Systems | Development | Analytics | API | Testing

Demo days: Reliability Under Pressure: How to Build Self-recovering Data Pipelines

Modern data pipelines don’t fail loudly. A schema change slips through. A few bad records halt ingestion. Dashboards go stale. Engineers rerun backfills. Warehouse costs spike. Business teams begin to question the data. Pipeline instability and silent failures remain some of the biggest bottlenecks for analytics teams operating at scale.

Confluent Intelligence expands real-time business data to enterprise AI

Support for the Agent2Agent protocol helps connect AI agents anywhere in real time so they can collaborate at enterprise scale. Multivariate Anomaly Detection takes anomaly detection to the next level, stopping problems before they start.

#OnTheSpot: Connecting Structured Data with Live Web Search for Smarter PR

Public relations professionals spend hours pulling coverage reports, but our Director of Corporate Communications Russell Dougan does it in minutes In our latest, Russell shows how he uses ThoughtSpot Spotter to prep for his QBR. By connecting structured data with live web search, Spotter analyzes Q2 media performance and delivers a plan for which publications and podcasts to prioritize in Q3. This is what agentic analytics looks like for comms teams.

Simba Connect Demo: Simplify Workday Data Access With SQL

From 51 API Calls to 10 Lines of SQL: Accessing Workday Data With Simba Connect Accessing Workday data through raw APIs means dealing with pagination, rate limits, nested JSON parsing, authentication logic, and dozens of sequential API calls — just to build a simple HR dashboard.

New in Confluent Intelligence: A2A, Multivariate Anomaly Detection, Vector Search for Cosmos DB, Amazon S3 Vectors, and More

As AI models are increasingly commoditized, the value driver for enterprises is no longer “Which large language model (LLM) are we using?” but “How can we use our data for reliable, real-time AI decisioning?” Agentic AI systems—where agents plan, decide, and act autonomously—are only as useful as the context they have. When that context is stale, fragmented, or locked away behind brittle point-to-point integrations, even the best models fail to deliver.

Kafka Copy Paste (KCP): How to Migrate to Confluent Cloud in Days, Not Weeks

While Apache Kafka is incredibly powerful, self-managing brokers, upgrades, capacity, security, and incidents can quickly distract teams from what matters most: building real-time applications and delivering business value. Confluent Cloud can remove that operational burden, yet migration can still be seen as risky and tedious.