Since its beginning as a project aimed at building a better web search engine for Yahoo – inspired by Google’s well-known MapReduce paper – Hadoop has grown to occupy the center of the big data marketplace. From data offloading to preprocessing, Hadoop is not only enabling the analysis of new data sources amongst a growing legion of enterprise users; it is changing the economics of data. Alongside this momentum is a budding ecosystem of Hadoop-related solutions, from open source projects like Spark, Hive and Drill, to commercial products offered on-premises and in the cloud. These new technologies are solving real-world big data challenges today.
Whether your organization is currently considering Hadoop and Hadoop-related solutions or already using them in production, Hadoop Day is your opportunity to connect with the experts in New York City and expand your knowledge base. This unique event has all the bases covered:
For more than a decade “data” has been at or near the top of the enterprise agenda. A robust ecosystem system has emerged around all aspects of data—collection, management, storage, exploitation and disposition. And yet, more than 66% of Global 2000 senior executives are dissatisfied with their data investments/capabilities. This is not a technology problem. This is not a technique problem. This is a people problem. Futurist Thornton May, in a highly interactive session, shares research results of his multi-institution examination of the human side of the data revolution.Thornton A May, CEO, FutureScapes Advisors, Inc.
Hadoop is here to stay, but so are a host of other approaches. To be effective, they must all work together in the enterprise.
During the last 10 years, Apache Hadoop has proven to be a popular platform among developers who require a technology that can power large, complex applications. For customers, partners, and application ISVs who write on top of Hadoop, there is still one huge issue that remains—interoperability. Steve Jones and John Mertic take a closer look at how Apache Hadoop can become more interoperable to accelerate Big Data implementations.John Mertic, Director, ODPi
SQL has been with us for more than 40 years and Hadoop, about 10. Even though when Hadoop was born there was no SQL interface to it, it has become imperative that SQL on Hadoop solutions are brought to the market. This talk provides an overview of SQL on Hadoop, including low latency SQL on Hadoop for analytic workloads, and how SQL engines are innovatingSumit Pal, Big Data and Data Science Architect, Independent Consultant
Open source platforms and frameworks such as Apache Spark have paved the way for commodity-priced processing on a massive scale.
The journey of leveraging IoT starts by learning how sensors/robotic brains and complex micro controller networks can push data to a private/public cloud or to a centralized compute platform. Learn how to build a recipe from scratch using an electronic sensor, an open source micro controller circuit, C programming, a few wires, and cloud programming.Abhik Roy, Database Engineer, Experian
Real-time utilization of streaming data requires a modern architecture that can scale. Learn about the technologies that can help.
This presentation covers how to build a multiple location, event-driven architecture that uses streaming data to interconnect Docker-hosted microservices that allow implementation of scalable, redundant, and highly available services across multiple data centers. Using Docker containers and single-purpose microservices, this presentation demonstrates how these services are interconnected with event-driven streams and how this architecture can be deployedPaul Curtis, Senior Field Enablement Engineer, MapR Technologies
The concept of an enterprise data lake is enticing. Find what’s needed and the technologies available to help build a data lake for the enterprise.
An enterprise data lake typically requires substantial effort to ingest, process store, secure, and manage data from a variety of sources. Cask Data Application Platform (CDAP) is an open source solution, which offers a self-service user interface for creating data lakes and simplifies the building and managing of production data pipelines on Spark, Spark Streaming, MapReduce and Tigon. This talk discusses how to achieve broad, self-service access to Hadoop while maintaining the controls and monitors necessary within the enterprise.Jonathan Gray, CEO & Founder, Cask
A recent Unisphere Research survey on data management found that Apache Hadoop is gaining significant traction. About 40% of respondents now have a Hadoop installation.
Think Hadoop is not in your future? According to a recent survey, 97% of organizations working with Hadoop anticipate onboarding analytics and BI workloads to Hadoop. When this happens, the companies which have disregarded the Big Data opportunity may be left behind. The good news is that onboarding your business intelligence workloads to Hadoop is not as complicated as it used to be. If you understand some key concepts, the transition can be simpler and more successful—allowing you to recycle current skill sets while avoiding either a rip-and-replace of your technical stack or elimination of business analysts to hire data scientists.Josh Klahr, VP, AtScale