Databricks, a platform provider for data engineering, data science, machine learning, and analytics, has signed a new partnership with Google to deliver Databricks at global scale on Google Cloud. Under the partnership, organizations can now use Databricks to create a lakehouse capable of data engineering, data science, machine learning, and analytics on Google Cloud’s global, scalable, and elastic network.
Databricks on Google Cloud will deeply integrate with Google BigQuery’s open platform. It will also leverage Google Kubernetes Engine (GKE), enabling customers to deploy Databricks in a fully containerized cloud environment for the first time. With this integrated solution, organizations can unlock AI-driven insights, enable intelligent decision-making, and ultimately accelerate their digital transformations through data-driven applications.
“This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms,” said Ali Ghodsi, CEO and co-founder of Databricks. “We are thrilled to partner with Google Cloud and deliver on our shared vision of a simplified, open, and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster.”
Google Cloud Marketplace
Delivering Databricks on Google Cloud enables customers to rapidly provision Databricks on Google Cloud’s global network, with advanced security and data protection controls required for highly regulated industries, and with the flexibility to quickly adjust usage based on the needs of the business.
Additionally, customers will soon be able to deploy Databricks from the Google Cloud Marketplace, enabling simplified procurement and user provisioning, Single Sign-On, and unified billing.
“Businesses with a strong foundation of data and analytics are well-positioned to grow and thrive in the next decade,” said Thomas Kurian, CEO of Google Cloud. “We’re delighted to deliver Databricks’ lakehouse for AI and ML-driven analytics on Google Cloud. By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network – and our expertise in analytics and delivering containerized applications – we can help companies transform their businesses through the power of data.”
Google BigQuery Integration
Databricks on Google Cloud is tightly integrated with Google BigQuery. This would give users the freedom of choice and access to their choice of data analytics services. With this integration, businesses can extend their existing Databricks lakehouse capabilities, now running on Google Cloud. Businesses can now also cross-leverage Google BigQuery for analytics, ultimately “simplifying” their data investments, increasing usage, and creating new, data-driven business models and opportunities.
Unique integrations between Databricks and Google Cloud would include:
- Tight integration of Databricks with Google Cloud’s analytics solutions, giving customers the ability to easily extend AI-driven insights across data lakes, data warehouses, and multiple business intelligence tools
- Pre-built connectors to seamlessly and quickly integrate Databricks with BigQuery, Google Cloud Storage, Looker and Pub/Sub
- Fast and scalable model training with AI Platform using the data workflows created in Databricks, and simplified deployment of models built in Databricks using AI Platform Prediction
Containerized Databricks Deployments
Databricks on Google Cloud represents the first container-based deployments of Databricks, on any cloud.
Databricks on Google Cloud is built on GKE, Google Cloud’s secured, managed Kubernetes service, to support containerized deployments of Databricks in the cloud. By adopting GKE as an operating environment, Databricks is able to leverage managed services for security, network policy, and compute. As a result, it would provide users with increasing business value through Databricks analytics, AI, and ML capabilities. Additionally, with GKE, Databricks would increase its agility and the ability to accelerate the release of new features, quickly, and at scale.