Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
How Databricks & Snowflake work;
Why they’re so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives
Garren is a Solutions Architect at Databricks. He has specialized in big data for 7 years and Apache Spark for the past 4 years. Garren created Structured Streaming and Spark ML production applications to do real-time decision making, built a robust real-time big data science and reporting solution (30B+ records aggregated in < 1 second), and architected the core IP data assets for a B2B marketing company. His interests include enabling data scientists and engineers to use big data at scale to solve vexing problems. Garren has a BBA in Management Information Systems from Washington State University.