Tuesday, September 29, 2020
Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions.
This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS.
While the (managed) Kubernetes API being a great abstraction from the actual cloud provider, a number of challenges remain including for example networking, autoscaler, cluster provisioning, or node sizing. This talk provides an overview of those challenges and also discusses how they were solved as part of the ArangoDB managed Service.
A discussion on the changes, trends, and database technologies that are going to impact your business in the next 12-18 months.
In the current technology landscape, we have a lot of great innovation happening especially when it comes to Database Technology. A few examples include introducing new data models such as time series or graph, which focus on solving SQL at hyper-scale problem, this has been an elusive solution and scale was becoming synonymous with NoSQL environments. We now have a new Cloud-Native database design coming to market using the power of Kubernetes as well as employing Serverless concepts.
In this presentation, we will look at database technologies changing trends and what is driving them as well as talk about changes to the Open Source licenses and Cloud-based deployment and emerging class of not-quite Open Source Database software.
Business leaders desire data driven insights to help improve customer experience. Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to rapidly build, scale, and share results of their projects to accelerate delivery of AI-powered intelligent applications into production. This keynote will provide a brief overview of the AI/ML use cases, required capabilities, and execution challenges. Next we will discuss the value of Hybrid Cloud powered by containers, Kubernetes, and DevOps to help fast track AI/ML projects from pilot to production, and accelerate delivery of intelligent applications. Finally, the session will share real world success stories from various industries globally.
Many of us are familiar and comfortable with deploying automation for parts of our software development lifecycles. We don’t build all of our software by hand; continuous integration practices have been available, and improving, for a number of years. Our test engineers write more automated testing components to replace manually clicking through QA environments. Our production deployments are governed by automated delivery or deployment, coupled with automated infrastructure tooling to keep our services running.
But what happens when something goes wrong? How we respond to incidents, and the speed at which our teams can do so, is increasingly important. Employing automation at this edge stage of the lifecycle will help teams deal with the increasing complexity of modern systems and recover time from unplanned work. This talk will discuss some of the features to keep in mind when automating for incident response as well as approaches to introducing automation to help reduce alert fatigue for your teams.
Wednesday, September 30, 2020
In this session, you will learn about developing robust application modernization strategies as well as tooling and accelerators that can be used to realize business value quickly and iteratively. We will start by discussing how to analyze applications and infrastructure using expert systems and automated tooling as well as evaluating the existing IT operating model and foundational capabilities to identify gaps. Then we will explore several modernization techniques using cloud, containers, and exponential technologies. Next there will be a brief overview of tooling and accelerators that illustrate key transformation patterns such as containerization and decomposition of applications into microservices. We will wrap up the discussion with some considerations for building a robust business case which is a critical success factor for launching, and continuing, your modernization journey.
Today’s monitoring systems, designed for different times, to maintain availability and performance of traditional applications and architectures. Digital business has forced monitoring of the past to transform into observability of today. Choices have progressed significantly for both small teams and large enterprises. These observability signals encompass logs, metrics, and traces which create the monitoring tooling for today’s DevOps teams.
Previously products had been in silos, which created burdens in maintaining and scaling solutions, but today this is no longer an issue with many commercial options which unify these signals. There have been significant improvements in the open source projects, but scaling them requires expertise and investment in people and infrastructure. If the investment is too great there are SaaS options available across the board from simple to complex.
The market is changing faster than ever, driven by open source initiatives and projects along with software foundations which underpin today’s cloud native architectures. In this talk you’ll learn how monitoring has shifted, which open source technologies are meeting new challenges, and how the community initiatives will change observability significantly in the next 12 months. Your organization can adopt these new technologies to save money, support new architectures, and provide new capabilities to your development, operations and DevOps teams.
Digital business requires observability and agility.
Yesterday’s siloed organizations do not work in high velocity environments, which require agile DevOps teams.
DevOps teams need many tools and technologies to handle the complete life-cycle of the application and environments.
Observability includes collecting logs, metrics, and traces from applications and infrastructure.
Open source projects have improved vastly in the last few years, but scaling them is a challenge
Markets are changing, learn which technologies meet these challenges and where the ecosystem is headed in the next year