Systems | Development | Analytics | API | Testing

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.

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.

Most organizations are investing heavily in AI—but the outputs still aren’t reliable. The reason often isn’t the model. It’s the data pipeline behind it. Disconnected systems, inconsistent preparation, and limited governance make it difficult for AI to produce accurate answers. Before AI can deliver real value, the data feeding it must be structured, contextualized, and governed. In this animation, we break down the 5 Pillars of AI-Ready Data and show how data moves through a connected pipeline before it reaches AI.

Data trust is broken. In the "good old days," one expert vetted one dashboard. Today? You have massive scale and AI models that need accurate data to survive. Jessica Talisman joins Cindi Howson on The Data & AI Chief to reveal why the ontology pipeline is the secret sauce for trustworthy AI. Learn how structural clarity turns data chaos into your biggest competitive advantage. Catch the full discussion on your preferred podcast player!

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.

AI ambition is everywhere. The models are ready, the investment is flowing, yet the outcomes aren’t keeping up. Cloudera’s Data Readiness Index 2026 survey identifies a widening gap between what enterprises want from AI and what they can actually deliver. In this episode of The AI Forecast, Paul Muller sits down with Cloudera CTO Sergio Gago to bring a practitioner’s lens to the problem, drawing on experience across the full spectrum from startups to global enterprises.

In part 4 of this series, Mike Tarallo form Qlik, walks you through the core components of Qlik Datasets, giving you a clear understanding of how to navigate and interpret key features within the platform. We explore the Profile tab, Data Lineage, Impact Analysis, and Data Preview to see how each helps you better understand your data and its flow across systems.

NKT makes the “electrical superhighways” that bring renewable energy to city consumers. Its 24/7 site in Karlskrona, Sweden is the world’s largest producer of high voltage undersea cables, making operational stability vital. However, the plant faced hurdles. Data was trapped in silos, leading to intuition-based decisions with no single source of truth.