Name: Two talks: Streaming and Akka Persistence + Cassandra availability management
Start: 2016-07-12T18:30:00-04:00
End: 2016-07-12T21:30:00-04:00
Location: Weight Watchers

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Two exciting talks this month! We'll be discussing handling, processing and managing the availability of big data with Scala, Spark, Akka and Cassandra.

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Data in Motion: Streaming Static Data Efficiently in Akka Persistence by Martin Zapletal

Processing streaming data is becoming increasingly important in many areas. Scala and the Lightbend Reactive platform offer multiple solutions for processing streaming data, including Akka, Akka Streams and Apache Spark. This talk introduces the advantages and concepts of streaming data processing. It will mention differences between static data and data in motion and their usage as streaming data sources. The main goal of the presentation is detailed discussion of Akka Persistence Query and implementation of the stream production specification in Cassandra plugin for Akka Persistence (akka-persistence-cassandra) that the author participated in. Focus is on architecture and design considerations, implementation details, performance tuning and distributed system specifics such as correctness, efficiency, consistency, order, causality or failure scenario handling that are inherently part of the solution and apply to wide variety of distributed systems. Finally, other improvements to the Cassandra plugin for Akka Persistence project such as reusing the stream generation for non blocking asynchronous Akka Persistence recovery as well as application of the project and the discussed concepts to build modern reactive enterprise stream processing and asynchronous messaging distributed applications are presented.

About Martin:

Martin is heading up Cake Solutions technical team in the US and is Apache Spark and Cassandra plugin for Akka Persistence contributor. Martin focuses on distributed systems, parallel and distributed approaches to data processing as well as machine learning, data mining in large volumes of data, and big data in general. These fields seem to be increasingly important in the industry and Martin has been promoting Scala, functional programming, and Reactive approaches as they provide very useful tools to solve these problems.

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You've Built Your Cassandra Applications. Is the Data Always On? by Srinivas Vadlamani

The Cassandra movement has enabled companies to deploy brand new applications, many of which are critical to a company’s business operations. As these new applications fall under business SLAs, concepts such as scalable backup, recovery, and test/dev management are critical to ensure that data is "always on" to power these applications. However, there are a number of technical challenges to overcome when it comes to applying these data management processes to Cassandra applications. Some of these include:How do you ensure that your data recovery architecture is nimble enough to back up hundreds of tables in a single workflow at high performance?What sort of data management architecture is relevant in an environment where nodes are constantly being commissioned and decommissioned?Your catalog will need to store and version millions of objects from your Cassandra environment - how do you ensure that you can quickly identify the appropriate set of objects to recover when dealing with petabytes of data?

Srinivas Vadlamani is Chief Architect and co-founder of Talena. Srinivas brings deep experience in distributed systems, parallel computing, and big data architectures to his current role, having spent years as an architect and engineer at companies like Aster Data, Couchbase, and Tata Consultancy Services. He has a Ph.D in Electrical Engineering and Computer Science from the University of California at Irvine.

Mark Harrison

Distributed Data New York

Open Source Developer Network

Venues, refreshment, and speaker support.

Disney Streaming Services

Technology

Scala

Big Data

Functional Programming

Data Analytics

Data Center and Operations Automation

NoSQL

Cassandra

DevOps

Distributed Systems

Asynchronous Programming

Akka

Stream Processing

Apache Spark

Apache Kafka

Reactive Programming