Analysis of Dremio's Lakehouse AI report on shifting enterprise priorities from cost migration to agent-ready data platforms.
Explores how open standards like Iceberg and REST catalogs prevent data silos for AI agents in lakehouse architectures.
Explains a five-layer architecture for safe agentic analytics, preventing agents from directly querying raw storage.
Guide to zero-copy mirroring for migrating proprietary data warehouses to open Apache Iceberg tables, assessing conditions and staged migration.
Designing secure, air-gapped data lakehouses using Apache Iceberg for defense, healthcare, finance, and other high-security sectors.
Explores multi-engine catalog federation using Apache Polaris to sync metadata across Google Cloud, AWS, and Azure for open lakehouse governance.
Explains how to prepare a data lakehouse for EU AI Act compliance, focusing on data lineage and provenance.
Technical guide on preparing data lakehouses for EU AI Act compliance, focusing on auditable lineage and data provenance.
Overview of modern Python tools for Apache Iceberg, including PyIceberg, IceFrame, and CLI for metadata management.
Overview of Apache Iceberg v4 roadmap proposals including adaptive metadata trees, single-file commits, and convergence with Delta Lake.
A guide to preventing data swamps in lakehouses through active governance, metadata stewardship, schema evolution safety, and drift detection.
Best practices for managing Apache Iceberg snapshot expiration in data lakehouses to optimize query performance and metadata size.
Explores how Apache Iceberg decouples storage and compute for cost optimization, including multi-engine routing and TCO analysis.
Explains the 2026 unified data architecture for multi-cloud data lakehouses using open standards like Apache Iceberg.
Comparison of Iceberg catalog control planes: Polaris, Unity Catalog, and Cloud REST for lakehouse architecture.
Overview of Apache Iceberg 1.11.0 release, covering new features like metadata encryption, pluggable file formats, and query optimizations.
Explains Apache Iceberg metadata tables for querying table internals using SQL, covering snapshots, files, manifests, partitions, and practical use cases.
Explains Apache Parquet's columnar architecture, dictionary encoding, and performance benefits for data analytics.
Explains Apache Iceberg, a table format that replaces directory-based metadata with file-level tracking for scalable analytics on cloud storage.
A guide to integrating Dremio's data lakehouse platform with Amazon Kiro's AI IDE for data querying, app building, and pipeline generation.
