Ben Jarvis
CTO at Adatis
London, United Kingdom
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My IT career has spanned infrastructure, application development, database administration and data engineering. During the past 4 years, working at Adatis, I have worked across the full Azure stack architecting and developing Azure solutions for clients in various industries ranging from construction, to music, to the public sector; all with a range of requirements and interesting technical challenges.
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Combining web apps and AI - how to unlock the power of the Azure OpenAI Service
ChatGPT and OpenAI are two big buzzwords in the industry at the moment with lots of attention and promotion from Microsoft and others. We’ve all seen examples online of ChatGPT being used to write code and automate everyday tasks but how do we make use of it in a real-world application to increase the productivity of users within our business?
In this session we will take an existing web application, look at areas where OpenAI could improve/augment the user experience and then implement an integration with the Azure OpenAI service to show how this new tool can quickly and easily be used to improve user productivity.
This session will be demo heavy and no previous knowledge of OpenAI is needed to attend.
Patterns for Master Data Management in Azure
Do your source systems contain duplicate data? Are the business begging you to merge together multiple, disparate sources to provide a single view of entities such as products and customers? Are you struggling to meet these requirements using PaaS services in Azure? If so, then this session is for you.
In this session we will explore master data management and the benefits it can bring to your organisation. We'll explore what master data management is, understand the different types / levels of maturity that can be adopted and then dive into a real-world example to understand how we can utilise PaaS services in Azure to build a matching engine that can match and master large amounts of data in a scalable and future-proof manner. Finally, we'll explore how you can serve mastered data to other applications within the organisation and provide business users with a method to amend and enrich mastered data.
This session is based on learnings from real-world master data management projects and will explore how we can adopt an iterative approach to master data management, using Azure Databricks with ready-built open source implementations of matching algorithms to enrich our matching engine and ensure we get the highest possible match rate.
No specific prior knowledge is required to attend this session however, some understanding of Azure services such as Databricks is useful.
Azure Container Apps for Data Engineers
Are you a data engineer that is utilising Azure services to gain meaningful insight from your data? Do you have a requirement to ingest data from sources in real-time or serve data from your Azure data platform via APIs? Do you want to meet these requirements using the simplest and lowest cost solution possible? If so, then this session is for you.
In this session we will explore Azure Container Apps, which is the latest service to become available in Azure, and understand how the service can benefit you as a data engineer working in Azure.
Azure Container Apps provides the ability to run container based applications in a serverless environment where you only pay for the time the container is online. This makes it ideal for many data related use cases and provides a low-cost solution for running applications in Azure. We will explore the Azure Container Apps service and show how easy it is to implement two typical data platform use cases - real-time ingestion and application integration. The end-to-end implementation process will be demoed ranging from creating the application in Visual Studio through to deploying it in Azure and testing it.
This session assumes you have some basic understanding of Azure and data platforms in general however, the key building blocks that make up the Azure Container Apps service such as containers, Kubernetes, Dapr and serverless processing will be explained.
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