Explains data lakes, their key characteristics, and how they differ from data warehouses in modern data architecture.
Explains core data engineering concepts: metadata, data lineage, and governance, and their importance for scalable, compliant data systems.
Explores the importance of data quality and validation in data engineering, covering key dimensions and tools for reliable pipelines.
Explores Apache Iceberg, Arrow, and Polaris—three key technologies powering modern, high-performance data lakehouse platforms.
An introduction to data warehousing concepts, covering architecture, components, and performance optimization for analytical workloads.
Explains batch processing fundamentals for data engineering, covering concepts, tools, and its ongoing relevance in data workflows.
Explains the data lakehouse architecture, a unified approach combining data lake scalability with warehouse management features like ACID transactions.
Explores core principles of scalable data engineering, including parallelism, minimizing data movement, and designing adaptable pipelines for growing data volumes.
Explains the importance of data storage formats and compression for performance and cost in large-scale data engineering systems.
Explains the Model Context Protocol (MCP), an open standard for connecting AI agents and LLMs to external data sources and tools, enabling interoperability.
A comprehensive 2025 guide to Apache Iceberg, covering its architecture, ecosystem, and practical use for data lakehouse management.
A technical guide on designing and implementing a modern data lakehouse architecture using the Apache Iceberg table format in 2025.
A look at 10 upcoming features and enhancements for the Apache Iceberg data lakehouse table format, expected in 2025.
A guide to setting up and using Dremio's Auto-Ingest feature for automated, event-driven data loading into Apache Iceberg tables from cloud storage.
A tutorial on using SQL with Apache Iceberg tables in the Dremio data lakehouse platform, covering setup and core operations.
Explores how Dremio and Apache Iceberg create AI-ready data by ensuring accessibility, scalability, and governance for machine learning workloads.
A hands-on tutorial for setting up a local data lakehouse with Apache Iceberg, Dremio, and Nessie using Docker in under 10 minutes.
Explores why Parquet is the ideal columnar file format for optimizing storage and query performance in modern data lake and lakehouse architectures.
Quarterly roundup of data lakehouse trends, table formats, and major industry news from Apache Iceberg to Delta Lake.
Explains how to implement access control and security for Apache Iceberg tables at the file, engine, and catalog levels.
