Past Meetup

A night of Cassandra and Spark at ING

This Meetup is past

83 people went

ING - Amsterdamse Poort

Bijlmerplein 888 · 1102 MG Amsterdam ZO

How to find us

http://www.ing.com/web/file?uuid=49188d65-2345-4823-af51-7a0b9d0a157b&owner=2abed2fc-485d-445b-b783-13ee02cac77d

Location image of event venue

Details

ING is really excited to have DuyHai Doan over with us for the next MeetUp. We will focus specifically on Cassandra and Spark using DSE.

Agenda:

18:00 – 19:00: Reception(with food/beverages)

19:00 – 20:15: Fast Track to getting started with DSE Max (Cassandra and Spark) - Datastax – DuyHai Doan

20:15 – 20:30: Coffee break / Cigarette break

20:30 – 21:15: Streaming analytics with Spark and Cassandra - ING – Natalino Busa

21:15 – 22:00: Drinks

DuyHai Doan - Datastax

Title: Fast Track to getting started with DSE Max (Cassandra and Spark)

BIO:

DuyHai Doan is a Cassandra technical advocate. He spends his time between technical presentations/meetups on Cassandra, coding on open source projects to support the community and helping all companies using Cassandra to make their project successful. Previously he was working as a freelance Java/Cassandra consultant.

Synopsis:

Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark and Cassandra and demonstrate some usages with live code demo.

Natalino Busa - ING

Title: Streaming analytics with Spark and Cassandra

BIO: Natalino leads the definition, design and implementation of big/fast data solutions for data-driven applications, such as personalized marketing and predictive analytics.

All-round Software Architect, Data Technologist, Innovator, with 15+ years experience in research, development and management of distributed architectures and scaleable services and applications.

Synopsis:
Cassandra is a good candidate to store time series. By adding Spark to the picture, it is possible to create real-time, continuous data transformations on the incoming data events. In this talk, we will look at how to setup a streaming, real-time machine learning pipeline by combining spark streaming with events. Spark and Cassandra provide a good alternative to traditional big data architectures based on Hadoop, by bringing the operational and the analytic world closer to each other, as different elements of a distributed in-memory solution.

Transportation Details:

Full details regarding the location: ING - Route description (http://www.ing.com/web/file?uuid=49188d65-2345-4823-af51-7a0b9d0a157b&owner=2abed2fc-485d-445b-b783-13ee02cac77d)

Please let us know if you require a parking space and we will try our best to arrange a parking space. ING staff will need to arrange their own parking space.