Apache Ignite community update for week of April 2: Washington meetups

[Tom Diederich <to...@gridgain.com>]

Spring has sprung and hopefully the sun is shining where you are. Unfortunately for technology evangelist Akmal Chaudhri, he’ll likely encounter rain as he traverses the Washington, D.C. area this week speaking at four meetups. But then again, Akmal is from London so he’s used to wet weather. J 

 

Akmal’s first talk is tonight at an event called Big Data, Washington DC. He’ll be using Apache Ignite™ for his talk on machine learning and other data science tools. 

 

On Tuesday Akmal will make the short trip to Arlington, Virginia, for his talk that evening at the DC Spring Framework Meetup. Akmal’s talk will be a little different at this event. In fact, it won’t be a talk at all – rather a sort of working meeting. 

 

Together with Pariveda Solutions senior developer Sunny Cheng, he’ll look at whether Ignite could be useful on some of the real world applications Sunny and his team are working on at Pariveda, a technology consulting firm.

 

Akmal will also cover a few typical use cases and work through some code examples. Attendees will leave ready to fire up their own database deployments!

 

On Wednesday, Akmal will be in Dulles, Virginia – but he won’t be visiting the airport any time soon. Instead he’ll be the featured speaker at the NOVA Data Science Meetup. Machine learning will once again be the topic of discussion for this inquisitive group of 1,200 members. 

 

In this presentation, Akmal will look at some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

 

Akmal will be in the homestretch on Thursday as he rolls into Hanover, Maryland, for the Apache Spark and Distributed Computing Meetup. Akmal will deliver a talk titled, “Apache Spark and Apache Ignite: Make streaming analytics real with in-memory computing.”

 

Akmal will demonstrate:
- how to use Ignite as an in-memory database for Spark applications
- how to perform streaming analytics by deploying Spark stream pipeline
- how to process data stored in Ignite with Spark RDDs and DataFrames
- how to speed up SQL queries by leveraging the Ignite SQL engine and indexing

 

Tom