Building a Fast Data Front End for Hadoop

Screencast  Building a Fast Data Front End for Hadoop.

Date: This event took place live on June 24 2015
Presented by: John Hugg
Duration: Approximately 60 minutes.
Cost: Free


Massive increases in both the volume and velocity of data have led to the development of interactive, real-time applications on fast streaming data. These fast data applications are often the front ends to Big Data (data at rest) and require integration between Fast + Big. To provide maximum value they require a data pipeline with the ability to compute real-time analytics on fast moving data, to make transactional decisions against state, and ultimately deliver data at high speeds to long-term Hadoop-based analytics stores like Cloudera, Hortonworks and MapR.

The new challenge is building applications that tap fast data and seamlessly integrate with the value contained in data stores — combining machine learning and dynamic processing. A variety of approaches are employed including Apache Storm, Spark Streaming, Samza, and in-memory database technology.

During this webcast you will learn:

  • The pros and cons of the various approaches used to create fast data applications
  • The pros and cons of Lambda and Kappa architectures compared to more traditional approaches
  • Understand the tradeoffs and advantages surrounding the resurgence of ACID and SQL
  • How integration with the Hadoop ecosystem can reduce latency and improve transactional intelligence

About John Hugg, Founding Engineer

John Hugg is a Software Developer at VoltDB. He has spent his entire career working with databases and information management. In 2008, he was lured away from a Ph.D. program by Mike Stonebraker to work on what became VoltDB. As the first engineer on the product, he liaised with a team of academics at MIT, Yale, and Brown who were building H-Store, VoltDB’s research prototype. Then he helped build the world-class engineering team at VoltDB to continue development of the open source and commercial products.



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