Green must be convenient

The Green Software Foundation (GSF) has garnered significant attention at the Conference of Parties, particularly with the introduction of the SCI specification. This session delves into the fundamentals of Energy Efficiency, Hardware Efficiency, and Carbon Awareness. We will explore the core principles, patterns, and practices to be implemented. It's crucial to note: for sustainability to truly take root, it must also be economically viable.

Let's go Hybrid! Is it better? No. Is it cheaper? Yes.

When your solution is mature and your consumption is stable, you would probably save some money exiting the Cloud, instead paying for the PaaS we (and I, especially) suggested for a long time. And you're right. In this session we try to identify when a service becomes too expensive for a given usage pattern and how to achieve the same results with a piece of Hybrid in it.

Implement a Serverless Data Analytics strategy with Microsoft Azure

After a decade of innovation around big data, distributed filesystems, data movement tools and scalable data warehouses, I want to share my thoughts on how Microsoft Azure tries to wrap all this stuff with a few tools, like Synapse, DataLake Storage and more. As a result, we will implement a Data Analytics strategy in a serverless mode, on a consumption-based model.

Implement a Serverless Data Platform with Microsoft Azure

Azure is now a first-class suite of service even for the Data Platform stack. This session evolves the one of last year, adding new things and thoughts learned during the way. I would like to share my thoughts about building a custom Data Platform, using few tools like Synapse, DataLake Storage and something more. As a result, we will implement a Data Platform strategy in a serverless mode, on a consumption-based model.

Working with Azure Database Services Platform

Azure SQL Database has been one of the first Platform-as-a-Service offering of the Azure ecosystem in the early stages of the platform. Now is one of the most advanced managed RDBMS in the cloud, actually very different from the competition. In this session, inspired by the chapter 1 of the “Cloud Data Design, Orchestration, and Management using Microsoft Azure (Apress)” book, we try to make things together to have a big picture of the service. Assuming all the basics, we discuss Design, Connectivity, Governance, Scaling, Security and Management, plus some good tips coming from the everyday usage.

The JSON evolution with SQL Database

The old school lesson "do not store large objects into database" is now really deprecated. In this session we see how a (used-to-be-a) relation database like Azure SQL Database can handle tons of JSON objects with effectiveness. We'll see how this enables a shortcut between the raw data orignally stored into a data lake, and the relational view consumed by the analysis applications.

Scalable Azure WebJobs deployments with Azure DevOps and hierarchical YAML

Directly from the production line, I want to share with you how we manage hundreds of Azure Webjobs (microservices ) deployments with a little administrative effort, reusing a single robust pipeline across them. In this session we see how to build and deploy Azure Webjobs to Azure App Service and containerize them in minutes.

Microservices, YAML, DevOps, Templating, CI/CD

Scraping the Web using C#, Selenium, Azure Webjobs and Browserless

Sometimes we don't have a public API to call to get the structured data we want to evaluate our KPIs. So, we can scrape the web to do this, but with a cost, in terms of governance and robustness. In this session we see how to solve those issues by setting up the appropriate process and using the right tools to make the crawling process seamless and easy.

Scraping, Selenium, Background job, Headless browser

Multi-tenant Full-Text Search and Data Consolidation With Azure

For many years we used SQL and FTS to extend the features of the frontends which need a good search solution. Despite it is a valid approach, it is often unoptimal, since there are products that do this as their first purpose. One of that is Azure Search, which is fully managed. In this session we see how to consolidate data from several DBs into a search index to search into that with high performance and flexibility.

Full-text search with Azure Search and SQL Database

For many years we used SQL and FTS to extend the features of the frontends which need a good search solution. Despite it is a valid approach, it is often unoptimal, since there are products that do this as their first purpose. One of that is Azure Search, which is fully managed. In this session we see how to consolidate data from several DBs into a search index to search into that with high performance and flexibility.

From 0 to 100 containers with .NET Core and Azure

This session mixes many of the lesson learned in years while writing distributed software. First is "design for failures", to be sure our code will never generates inconsistencies. Second is "stateless (almost) everywhere", to be sure our compute power can be built in minutes. Third is "performance", which has to be considered a feature itself while it is often a competitive gap between you and others. In this session we approach the "simple" problem of reducing the computation time of a CPU intensive application, using C#, Queues, Azure Containers Instances and a bit of ARM/YAML templates.

Deploy long-running workloads on Azure: options

In this session we see which are the most common lesson learned while developing long-running jobs to be deployed in the cloud. Starting from avoiding lock-in, logging, managing the state up to scale out really fast.

Deeply App Service and the power of PaaS

Platform-as-a-Service is the key enabler for companies growing fast. With a little effort in standardization and refactoring of existing application, running into a PaaS can reduce the cost of governance and maintenance a lot. Azure App Service is one of the most advanced PaaS in the cloud’s world. In this session we are going to understand deeply the key concepts of App Service, how it can be used in production at different levels and how to create high-scale with it.

Azure management libraries for advanced code-based automation

Infrastructure people often use ARM templates to define service topology by JSON configurations. Developers often like to do that in C#, with code. In this demo-only session I will connect some of the management experience made with Azure and C# to perform repetitive tasks with a great degree of automation.

API Gateway made easy with Ocelot and Containers

We have a bunch of options today when implementing an API Gateway for our APIs. The starting point is actually DOING THAT immediately, regardless the underlying technology. In this session we see how to go in production with an implementation of the open source project "Ocelot", written in ASP.NET Core, in conjunction with the hosting facility of Azure and App Service for Containers.

100 containers do 100s work in 1s

This session mixes many of the lesson learned in years while writing distributed software. First is "design for failures", to be sure our code will never generates inconsistencies. Second is "stateless (almost) everywhere", to be sure our compute power can be built in minutes. Third is "performance", which has to be considered a feature itself while it is often a competitive gap between you and others. In this session we approach the "simple" problem of reducing the computation time of a CPU intensive application, using C#, Queues, Azure Container Instances and a bit of ARM templating.

Implement a Serverless Data Analytics strategy with Microsoft Azure

After a decade of innovation around big data, distributed filesystems, data movement tools and scalable data warehouses, I want to share my thoughts on how Microsoft Azure tries to wrap all this stuff with a few tools, like Synapse, DataLake Storage and more. As a result, we will implement a Data Analytics strategy in a serverless mode, on a consumption-based model.

Serverless, Big Data, Data Movement, Parquet, SQL

Serverless Containers with Azure, Containers Instances & Apps

In the latest years, containers become the new standard to run workloads regardless the underlying compute runtime. But, the orchestrator cost is still there, in most of the scenarios. With Azure, we can use containers without all the hassle of managing K8s, first with Azure Container Instances and now with Azure Container Apps. Come with me in this session to see how we made it run in production with those amazing tools.

Serverless, Containers, Docker, Dapr, Event-driven