During this exercise, we delve into the Data Warehouse design and implementation step, crafting robust data models, and designing transformation tasks. We explore how to efficiently load, cleanse, and merge data, ultimately creating dimension and ...
Design and implementation are two pivotal phases for our data warehouse solution. In the design phase, we lay the groundwork by defining the database system, schema model, and technology stack required to support the data warehouse’s implementatio...
When it comes to writing a data pipeline using Python, there are several options to consider. Apache Spark provides a powerful distributed processing framework, Apache Airflow offers a flexible and scalable solution for workflow orchestration, Pre...
A data pipeline is a workflow of tasks that can be executed in Docker containers. The execution, scheduling, managing and monitoring of the pipeline is refer as orchestration. In order to support the operations of the pipeline and its orchestratio...
Azure provides the Azure OpenAI services which integrates the OpenAI API in Azure infrastructure. This enables us to create custom hosting resources and access the OpenAI API with a custom domain and deployment configuration. There are API client ...