortonworks is an industry leading innovator that creates, distributes and supports enterprise-ready open data platforms and modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest.
Hortonworks is focused on driving innovation in open source communities such as Apache Hadoop, NiFi and Spark. Along with its 1,600+ partners, Hortonworks provides the expertise, training and services that allow customers to unlock transformational value for their organizations across any line of business.
SANTA CLARA, Calif., Aug. 7, 2018 — Hortonworks, Inc.® (NASDAQ: HDP), a leading provider of global data management solutions, today announced financial results for the second quarter of 2018.
It’s 2018, and business is changing before our eyes. Products and services are morphing and merging, drawing on each other to create a superior customer experience. This is the new world of the digital ecosystem.
According to Gartner, “data management architectures and technologies are rapidly shifting to become highly distributed.” This is because of the uptick in the types of data—and the amount of data—that organizations have to manage.
For CPG companies that are able to harvest and glean insights through big data analytics in retail, there is hope.
The future of manufacturing is inexorably linked to big data. In fact, Accenture suggests that the industrial Internet of Things (IoT) could add $14.2 trillion to the global economy by 2020.
In this paper, we will paint the overall landscape of IoT implementations, the key challenges that enterprises face in such environments, and the solutions that address such pain points.
Hortonworks DataFlow is a single combined platform for data acquisition, simple event processing, transport and delivery, designed to accommodate the highly diverse and complicated dataflows generated by a world of connected people, systems and things. This book introduces you to the HDF platform and how it relates to public sector missions.
As companies develop new use cases for the Internet of Things (IoT) and advanced IoT analytics, real-time collection of streaming data from sensors and other IoT devices is expected to grow 200% in the next few years. It’s a change that’s affecting both business and IT organizations, and existing IoT data management strategies are proving inadequate in many cases.
In the 1990’s, retailers built their e-commerce sites to emulate stores with the assumption that consumers would begin and end their shopping trip on that channel, just as they did in the store. But by the early 2000’s, consumers began exhibiting a new behavior, using the e-commerce channel to investigate solutions for their lifestyle needs even when they intended to complete their purchases in a store.
Apache Hadoop 3 is the third major revision of the big data distributed computing framework. The enhancements provide improved storage options as well as scalability and high availability features.
Robert Hryniewicz does a 12 min recap of the mini self-driving car Meetup (TensorFlow, Deep Learning, and Apache Hadoop 3.1)
Self-driving cars are here, and how they are deployed will change the world. In this session, you will see how a miniature race car can be powered by open source to faster time to innovation. Learn how data is captured, models are trained, created and deployed back to the car to improve functionality as new data is fed in. Witness how new innovations from Apache Hadoop 3.1 support bleeding edge solutions.
This video describes how to write a custom UDFs in Hive and the steps that needs to be performed.
This Video focuses on how to retrieve the curl commands for various tasks that are performed in Ambari UI.
HDP 3.0 helps accelerate time to market, support deep learning workloads, optimize storage efficiency, and comply with evolving regulations.