Join us to learn how -memory computing solutions can advance your micro-services architectures and accelerate Apache Spark-powered workloads. This session is to be led by Nicolas Frankel a Developer Advocate of Hazelcast, and Denis Magda, Apache Ignite PMC member and GridGain Head of Developer Relations.

Agenda:
6pm - Socializing, Raffle Entry and, Pizza (Come early for the best pizza selection!)

6:30pm - Talk 1: Nicholas Frankel

7:10pm - Talk 2: Denis Magda

7:40pm - 8:00pm - Q&A and Raffle Winners

There will be a raffle so come early to register.

Here's a full abstract of what will be covered, followed by an interactive Q&A:

Talk 1 -Nicolas Frankel:
3 easy performance improvements in your architecture

While architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I'll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it's much easier than one would expect.

Talk 2- Denis Magda:
How to Speed Up Spark SQL With In-Memory Computing Stack

With Spark SQL based on the Catalyst optimizer, we can query and join various data sources, including Hive, relational databases, Avro, and Parquet. add data source-specific rules to push down aggregations and execution into external storage systems. Such optimizations speed up Spark SQL operations significantly by reducing data shuffling between Spark workers and an external data source.
This talk aims to explain how Apache Ignite’s in-memory store and internal SQL engine were integrated into the Catalyst optimizer to accelerate real-time analytics workloads with a highly- in-memory computing stack. We’ll start from how to gain a performance boost by merely running Spark and Ignite together. Next, we’ll dive into more sophisticated optimizations to achieve an order of magnitude increase.

How to find us:

Courier: 182 Shipley · San Francisco, CA