Athens BigData Meetup - Sept 17

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Landoop presentation in the Athens Big Data meetup, about streaming technologies on Apache Kafka. Introduction to the Lenses SQL engine and the Lenses platform and our open-source projects.

Engineering

Landoop
www.landoop.com
Antonios Chalkiopoulos
Athens Big Data Meetup
19 / 09 / 2017

$ whoami
@chalkiopoulos
Big Data Architect in Media,
Betting, Retail and
Investment Banks in London
Books Author & Reviewer 
Programming MapReduce
with Scalding
Founder of Landoop

Hadoop 
Integration
Kafka Connectors 
with SQL support
Kafka  
Web Tools
Docker  
containers
Kafka Monitoring
Lenses  
SQL Engine
2016 2017
Lenses  
Platform
LANDOOP

partners with the most popular Hadoop vendor
The full stack in few clicks

20+ Open Source Connectors  
with SQL support

$ docker run --rm --net=host landoop/fast-data-dev

LensesZoom into your data streams
for Apache Kafka

empower the data teams 
securely access data in motion
Discover & Analyse Implement Deploy & Operate
on perm on cloudon laptop
SQL Streams ConnectorsData Browsing
PlatformSources Sinks
Manage Applications Simplify IngestionDiscover Data Deploy Topologies
Admin & Monitoring Kubernetes / Yarn / and more
Lenses SQL Engine

Apache Kafka™
made easy
http://landoop.com
Register Now for early access
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PayPal currently processes tens of billions of signals per day from different sources in batch and streaming mode. The data processing platform is the one powering these different analytical needs and use cases, not just at PayPal but our adjacencies like Venmo, Hyperwallet and iZettle. End users of this platform demand access to data insights with as much flexibility as possible to explore it with low processing latency. One such use case is where our Switchboard(data de-multiplexer) platform where we process approximately 20 billion events daily and provide data to different teams and platforms with-in PayPal and also to platform outside PayPal for more insights. When we started building this platform Kafka was just another asynchronous message processing platform for us but we have seen it evolving to a place where its adds value not just in terms of event processing but also for platform resiliency and scalability. Takeaway for the audience: Most people work with and have knowledge about data. With this talk I want to present information which is relevant and meaningful to the audience. Information and examples which will make it easier for attendees to understand our complex system and hopefully have some practical takeaways to use Kafka for similar problems on their hand.

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