Level Up Your Column-level Security: Using IAM Data Governance Tags in BigQuery
Learn how to manage column-level access controls in BigQuery using IAM data governance tags, ensuring secure, scalable data classification and protection.
The Reason Behind Stalled AI Projects
As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.
How Cloudera Powers Agentic Workflows? #DataInMotion #Cloudera
Ready to eliminate data delays and fuel your AI models with trusted, real-time insights? In this video, we break down how Cloudera Data in Motion allows organizations to unlock the full potential of their data fabric. Whether your corporate datasets are scattered across diverse storage systems, multiple cloud vendors, or on-premises data centers, Cloudera provides the scalable, engine-agnostic data services required to stream and process information instantly—without needing to redesign or refactor your existing pipelines.
Bridging the gap between SQL and Python with BigQuery and the %%bqsql magic
Seamlessly chain Python and SQL in Jupyter notebooks using BigQuery DataFrames and the %%bqsql cell magic. Learn how in this step-by-step guide.
How to Analyze and Govern Gemini Enterprise App Usage at Scale with BigQuery
Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.
AI to Write Rules, or AI to Make Decisions?
Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.
Extracting and Harvesting Metadata for Cloudera Data Lineage
This is a comprehensive walkthrough of the metadata extraction process for Cloudera Data Lineage. Learn how to utilize the harvesting agent to set up a new metadata source, such as Informatica Oracle, and perform a local extraction. The video demonstrates how the agent securely reads metadata from databases, ETL tools, and reporting systems, staging it as local XML files to ensure data does not leave the network without explicit action.
No walled gardens: Amazon reversed its Seller Partner API fees
Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.
Qlik Global Impact Report: A Letter from Julie Kae
Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.
Cloudera Agent Studio & Iceberg MCP to Monitor Table Health
In this video, Cloudera’s Dipankar demonstrates how to build an AI agent in Cloudera Agent Studio powered by an open-source Apache Iceberg MCP Server. As a real-world use case, the agent monitors Apache Iceberg table health by analyzing metadata for issues such as small files, partition skew, snapshot history, and other operational signals. Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations.