Near-Realtime ingestion and analytics of large quantities of sensor data

Olivier van Goethem – Cloudreach

Multitenant PostgreSQL

Stepan Rutz - Scopevisio AG

You use (managed) PostgreSQL on Amazon RDS and (plan to) have a lot of customers … A look at the possible approaches on how to store your customers and how that affects your ability to selectively try out new features, do Point-In-Time recovery, physical replication, logical backups, connection-pooling, red-green-deployments and how your query-performance will suffer.

The 7 quests of resilient software design

Uwe Friedrichsen - Codecentric

Resilient software design is around for some years meanwhile. Still, there seems to be a mismatch between popularity and progress - while patterns and libraries are quite well known for a while, only little progress in terms of creating better robust applications seems to be made.
In this session we will examine the probably biggest obstacles and pitfalls on our way towards creating resilient systems. You will learn what they are, how you identify them and what you can do about it. After all, it is still a long way to become a resilient software designer ...

Observability foundations in dynamically evolving architectures

Boyan Dimitrov - Sixt

Automate all the things

Govinda Fichtner - Paessler

Cloudformation gives you Super-Powers when it comes to automating AWS.
This talk is about our journey of using Cloudformation as the backbone for our SaaS product PRTG in the Cloud. Find out how we automatically manage the complete lifecycle of individual customer server instances with no human intervention. Join us and profit from our hard-won experiences on this journey, learn what kind of problems we encountered and how we overcame them.

From 0 to Elixir at Onfido

Paulo Pereira - Onfido

More security with serverless

Ekkard Schnedermann - elephantshop

The Hashicorp toolstack in a global IoT platform, lessons learned.

Gerrit Tamboer - mindcurv

Building and maintaining an AWS based, globally deployed IoT platform was surprisingly easy by using the Hashicorp toolstack.
This is how we did it, what we did wrong and how we matured.” (AWS, Terraform, Docker, Consul, Vault, etc)

Continuous Delivery with Containers

Tobias Schwab - PhraseApp

Running a monolithic Ruby on Rails application with Kubernetes on AWS. Covering topics like Autoscaling Kubernetes Cluster, application deploymend methods from QA to production, logging and monitoring.

Managed Container Orchestration with Amazon ECS at Scout24

Philipp Garbe - Scout24

AWS offers with ECS a managed container service that states to schedule containers based on resource needs, isolation policies and availability requirements. Sounds good, but is it really that easy?
In this talk, Philipp gives insights how Scout24 uses ECS to improve their developer experience. Learn, what needs to be done to make ECS production ready, which tweaks are useful and how changes can be delivered continuously. Let yourself be surprised by a a better solution to scale your cluster effectively and save some money. Also, find out, how CloudFormation can be leveraged to free teams from the burden of running their own cluster while keeping a "You build it, you run it" culture.

Maintaining an OS library of production-ready CloudFormation templates

Michael Wittig - Author of Amazon Web Services in Action

CloudFormation is the standard to provision AWS resources. But developing a template is a lot of work. Let’s speed up development and maintenance by working together on high-quality templates: Reviewed by certified experts, secure, highly available, scalable, easy to deploy and update, built-in monitoring and logging; As a maintainer of https://github.com/widdix/aws-cf-templates I will
share what I learned by answering the following questions: What is a
production-ready template? How to reuse templates? How to modularize
templates? How to keep stacks updated? How to assure that templates are
working? How to create a sustainable library?

Automating Kubernetes Cluster Operations with Operators

Timo Derstappen

At Giant Swarm, we manage Kubernetes clusters for customers 24/7, both on-premises and in the cloud. That means we do not just set something up and hand it over, but we actually take care that it’s operational and up-to-date at all times.
In this talk Timo explains how Giant Swarm are using Operators to codify all operational tasks of managing Kubernetes cluster and distributed applications on top. The operators manage PKI infrastructures, networks, VMs and storage both on-premises and in the cloud. There have been a lots of challenges and learnings in the past year and Timo would like to share them with you.

Large Scale Kubernetes on AWS at Europe's Leading Online Fashion Platform

Henning Jacobs - Zalando

Bootstrapping a Kubernetes cluster is easy, rolling it out to nearly 200 engineering teams and operating it at scale is a challenge.
In this talk, we are presenting our approach to Kubernetes provisioning on AWS, operations and developer experience for our growing Zalando Technology department. We will highlight in the context of Kubernetes: AWS service integrations, our IAM/OAuth infrastructure, cluster autoscaling, continuous delivery and general developer experience. The talk will cover our most important learnings and we will openly share failure stories.

From 0 to DevOps in 80 Days: Lessons Learned Moving from On-Prem to Cloud Native

Franz Karlsberger, Klaus Enzenhofer - Dynatrace

"Innovate or die” may sound extreme, but it’s the only way to thrive in today’s ever competitive market. The Dynatrace team wanted to ensure that the company was relevant 5 years from now so they formed an internal incubator with one goal: transform Dynatrace into a Cloud Native DevOps organization. The incubator focused on what the company needed to do in order to integrate nascent cloud technologies so that they wouldn’t be left in the dust when the inevitable tipping point to cloud arrives. Transforming into a cloud native company would allow for rapid release cycles and provide an embedded feedback loop.
The Results: Dynatrace now has a 99.998% availability of SaaS Service and can deploy changes within an hour if necessary. In parallel, a new SaaS and managed offering is released every 2 weeks with 170 production updates per day.

