Systems | Development | Analytics | API | Testing

In this video, we dive into how Click Data Products combine Data Quality, Semantic Types, and business context to create trusted, scalable, and reusable data assets. We explore how semantic types in Qlik help classify and validate data using meaningful business definitions — improving consistency, discoverability, and confidence in analytics and AI-driven insights.

Post-training is rapidly becoming a critical phase of enterprise AI development. To get reliable output from an AI model, organizations must align its terminology (e.g., abbreviation) to fit their specific use cases. But getting started shouldn't require heavy computing resources—you can quickly train an open-source model right on your local device. In this tutorial, we sit down with the ASAP_DPO_Finetuning Cloudera AMP to demonstrate exactly how to align a language model to specific industry standards—in this case, Oil & Gas abbreviations.

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.

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.

Check out what’s new in ThoughtSpot’s latest release. dbt MetricFlow Integration: Seamlessly import semantic definitions from dbt for a single source of truth across your stack. AI Theme Builder: Stop mapping CSS. Describe your brand guidelines and watch a polished UI appear instantly. Enhanced Mobile Experience: Bring decision-making to your pocket with expert-level reasoning via Spotter 3 and mobile-first Muze charts.

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.

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.