Name: Apache Spark, Spark + Cassandra, Cassandra 2.2/3.0
Start: 2015-07-22T18:15:00+01:00
End: 2015-07-22T21:15:00+01:00
Location: BlackRock, 12 Throgmorton Avenue

PLEASE MAKE SURE YOU RSVP WITH NAME AND LAST NAME AS IT WILL BE A MANDATORY REQUIREMENT TO GET INTO THE BUILDING!

This month we will change things up a bit as we have a guest speaker who will be talking Spark. See you soon!

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Schedule:

18:15 - Doors Open (Pizza will be served at the break only)

18:45 – Creating data warehouse solutions using Apache Spark - Brendon Smith & Mayur Ladwa

19:30 – The C* Spark connector under the covers - Chris Batey

19:45 - Break (With Pizza and Drinks)

20:00 - What's new in Cassandra: 2.2 and a look at potential 3.0 features - Chris Batey

20:45 – Networking

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Speakers:
Brendon Smith & Mayur Ladwa

Brendon and Mayur are software developers in BlackRock. Brendon manages the investment risk team and is also responsible for delivering various fixed-income tools in BlackRock. Mayur works in the core client processing team dealing with tools around the client investment lifecycle. Both of them are passionate about big data technologies and spend a lot of their own time learning new ways of solving data-related challenges.

Title:
Creating data warehouse solutions using Apache Spark

Abstract:
Historically, majority of companies have been looking at various data warehouse solutions, possibly with structured data in a star schema environment. Given the increasing amount of data and performance benefits of newer technologies, in this talk, Brendon and Mayur will go through how we use technologies such as Spark and a HDFS compliant file system with columnar storage for a multi-data centre architecture that allows employees firm wide to run analytical queries.

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Speaker:
Christopher Batey
Technical Evangelist, DataStax

Christopher Batey (@chbatey) is a Software Engineer by trade and is currently employed by DataStax as a Technical Evangelist for Apache Cassandra. Chris has also worked for Sky, where he helped build their online television platform, and IBM, where he helped develop a variety of messaging products. He spends a lot of his own time contributing back to the software community. He's founder of an open source test double for Apache Cassandra: Stubbed Cassandra, helps with the running of the London Java Community and blogs regularly at: http://christopher-batey.blogspot.co.uk/.

Talk 1:
The C* Spark connector under the covers

Abstract:
We've seen a lot of hype about integrating C* and Spark, this will be a 10 minute deep dive into how Spark partitions are built from C* data and how the connector writes data into C* (and how you can throttle it). It is assumed you already understand Spark and C*.

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Talk 2:
What's new in Cassandra: 2.2 and a look at potential 3.0 features

Abstract:
Cassandra 2.2-rc2 has just been released which means that 2.2 is just around the corner. Let's have a look at the new features and how they'll affect the way you model data in C*. We'll go through:

- JSON support

- User defined functions

- User defined aggregates

- Role based authentication

Then we'll step into the future and take a look at 3.0 with Materialised Views being the biggy. However be warned: all 3.0 examples are off trunk and the exact syntax/feature may change come release time!

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In addition to the talks there will be, as usual, plenty of opportunity to meet other Cassandra users and enjoy robust discussions around NoSQL.

Special thanks to BlackRock (http://www.blackrock.com/) who are hosting us for this event!

Big thanks to long term supporter DataStax (http://www.datastax.com/). DataStax are one of the largest supporters of the Cassandra project and offer comprehensive support as well as their own distribution of Cassandra, Hadoop and Solr (DataStax Enterprise).

NOTE: We are unable to cater for any attendees under the age of 18. Please do not sign up for this event if you are under 18.

Ale Murray

Cassandra London

Technology

Cloud Computing

Internet Professionals

Web Technology

New Technology

Cassandra

NoSQL

Open Source

High Scalability Computing

Big Data