Terraform, Ansible, or pure CloudFormation?

Vladimir Dobriakov

OK, you are convinced, that infrastructure-as-code (managing computing resources, networks, configuring services through machine-readable definition files, rather than interactive configuration tools) is the way to go!
But do not know, which technology to use for it? Should I learn Terraform, do I need to buy Atlas, is Ansible really enough to implement everything? Will my code stay maintainable in 3 or 5 years?
We will have a look at a small, but real world task, consider implementations with all the mentioned tools and we will also discuss, which tools give you advantages in which specific situations

Develop Alexa Skills for Amazon Echo with PHP

Ralf Eggert - Travello

Alexa and the Amazon Echo is one of the biggest players in the new area for digital language assistents. Although there is no official support to build Alexa Skills with PHP, in early summer 2017 the most used German Skill was based on a PHP application. In this talk you will learn from the author of this skill how to build Alexa Skills with PHP based on an open-source library. The talk will present what you need to consider when building your Alexa Skill with your own HTTPS endpoint server instead of an AWS Lambda function.

Enhance you applications with Amazon AI services

Christian Petters - AWS

Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and machine learning (ML) technologies within the reach of every developer. In this session, you will be introduced to several Amazon AI services and learn on a number of examples how those Deep Learning services can be utilised in your application with a few simple API calls.

“Den Hund hasst die Katze!” Herausforderungen bei der Entwicklung deutschsprachiger Skills für Amazon Alexa.

Tim Kahle - 169 Labs GmbH

Entwickler deutscher Skills für Alexa haben es bei der Ausarbeitung von Sprachbenutzeroberflächen nicht leicht. Die Auswahl an Werkzeugen zur Umsetzung deutschsprachiger Anwendungen für Amazon Alexa stellt die Programmierer täglich vor neue Herausforderungen, die gelegentlich viel Kreativität und Fleißarbeit abverlangen. Nicht nur die systemseitig angebotenen Platzhalter, sondern auch die grammatikalischen Besonderheiten der deutschen Sprache ermöglichen Raum für Spekulation: Fällt Alexa die maschinelle Verarbeitung natürlicher Sprache im Englischen leichter? Englisch verzeiht eben mehr und ist an wesentlichen Stellen maschinenfreundlicher aufgebaut als die deutsche Sprache.

Getting started fast: Development infrastructure, CI, and test deployment to a Kubernetes cluster on AWS

When a new software development project is to start, infrastructure is urgently needed. This is even more important when developing a modern system based on microservices. We present, as a viable approach, hosting both development infrastructure (source control, issue tracking, documentation, continuous integration, continuous deployment pipeline) and also a test-bed for the new software itself on a Kubernetes cluster deployed to AWS. All this can be set up in a few hours or even less. We describe the software stack used and some lessons learned.

How ERGO setup an enterprise ready multi cloud setup with Kubernetes in AWS

Learn on a real-world example how the ERGO Digital setup a enterprise ready cloud environment. ERGO used a multi cloud setup based on Kubernetes deployed on AWS. Which challenges had been met to create a cloud setup which fulfill the enterprise requirements, like:

• Fulfill compliance rules
• Integration in Incident management processes with monitoring
• Logging
• Security checks
• Automated CI/CD Pipeline

and how bootstrap such a common cloud environment for multiple project with an infrastructure as Code approach.

AWS + K8s = ❤?

This is 30 minute talk about how Contentful uses AWS services and integrates them with non-AWS services, specifically Kubernetes. The talk will cover 4 main areas: 

1. Overview of Contentful and Contentful Infrastructure set up (specifically covering AWS)
2. The decision to use Kubernetes
3. Migration (from old architecture) and Integration between AWS and Kubernetes
4. Lessons learned and top tips

How we process billions of metrics to make your microservices run smoothly

All applications are evolving into microservice applications, old conventions on how or with which language these services are developed, or where these apps are deployed no longer exist. While this new age comes with an efficiency boost, operating those IT system becomes even harder. Netflix stated: "humans cannot monitor out systems anymore". At Instana we build a monitoring solution designed to help you with such systems. We perform realtime processing of large amounts of metric, event and trace data. In this talk, I will be giving a technical peek behind the scenes on how we collect, process and present monitoring data efficiently through our reactive "not so micro" stream processing engine.

Building a Stream Analytics Pipeline without deep Knowledge of Stream Analytics

Capturing, processing and analyzing streaming data usually takes specialized knowledge about programming and certain frameworks like Apache Kafka, Storm or Spark. For users unwilling or unable to use these technologies, Amazon offers different services, summarized under the name Kinesis. These services allow data ingestion (Kinesis Streams, Kinesis Firehose) and analytics using an SQL-like syntax (Kinesis Analytics) of streaming data.

Similar services are also offered by two of the other big cloud providers, Microsoft Azure and Google Cloud Platform. While Microsoft Azure Event Hubs and Stream Analytics are specifically designed for these use cases, using Google Pub/Sub, Dataflow and BigQuery can be seen more as a workaround.

To show the differences and similarities of these solutions, a small sample scenario is run and the software is shown in a live demo. Focus will be data upload and creation of SQL queries, also some glimpses into storage and visualization will be given.

Amazon Web Services Lambda promises to make building highly-scalable applications easier, cheaper, and faster. While many teams have been successful in using serverless in different types of production systems, it’s still less well understood how to understand the performance of serverless functions—from what metrics to gather to how to understand the relationship of serverless components to other parts of the stack.