Source proof for Zapier Powers Real-Time Customer Experiences with Databricks

Zapier Powers Real-Time Customer Experiences | Databricks Skip to main content Login Why Databricks Discover For App Developers For Execu…

…systems and heavy reliance on one team limited what could be explored or built. After adopting the Databricks Platform, Zapier unified its data and unlocked self-serve, AI-powered analytics across the business. What was once “not worth it” due to cost and time is now routine, from dynamic…

…ystems.” How did Databricks enable real-time analytics and AI-driven workflows? Through the MCP connector , AI agents receive governed, real-time access to Zapier’s own data. Built on top of Unity Catalog , this ensures every query an agent makes follows the same fine-grained permissions and access controls as human users. This enables employees to query, analyze and act on information without relying…

…paign performance and surface deal blockers and expansion signals in real time. Vector Search allows Zapier to index and retrieve both structured and unstructured data at scale, handling over 600k requests per day. “With Vector Search, we now have a single pipeline to ingest and index the data…

…ng semantic search easier and more accessible across the company,” added Lukas. This powers internal RAG-based knowledge retrieval systems, storing embeddings (e.g., help documentation, customer data from third-party applications like Zen…

…mer data from third-party applications like Zendesk) to provide richer context. MLflow and Model Serving operationalize their machine learning workflows. MLflow provides a centralized system for tracking experiments, managing model v…

…rity and Trust Ready to get started? Get a Demo Login Contact Us Try Databricks Customer Stories / Zapier CUSTOMER STORY Delivering AI-powered product and business insights 2–3 hours To…

…ce, automation and decision-making across the business. Share this post Details Industry : Technology and Software Use Case : Analytics and Business Intelligence , Artificial Intelligence , Data…

…for speed and personalization, Zapier’s legacy data stack created bottlenecks. Siloed systems and heavy reliance on one team limited what could be explored or built. After adopting the Databricks Platform, Zapier unified its data and unlocked se…

…use cases were either difficult to achieve or not worth pursuing. High latency Zapier’s legacy data warehouse, AWS Redshift, limited its ability to handle low-latency data, which meant real-time personalization and responsive product experiences required entirely separate systems. That introduced complexities like duplicated pipelines and fragmented sources o…

…ke duplicated pipelines and fragmented sources of truth. Innovation constraints Even when alternative technologies could address specific gaps, the engineering effort required to integrate and maintain them made experimentation costly and slow. Many potential use cases were never attempted because the return did not justify the investment. Data silos Business teams relied on the insights team to model data, design exp…

…e never attempted because the return did not justify the investment. Data silos Business teams relied on the insights team to model data, design experiments and generate reports. In many cases, teams either waited for support or moved forward without data. A…

…that defined its previous stack. Why did Zapier choose the Databricks Platform? Zapier adopted the Databricks Platform to unify its data and make information more accessible across the organization. Databricks allows teams to experiment more freely and deliver new capabilities…

…ystems.” How did Databricks enable real-time analytics and AI-driven workflows? Through the MCP connector , AI agents receive governed, real-time access to Zapier’s own data. Built on top of Unity Catalog , this ensures every query an agent makes follows the same fine-grained permissions and access controls as human users. This enables employees to query, analyze and act on information without relying…

…text. MLflow and Model Serving operationalize their machine learning workflows. MLflow provides a centralized system for tracking experiments, managing model versions and monitoring performance. Model Serving enables teams to deploy models into production with consistent, reliable access. This includes models that support personalization and analytics use cases. By k…

…e results did Zapier achieve with Databricks? At a glance, Zapier successfully: Reduced dashboard creation time from 2–3 days to a few hours Achieved up to 10x faster turnaround for analytics workflows Enabled self-servi…

…pier successfully: Reduced dashboard creation time from 2–3 days to a few hours Achieved up to 10x faster turnaround for analytics workflows Enabled self-service analytics across product, marketing, sales, and support te…

…bled self-service analytics across product, marketing, sales, and support teams Shifted from 0% to ~75% of queries executed via AI-assisted workflows within 6–9 months Rather than measuring success purely in efficiency gains, the bigger shift is t…

…paign performance and surface deal blockers and expansion signals in real time. Vector Search allows Zapier to index and retrieve both structured and unstructured data at scale, handling over 600k requests per day. “With Vector Search, we now have a single pipeline to ingest and index the data…

…ng semantic search easier and more accessible across the company,” added Lukas. This powers internal RAG-based knowledge retrieval systems, storing embeddings (e.g., help documentation, customer data from third-party applications like Zen…

…text. MLflow and Model Serving operationalize their machine learning workflows. MLflow provides a centralized system for tracking experiments, managing model versions and monitoring performance. Model Serving enables teams to deploy models into production with consistent, reliable access. This includes models that support personalization and analytics use cases. By k…

…ystems.” How did Databricks enable real-time analytics and AI-driven workflows? Through the MCP connector , AI agents receive governed, real-time access to Zapier’s own data. Built on top of Unity Catalog , this ensures every query an agent makes follows the same fine-grained permiss…

…paign performance and surface deal blockers and expansion signals in real time. Vector Search allows Zapier to index and retrieve both structured and unstructured data at scale, handling over 600k requests per day. “With Vector Search, we now have a single…

…mer data from third-party applications like Zendesk) to provide richer context. MLflow and Model Serving operationalize their machine learning workflows. MLflow provides a centralized system for tracking experiments, managing model v…

…ness Intelligence , Artificial Intelligence , Data Science , Data + AI Platform Cloud : AWS Product : Agent Bricks , Databricks Marketplace , Unity Catalog Want to learn m…

…ness Intelligence , Artificial Intelligence , Data Science , Data + AI Platform Cloud : AWS Product : Agent Bricks , Databricks Marketplace , Unity Catalog Want to learn m…