Imagine if you could chain together SQL models using nothing but python, write functions that treat Snowflake tables like dataframes and dataframes like SQL tables. Imagine if you could write a SQL airflow DAG using only python or without using any python at all. With Astro SDK, we at Astronomer have gone back to the drawing board around fundamental questions of what DAG writing could look like. Our goal is to empower Data Engineers, Data Scientists, and even the Business Analysts to write Airflow DAGs with code that reflects the data movement, instead of the system configuration. Astro will allow each group to focus on producing value in their respective fields with minimal knowledge of Airflow and high amounts of flexibility between SQL or python-based systems. This is way beyond just a new way of writing DAGs. This is a universal agnostic data transfer system. Users can run the exact same code against different databases (snowflake, bigquery, etc.) and datastores (GCS, S3, etc.) with no changes except to the connection IDs. Users will be able to promote a SQL flow from their dev postgres to their prod snowflake with a single variable change. We are ecstatic to reveal over eight months of work around building a new open-source project that will significantly improve your DAG authoring experience!

This talk tells the story of how we have approached data and analytics as a startup at Preset and how the need for a data orchestrator grew over time. Our stack is (loosely) Fivetran/Segment/dbt/BigQuery/Hightouch, and we finally got to a place where we suffer quite a bit from not having an orchestrator and are bringing in Airflow to address our orchestration needs. This talk is about how startups approach solving data challenges, the shifting role of the orchestrator in the modern data stack, and the growing need for an orchestrator as your data platform becomes more complex.

At Credit Karma, we enable financial progress for more than 100 million of our members by recommending them personalized financial products when they interact with our application. In this talk we are introducing our machine learning platform to build interactive and production model-building workflows to serve relevant financial products to Credit Karma users. Vega, Credit Karma’s Machine Learning Platform, has 3 major components: 1) QueryProcessor for feature and training data generation, backed by Google BigQuery, 2) PipelineProcessor for feature transformations, offline scoring and model-analysis, backed by Apache Beam 3) ModelProcessor for running Tensorflow and Scikit models, backed by Google AI Platform, which provides data scientists the flexibility to explore different kinds of machine learning or deep learning models, ranging from gradient boosted trees to neural network with complex structures Vega exposed a unified Python API for Feature Generation, Modeling ETL, Model Training and Model Analysis. Vega supports writing interactive notebooks and python scripts to run these components in local mode with sampled data and in cloud mode for large scale distributed computing. Vega provides the ability to chain the processors provided by data scientists through Python code to define the entire workflow. Then it automatically generates the execution plan for deploying the workflow on Apache Airflow for running offline model experiments and refreshes. Overall, with the unified python API and automated Airflow DAG generation, Vega has improved the efficiency of ML Engineering. Using Airflow we deploy more than 20K features and 100 models daily

This workshop is sold out Hands on workshop showing how easy it is to deploy Airflow in a public Cloud. Workshop consists of 3 parts: Setting up Airflow environment and CI/CD for DAG deployment Authoring a DAG Troubleshoot Airflow DAG/Task execution failures This workshop will be based on Cloud Composer ( https://cloud.google.com/composer ) This workshop is mostly targeted at Airflow newbies and users who would like to learn more about Cloud Composer and how to develop DAGs using Google Cloud Platform services like BigQuery, Vertex AI, Dataflow.