Many organizations are shifting to a data management paradigm called the "Lakehouse," which implements the functionality of structured data warehouses on top of unstructured data lakes. This presents new challenges for query execution engines. The engine needs to provide good performance on the raw uncurated datasets that are ubiquitous in data lakes, and excellent performance on structured data stored in popular columnar file formats like Apache Parquet. Toward these goals, we present Photon, a vectorized query engine for Lakehouse environments that we developed at Databricks. Photon can outperform existing warehouses on SQL workloads and also supports the Apache Spark API. We discuss the design choices we made in Photon (e.g., vectorization vs. code generation) and describe its integration with our existing SQL and Apache Spark runtimes, its task model, and its memory manager. Photon has accelerated some customer workloads by over 10x and has recently allowed Databricks to set a new audited performance record for the official 100TB TPC-DS benchmark.

We live in the golden age of distributed computing. Public cloud platforms now offer virtually unlimited compute and storage resources on demand. At the same time, the Software-as-a-Service (SaaS) model brings enterprise-class systems to users who previously could not afford such systems due to their cost and complexity. Alas, traditional data warehousing systems are struggling to fit into this new environment. For one thing, they have been designed for fixed resources and are thus unable to leverage the cloud's elasticity. For another thing, their dependence on complex ETL pipelines and physical tuning is at odds with the flexibility and freshness requirements of the cloud's new types of semi-structured data and rapidly evolving workloads. We decided a fundamental redesign was in order. Our mission was to build an enterprise-ready data warehousing solution for the cloud. The result is the Snowflake Elastic Data Warehouse, or "Snowflake" for short. Snowflake is a multi-tenant, transactional, secure, highly scalable and elastic system with full SQL support and built-in extensions for semi-structured and schema-less data. The system is offered as a pay-as-you-go service in the Amazon cloud. Users upload their data to the cloud and can immediately manage and query it using familiar tools and interfaces. Implementation began in late 2012 and Snowflake has been generally available since June 2015. Today, Snowflake is used in production by a growing number of small and large organizations alike. The system runs several million queries per day over multiple petabytes of data.