As data workloads grow in complexity, teams need seamless orchestration to manage pipelines across batch, streaming, and AI/ML workflows. Apache Airflow provides a flexible and open-source way to orchestrate Databricks’ entire platform, from SQL analytics with Materialized Views (MVs) and Streaming Tables (STs) to AI/ML model training and deployment. In this session, we’ll showcase how Airflow can automate and optimize Databricks workflows, reducing costs and improving performance for large-scale data processing. We’ll highlight how MVs and STs eliminate manual incremental logic, enable real-time ingestion, and enhance query performance—all while maintaining governance and flexibility. Additionally, we’ll demonstrate how Airflow simplifies ML model lifecycle management by integrating Databricks’ AI/ML capabilities into end-to-end data pipelines. Whether you’re a dbt user seeking better performance, a data engineer managing streaming pipelines, or an ML practitioner scaling AI workloads, this session will provide actionable insights on using Airflow and Databricks together to build efficient, cost-effective, and future-proof data platforms.

As data workloads grow in complexity, teams need seamless orchestration to manage pipelines across batch, streaming, and AI/ML workflows. Apache Airflow provides a flexible and open-source way to orchestrate Databricks’ entire platform, from SQL analytics with Materialized Views (MVs) and Streaming Tables (STs) to AI/ML model training and deployment. In this session, we’ll showcase how Airflow can automate and optimize Databricks workflows, reducing costs and improving performance for large-scale data processing. We’ll highlight how MVs and STs eliminate manual incremental logic, enable real-time ingestion, and enhance query performance—all while maintaining governance and flexibility. Additionally, we’ll demonstrate how Airflow simplifies ML model lifecycle management by integrating Databricks’ AI/ML capabilities into end-to-end data pipelines. Whether you’re a dbt user seeking better performance, a data engineer managing streaming pipelines, or an ML practitioner scaling AI workloads, this session will provide actionable insights on using Airflow and Databricks together to build efficient, cost-effective, and future-proof data platforms.

Building and maintaining data pipelines and workflows can be very complicated, difficult to manage and error prone. AirBnb created and open-sourced Apache Airflow in 2015 to solve a lot of the problems. If you have ever needed to automate, schedule, handle errors or send out alerts for your data workflows then Airflow will be the perfect new tool in your data toolbox.

The advancements in data and machine learning have resulted in some fascinating results. We will explore some exciting recent projects that are using Google Cloud and the open source library TensorFlow with the aim to inspire you to build things you may have thought were not possible.