watermark picture

Modern Data Platform - How to approach the architecture and setup of Azure & Power BI based platform

This training starts with understanding why we should switch from traditional data warehouses to a modern data platform. The training continues with an introduction to Azure Synapse.

More and more organizations are switching from traditional data warehouses to a modern data platform, serverless and without using databases. The traditional data warehouses lack the ability to add semi-structured and unstructured data, and SQL databases do not allow for a cost-optimized setup. And while decoupling data storage and processing is a good thing by itself, it adds significant complexity and unwanted delays because data is copied from one component to another. 

In this training 

  • you'll start with understanding why we should switch from traditional data warehouses to a modern data platform.
  • The training continues with an introduction to Azure Synapse. Hands on you will create your Azure Synapse data model/solution, and all best practices to take into account.
  • In the last part of the training, you will set up your Power BI platform and learn the best practices to load your data into Power BI.
  • The visualization of your data inside Power BI is not in scope of this training.
  • Other topics as cost-optimization, performance, incremental loading, security, documentation, governance,… will also be discussed briefly.

After this training 

  • you will learn the differences between traditional data warehouses and a modern data platform.
  • You will discover all capabilities of Azure Synapse.
  • Hands on, you will learn all best practices to load your data in Azure Synapse and Power BI.
  • You will be able to discuss your specific questions with our experienced data professionals.

This training is for 

  • Data engineers or developers that wish to develop their knowledge of Azure Synapse
  • Any data analyst working within an Microsoft environment
  • Data Team Manager that wishes to leverage new capabilities of the Microsoft stack.

Related cases

Related blogs

Why lift-and-shift isn’t copy-and-paste

Lift-and-shift is potentially a very efficient method to move your applications to the cloud. You need to be aware, though, of the implications of the pay-as-you-go pricing model that comes with a cloud deployment. Check out our 3 tips to ensure lift-and-shift delivers the most cost-effective solution.

Read More

How to query your S3 Data Lake using Athena within an AWS Glue Python shell job

AWS Glue, the serverless ETL service of AWS, supports two types of jobs: Spark and Python shell. In this article, we'll focus on Python shell jobs and explain how you can make optimal use of your S3 Data Lake using Athena within Python shell jobs.

Read More

Data Mesh - Beyond the buzz

Chances are you have recently heard a lot about data mesh, a decentralized approach to sharing, accessing, and managing analytical data. So, let's dive into a practical example to help you understand what a data mesh stands for.

Read More

Everything you really need to know about a data lakehouse

Data lakehouses are the talk of the town when it comes to data architecture. But why is that? And why is that happening right now? Let's take a refreshing dive into the history of data warehouses, data lakes, and data lakehouses.

Read More