Accelerating micro-services and Apache Spark analytics with in-memory computing

Bay Area In-Memory Computing Meetup
Bay Area In-Memory Computing Meetup
Public group


Location image of event venue


Join us to learn how in-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.

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 onsite 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 microservices architecture

While a microservices 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. Catalyst’s extensible design lets us add data source-specific rules to push down aggregations and filters 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-performant in-memory computing stack. We’ll start from the basics showing 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.

Speaker Bio:
Nicolas Fränkel Bio:
Developer Advocate @ Hazelcast
Nicolas Fränkel is a Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps. Currently working for Hazelcast. Also double as a teacher in universities and higher education schools, a trainer and triples as a book author.

Denis Magda bio:
Head of Developer Relations @ GridGain Systems Inc.
Denis Magda is an open-source enthusiast who started his journey in Sun Microsystems as a developer advocate and presently settled down at Apache Software Foundation in the roles of Apache Ignite committer and PMC member. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies to build successful open-source projects. You can be sure to come across Denis at conferences, workshops, and other events sharing his knowledge about the open-source, community building, distributed systems.

Thanks Courier for the venue.
Thanks GridGain Systems for the food.