Simply Business' Data Platform, by Dani Solà Lagares

The end of summer holidays is near: kids are going back to school, some people will start going to the gym, and Barcelona Spark Meetup is coming back with a bunch of amazing events for the season!

This time we're flattered having Dani Solà Lagares with us. Based in London, Dani is Head of Data Engineering at Simply Business, an insurance provider for small companies. They have a top-notch data & streaming architecture, and he will share their experiences coping with Hadoop/Spark/Kafka ecosystem.

We're also glad to have Trovit Search as host, they have renewed their space and want to share it with the community! Thanks for the venue, beers & pizzas!

So see you Thursday 6th of September, 19:00 @ Trovit Search offices.

Abstract:

Simply Business is the leading UK small business insurance provider, and now we’re expanding to the US.

Since 2014 we've invested heavily in our data capabilities and platform, putting data at the center of the company to drive our marketing, operations, experimentation and overall decision-making. That has allowed us to scale, become fully data-driven and even win a number of awards over the years.

Among many other changes, we’ve migrated jobs from Scalding to Spark Streaming, then to Spark Structured Streaming and now we’re moving to Kafka Streams. In this presentation, I'll explain how our data platform is evolving to keep delivering value and adapting to a company that changes really fast.

Bio:

I’m the Head of Data Engineering at Simply Business, where I’m helping the company deliver value through data. Our data platform is built on AWS and uses Spark extensively, both for batch and streaming workloads.

I’ve been working on data engineering and analytics systems since 2010, which has given me the chance to play with a wide range of big data tools like Hadoop, Lucene, HBase, Spark, Cassandra, MongoDB and Kafka among many others.

Outside of work, I love to travel with my wife (especially to Southeast Asia), running in Hyde Park and enjoying a few beers with friends.