DBA Fundamentals Group

Do you need to move your legacy application to the cloud? Or maybe you need to select a database for a new cloud project. This session will help you by comparing the functionalities of Azure SQL and Azure Cosmos DB in detail.

We will start the session by explaining the differences between distributed databases and centralized databases. We will compare indexing, stored procedures, partitioning, constraints, and scaling in these two great products. Next, we will explore the available tools and languages to query and maintain the Azure Cosmos DB. Finally, we will compare the query optimization engine and execution plans between the two database engines.

This session will help you to understand and make the right decision for your upcoming cloud projects.
This session brings SQL Server and Azure Cosmos DB side-by-side and compares the two products in detail.

Business intelligence is more than fancy reports. It starts with data scattered across many operational systems. Thus, enterprise reporting requires integrating data from those separated data sources to provide information about the organization.

But with ever-increasing data volume, we also experience ever-increasing data shape. While integrating a few hundred source entities was sufficient in many past projects, many projects today have to tackle thousands of source entities, often based on semi-structured JSON or XML data. Or, there is a lack of those who can integrate data: the number of data scientists is growing linearly at best while data shape and volume grow exponentially.

We genuinely believe humans cannot process all the data their peers and machines created. We must tackle as much data processing as possible using scalable artificial intelligence.

Flow.BI is artificial intelligence that automatically integrates independent data sets into an integrated enterprise data model that can be used to define the enterprise data platform or data products in a data mesh.

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This session will guide you through the process of constructing a YAML
pipeline within Azure DevOps, tailored for the Azure Data Factory, to facilitate continuous integration and deployment. Initially, the pipeline conducts a validation check on the ARM template before proceeding to generate a build artifact. This artifact is subsequently utilized by the release pipeline to implement the required modifications in the designated environment. The procedure encompasses not only the cessation and reactivation of ADF triggers but also the allocation of parameter values specific to the environment. Concluding with the examination and approval of the code modifications, this method ensures a streamlined deployment process.

Creating a YAML pipeline in Azure DevOps for Azure Data Factory involves several key steps to ensure a smooth CI/CD process. The pipeline begins by validating the ARM template to prevent deployment issues. Once validated, it generates a build artifact that the release pipeline uses to apply changes to the target environments. This process includes stopping and starting ADF triggers and overwriting environment-specific parameters. Code changes can be reviewed and approved, ensuring that only verified updates are deployed, thus maintaining the integrity and reliability of the deployment workflow.
Participants will then receive the CI/CD pipeline and the templates with the relevant documentation so that they can considerably simplify and standardize the deployment process in their own organization.