Past Meetup

'Current best practices in HTAP' & 'Getting Machine Learning into production'

This Meetup is past

36 people went

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Details

• What we'll do
Commit to in-memory: Join us May 9 for two great talks from GridGain & VoltDB!

This month's meetup will feature Akmal Chaudhri, GridGain’s technology evangelist, and David Rolfe, VoltDB's director of Solutions Engineering (EMEA).

We'll also be raffling off 2 full-access passes to the In-Memory Computing Summit Europe, June 25-26 in London! https://www.imcsummit.org/2018/eu/

You can enter the raffle now by completing the online raffle form: http://bit.ly/LondonIMC

Agenda:
* 6:15 p.m. -- Pizza, beer & networking
* 6:30 p.m. -- Talk 1: Akmal
* 7:10 p.m. -- Talk 2: David
* 7:50 p.m. -- Closing remarks; raffle drawings.

>> Talk 1: Akmal will share some of the current best practices in HTAP, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. His talk is titled: "Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Processing (HTAP)"

Summary: The 10x growth of transaction volumes, 50x growth in data volumes -- along with the drive for real-time visibility and responsiveness over the last decade -- have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional/analytical processing (HTAP).

This session will cover:
* The requirements for real-time, high volume HTAP
* Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
* A detailed comparison of Apache Ignite and GridGain® for HTAP

>> Talk 2: David's talk is titled, "The part of ML nobody is paying attention to: Getting to production."

Summary: There is a massive difference between generating the input data needed by a Machine Learning model once (to prove a concept), and doing it continuously, indefinitely, at scale, and within short time periods. Years of experience working with disparate and imperfect data sets lead us to suspect that people trying to move desktop scale ML algorithms into production are likely to massively underestimate the time and energy required to get the data needed by ML algorithms into a form where they can use it.

• What to bring
Laptops encouraged! And bring an appetite because food and beverages will be available.

• Important to know
This is a free event thanks to sponsorship from GridGain Systems