Olivier Van Steenlandt

In traditional software development, Unit Testing is pretty well known. But what about Unit Testing for database solutions? Or more specifically, what about Unit testing for Data Warehouse solutions? In this session, we will dive into the world of Unit Testing for databases, with a focus on Data Warehousing solutions. Before we dive into practical examples, we need to understand the key concepts and the importance of Unit Testing. As soon as the basics are set, we will talk about different possibilities/frameworks to implement Unit Testing. Near the right before the halfway mark, we will dive into the tSQLt Framework specifically and we will start to get more practical. What are we going to discover? * How to create 1 or more Test Classes? * How to create different Unit tests? * What's possible with tSQLt? * How to automate your Unit tests? If you are wondering how to get started with Unit Testing for Data Warehouse solutions or databases in general this session is for you!

You have implemented Database Projects and Azure DevOps for database development successfully in your organization and you want to make your life easier by automating your database deployments? If the answer is yes, this session is for you!

Are you wondering how to get started with Apache Spark? Are you currently working with SQL Server or Azure SQL (MI)? Or, are you a Data Warehouse developer? Apache Spark gained popularity in the past few years and became the new cool kid on the block. But what is Apache Spark and how can we leverage its capabilities? Which skills do we need? During this session, I will show you how we can use Apache Spark starting from our existing SQL skillset. In this session, we will start with a brief introduction to Apache Spark and we will learn how we can use our SQL knowledge within Apache Spark. After a short introduction, we will be focussing on practical examples and we will be comparing how we will solve challenges in SQL, and what the alternatives are in Apache Spark. Throughout the session, we will make the examples more elaborate and complex as we go. You will learn how you can apply your SQL knowledge in Apache Spark. By the end of this session, you will have a solid understanding of how you can use your SQL skills to solve challenges with Apache Spark.

You have implemented Database Projects and Azure DevOps for database development successfully in your organization and you want to make your life easier by automating your database deployments? If the answer is yes, this session is for you!

Are you wondering how to get started with Apache Spark? Are you currently working with SQL Server or Azure SQL (MI)? Or, are you a Data Warehouse developer? Apache Spark gained popularity in the past few years and became the new cool kid on the block. But what is Apache Spark and how can we...

You have implemented Database Projects and Azure DevOps for database development successfully in your organization and you want to make your life easier by automating your database deployments? If the answer is yes, this session is for you!

You have implemented Database Projects and Azure DevOps for database development successfully in your organization and you want to make your life easier by automating your database deployments? If the answer is yes, this session is for you!

You have implemented Database Projects and Azure DevOps for database development successfully in your organization and you want to make your life easier by automating your database deployments? If the answer is yes, this session is for you! At the beginning of this session, we will do a quick recap of Database Projects and Azure DevOps. Just to make sure that we are all on the same page. During the session, we will set up an example build and deploy pipeline. Since there are a couple of ways to deploy changes from Azure DevOps to your target database, we will discuss these cases and I will be showing you which method I'm using. - 1 Release Pipeline to rule them all - multi-step deployment During this session, we will also cover the deployment pipeline differences between SQL Server and AzureSQL. At the end of the session, you should have a starting position to be able to start your database deployment automation.

Having source control in place sounds like the default these days. Although, a lot of companies didn't implement source control yet. This session will guide you how to setup source control step by step, using Database Projects and Azure DevOps. The session is devided in 3 courses: appetizer / starter , main course and finally dessert. At the beginning of the session, we will start with the appetizer/starter: a couple of use cases will be explained why it is important to use source control within your organization (use cases out of personal experiences how things went terribly wrong / took a lot of time because of the lack of source control). Some examples: deployments take a lot of time, change of priorities, urgent bugfixing, fast growing team perhaps... As soon as main course is served, multiple ways how to setup source control will be explained step by step in multiple demo's. Do you want to have everything in source control at once or would you like a phased approach, everything is possible if you know how to start. Principles of Git will be explained shortly. The demo's will demonstrate the ease of use of Database Projects at first and the integration with source control in a second phase. For dessert, a couple of topics will be covered how to move forward after a successful implementation of database projects and Azure DevOps since this is only the beginning. What about peer reviewing, testing or Automatic deployments? At the end of the session you should have a starting position to be able to start your adventure and where you might get to in the end.