Enterprise AI Infrastructure Security Series - 7) Monitoring & Auditing

In this final video of our enterprise AI security series, we cover ClearML's monitoring and audit trail capabilities — the visibility layer that ties everything together. We walk through the platform's operational dashboards, task-level audit surfaces, cost attribution, and external integration points, showing how ClearML delivers live operations and compliance-ready audit out of the box.

How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost

For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.

Raising the Bar: Can Your Charts Do This?

Visualizations in business intelligence software are often dismissed as a “commodity”, interchangeable and easy to overlook. But what this perspective ignores is that visualizations are a gateway to better understanding data. Instead of parsing through raw data, they make key details and trends visible so that users can easily interpret the insights derived from all the data gathering, preparation, and analysis.

Data Integration Tools Aren't the Problem. Your Source Data Is.

Data integration tools are designed to move and join data. But what they’re not designed to do is burn half their capacity cleaning up what arrives at the input. When a source exposes a schema built for application performance rather than analytics, the pipeline must compensate: Anything typed as a string because it was easier at build time gets cast into numbers or dates before a calculation can touch it. The difficult truth is this is cleanup and not value-added integration work.

Why Optimization in a Data Lakehouse is important? #cloudera #techshort #DataLakehouse

Discover the importance of optimization when operationalizing a data lakehouse for production workloads. We break down the journey of bringing a lakehouse into production—from choosing your data file format (Parquet) and table format (Iceberg) to plugging in your catalog and compute engines. Finally, learn why balancing ingestion jobs with critical table management services makes all the difference when moving beyond single-node workloads.

Why Cloudera AI is the Key to Solving Your Data Readiness and AI Project Backlog

Stop your AI projects from being abandoned due to a lack of data readiness. Cloudera AI provides the tools to secure, govern, and prepare your data for production, no matter where it lives. Turbocharge your AI journey today. Contact your Cloudera representative to learn more. *Read More:* Check out our blog post on solving the AI backlog.

Core Design Primitive of Apache Iceberg #Cloudera #short #techshort

In this video, Dipankar breaks down how Apache Iceberg works under the hood - starting from the limitations of Hive-style tables to why Iceberg was built in the first place. What you’ll learn: The shift from directory-based to metadata-driven architecture. How Iceberg tracks files on S3/Object Storage. Why abstraction is the key to scaling your data platform.