Real-Time Streaming Solution for Call Center Analytics: Business Challenges and Technical Enablement
Description
A large international client with a business footprint in North America, Europe and Africa reached out to us with an interest in having a real-time streaming solution designed and implemented for its call center handling incoming and outgoing client calls. The client had a previous bad experience with another vendor, who overpromised and underdelivered on the latency of the streaming solution. The previous vendor delivered an over-complex streaming data pipeline resulting in the data taking over five minutes to reach a visualization layer. The client felt that architecture was too complex and involved many services integrated together. Our immediate challenges involved gaining the client's trust and proving that our design and implementation quality would supersede a previous experience. To resolve an immediate challenge of the overly complicated pipeline design, we deployed a Databricks Lakehouse architecture with Azure Databricks at the center of the solution. Our reference architecture integrated Genesys Cloud : App Services : Event Hub : Databricks : : Data Lake : Power BI. The streaming solution proved to be low latency (seconds) during the POV stage, which led to …
Description from YouTube. Full content on the video page.
More from Databricks
NewsParallel Coding Agents with Lakebase | Claude Code + GitHub Actions
This video demonstrates how to run multiple coding agents in parallel by combining git worktrees, GitHub Actions, and Lakebase database branching. It shows how each agent automatically receives an isolated database branch for safe experimentation and schema migrations, followed by dedicated preview environments for pull requests.
EventsHow Enterprises Govern AI Agents Across Multiple Models
Databricks announced the general availability of the Unity AI gateway to provide centralized multi-model governance, cost controls, and end-to-end observability for enterprise AI agents. Panelists discussed how coding agents and harnesses are evolving beyond programming into long-running operations, personal software development, and automated organizational workflows.
EventsDemo: Building a Governed AI Agent with Unity AI Gateway
This video demonstrates how to build, update, and govern a store operations AI agent using Databricks Agent Bricks and the Unity AI Gateway. The tutorial highlights integrating custom Model Context Protocol servers, recording execution traces with MLflow, and enforcing security policies and budget controls.
NewsHow ModMed Transforms Healthcare AI and Agentic Workflows with Databricks
ModMed uses the Databricks Lakehouse platform and Unity Catalog to build secure AI-enabled healthcare applications and agentic workflows. The integration of these tools allows both technical and non-technical users to access near real-time data insights and solve complex problems efficiently.
NewsTeach AI how your business actually runs
Model intelligence is no longer the bottleneck for enterprise AI adoption because modern frontier models easily handle complex reasoning tasks. Business value requires providing these models with specific organizational context and metadata about internal processes to create a competitive advantage.

