Introduces Dremio's built-in Open Catalog for Apache Iceberg, offering a zero-configuration, production-ready lakehouse solution with automated management.
A tutorial on using Dremio's AI_CLASSIFY SQL function to categorize data like customer sentiment and support tickets directly within a data lakehouse.
A 2025 year-in-review of key Apache data projects: Iceberg, Polaris, Parquet, and Arrow, detailing their major updates and future roadmap.
A hands-on tutorial exploring Dremio Cloud Next Gen's new free trial, covering its lakehouse platform, AI features, and SQL capabilities.
A comprehensive guide to learning Apache Iceberg, data lakehouse architecture, and Agentic AI with curated tutorials, tools, and resources.
Explores the commercial Apache Iceberg catalog ecosystem, focusing on REST Catalog standards, optimization strategies, and architectural trade-offs.
Explores two paths for building a universal lakehouse catalog that extends beyond Apache Iceberg tables to manage diverse data formats and sources.
A technical guide on using Apache Iceberg with Apache Spark and Polaris for building and managing a data lakehouse, covering setup, operations, and optimization.
Overview of key proposals in Apache Iceberg v4, focusing on performance, metadata efficiency, and portability for modern data workloads.
A comprehensive guide to the data lakehouse architecture, its core components (Iceberg, Delta, Hudi, Paimon), and the surrounding ecosystem for modern data platforms.
A guide to building an autonomous, self-healing optimization pipeline for Apache Iceberg tables to maintain performance and cost efficiency.
Explores challenges and best practices for managing partition evolution and compaction in Apache Iceberg to maintain query performance.
Explains how to use Apache Iceberg's metadata tables to dynamically trigger data compaction based on file size, manifest health, and snapshot patterns.
A guide to scheduling compaction and snapshot expiration in Apache Iceberg tables based on workload patterns and infrastructure constraints.
Explains how to manage Apache Iceberg table metadata by expiring old snapshots and rewriting manifests to prevent performance and cost issues.
Explains techniques for incremental, non-disruptive compaction in Apache Iceberg tables under continuous streaming data ingestion.
A monthly roundup of data engineering links covering Apache Iceberg, Kafka, Debezium, Spark, and lakehouse architecture.
Explains how Apache Iceberg tables degrade without optimization, covering small files, fragmented manifests, and performance impacts.
Explains the importance of table maintenance in Apache Iceberg for data lakehouses, covering metadata and file management.
A guide on how to find, join, and organize community meetups focused on Apache Iceberg and modern data lakehouse architectures.
