In October, we will host an evening with two talks:
The perfect test for a realtime data architecture, is how to cope with time series data from millions of devices at a heartbeat rate. In this talk you’ll see how to combine streaming services to build near-realtime insights pipelines for smart devices and telemetry data at scale. We’ll allow machine learning and analytics on data at scale without provisioning a single server using AWS serverless services. You’ll see how to use Amazon Kinesis and Apache Flink to extend the data to Data Lakes on Amazon S3 for machine learning applications and Amazon Timestream at the same time for realtime monitoring with Grafana.
John Mousa has more than 16 years of extensive experience in wide range of solutions. He worked extensively with enterprise middle-wares, scalable and high velocity micro-services back-ends, and big data and analytics. He has engaged and worked with with multiple organizations from startups and non governmental organizations to leaders in digital native and enterprise services. When he’s not enabling customers on workloads, he utilizes his experience in process improvement, drive digital transformation strategies and data driven culture. He currently enables Digital Native Businesses in addition to enterprise, highly regulated customers, and others, world wide as a Sr. Solutions Architect at AWS based in Germany. Outside of work, he loves to spend time with his family and play video games.
Nearly every application out there is using a database. I am sure you also use MySQL, PostgreSQL, Kafka, Cassandra, MongoDB, or any other storage system in your app. And maybe you even run it on the cloud (or any container or application scheduler like Kubernetes), where you have endless flexibility and scalability options. In such cases, we talk about distributed systems. The truth is that distributed systems are hard, and Networks are not reliable. They (and your system) will fail, eventually.
Knowing the constraints of a distributed system before it fails will help you to ask the important questions and take actions to be prepared for a failure. The CAP theorem describes these constraints. In this talk, we will provide an easily understandable overview of the CAP theorem and look at how this can be applied to modern databases.
Andy Grunwals is a Software Engineer and Engineering Manager passionate about Backend, Infrastructure, Reliability, and Engineering Culture. In his professional life, he works for Aiven, a Database as a Service provider, as an Engineering Manager, leading a team of Site Reliability Engineers. He enjoys running side projects in his spare time to learn new things. Nowadays, Andy is talking about Software Engineering in the Engineering Kiosk Podcast, organizing the Web Engineering Meetup Düsseldorf, and builds sourcectl, a platform to analyze and improve your open source and inner source environment.
Attendance is free. WiFi, beer, and non-alcoholic drinks will be provided (for free as well!), and we would love to see you 🙂
• Language of the event: English
• WiFi: Available
• Costs: Free
• Drinks: Available
From Düsseldorf HBF: Tram 707 (direction “Medienhafen) to “Speditionstraße”, then a two min. walk.
When you arrive at the location use the stairs next to the well. We’re on the ground floor of the building on the right.
We don’t have parking spots available. There are some car parks near by at the cinema (paid only).
You will find parking spaces in the surrounding streets and car parks.