This session is repeated. In today’s data-driven world, the Data Lakehouse has emerged as a powerful architectural paradigm that unifies the flexibility of data lakes with the reliability and structure of traditional data warehouses. However, organizations must adopt the right data modeling techniques to unlock its full potential to ensure scalability, maintainability and efficiency. This session is designed for beginners looking to demystify the complexities of data modeling for the lakehouse and make informed design decisions. We’ll break down Medallion Architecture, explore key data modeling techniques and walk through the maturity stages of a successful data platform — transitioning from raw, unstructured data to well-organized, query-efficient models.

dbt became the de facto for data teams building reliable and trustworthy SQL code leveraging a modern data stack architecture. The dbt logic needs to be orchestrated, and jobs scheduled to meet business expectations. That’s where Airflow comes into play. In this quick introduction session, you’ll gonna learn: How to leverage dbt-Core & Airflow to orchestrate pipelines Write DAGs in a Pythonic way Apply best practices on your jobs

ETL data pipelines are the bread and butter of data teams that must design, develop, and author DAGs to accommodate the various business requirements. dbt is becoming one of the most used tools to perform SQL transformations on the Data Warehouse, allowing teams to harness the power of queries at scale. Airflow users are constantly finding new ways to integrate dbt with the Airflow ecosystem and build a single pane of glass where Data Engineers can manage and administer their pipelines. Astronomer Cosmos, an open-source product, has been introduced to integrate Airflow with dbt Core seamlessly. Now you can easily see your dbt pipelines fully integrated on Airflow. You will learn the following: How to integrate dbt Core with Airflow How to use Cosmos How to build data pipelines at scale