DUGTalks: Removing Big Data Obstacles with No-Cost, Open-Source Components

For anyone who is currently pursuing big data, machine learning and artificial intelligence and curious about how to generate more value and better performance from their efforts.

Date: October 24, 2018           Time: 1:00pm EST           Duration: 1 Hour

Fortune 500 companies are increasingly using open-source data solutions in the pursuit of big data, IoT, IoAT, and/or AI initiatives. This is because deep knowledge of these resources enable companies to reduce — even eliminate — license fees and the cost of data ownership by an average of 70 percent. 

Discover how to increase profits for shareholders as part of a new executive briefing series hosted by data innovation leaders at Sunset Learning Institute and DFHeinz. The first session begins Sept 12 at noon and serves to introduce companies to open-source data solutions that enable teams to pursue big data, IoT, IoAT, and/or AI. The second evening session will take place for companies interested in how to optimize faster streaming & batch analysis.

Open-source projects are freely available, easily secured and built by best-in-class developers, or, members of the Apache Software Foundation (ASF). Featured event host, Daryl Heinz, is a seasoned ASF contributor who has helped guide global companies like Netflix and NASA toward a vendor agnostic, future-proof approach to data systems. He says the demand for “component-oriented design” and software reusability methodologies comes as a direct result of the need for more computational power to manage more information coming in from more data sources.

“It’s easy to get shiny object syndrome,” Heinz said. “This is why these briefings are about getting laser-like focus. Our goal for attendees to is eliminate “big data” license fees while increasing the capacity for infinite storage and analysis.”

Attendees will discover how to optimize batch or “real-time” streaming analysis and introduce software reusability principles into the workplace. These two capabilities, according to Heinz, save the average company 70 percent or more on overall labor, storage, and analysis costs.

DUGTalks: Flink - A Serious Alternative to Spark

Learn about Apache Flink, an Apache Software Foundation open source framework that's more mature and superior to Spark for distributed stream and batch processing.

Date: October 24, 2018           Time: 6:00pm EST           Duration: 1 Hour

Apache Flink is an Apache Software Foundation open source framework, more mature and superior to Spark, for distributed stream and batch processing.

Flink features include “Streaming-first” processing.  This is not micro-batching but true event-time streaming that was built into Flink from the start.  Streams are treated as inifinite, unbounded data sets in Flink.  Batch processing is little more than a subset of Flinks capabilities as Batch data is treated as “finite streams” or bounded data sets.  Flink provides a flexible API to handle late and out-of-order data.  It provides stateful/fault tolerant streaming with exactly-once processing semantics.  Along with its programming API, Flink has powerful SQL and R interfaces for event time data analysis.

Flink performs uniformly at large scale, running on thousands of nodes.  It can run in single JVM, standalone cluster, YARN, or cloud.

This is why companies like Alibaba, Bouygues Telecom, Capital One, Comcast, Ebay, Ericsson, Huawei,King.com,Lyft,Netflix, Portugal Telecom, ResearchGate, Styx, Telefonica, Uber and Zalando all trust Flink in mission critical production.

SLI will be sponsoring Chris Ruegger, a Flink SME, in its first DUG talk (www.dugtalks.com) October 24.  Please be sure to register for this first of many DUG Talks at SLI!

neXT LIVE 365 Community Tour

Join us as we walk you through the resources and tools available in our neXT LIVE 365 Community. This will be a live, interactive session so we are happy to answer any questions you may have at this time!

Date: The last Friday of each month (October 26th is neXT)           Time: 1:00pm EST           Duration: 30 Min

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