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Thoughts on tech, data, policy, and philosophy.
Dimensional Modeling: Facts, Dimensions, and Grains
Dimensional modeling is the most widely used approach for organizing analytics data....
Apache Data Lakehouse Weekly: March 10–17, 2026
The open lakehouse stack had one of its busiest weeks in months....
Apache Data Lakehouse Weekly: March 10–17, 2026
AI Weekly: Agents Take Over, MCP Evolves, and Models Battle for Code
Week of March 10–17, 2026...
AI Weekly: Agents Take Over, MCP Evolves, and Models Battle for Code
Analyze Unstructured Data with SQL and AI
Data Modeling for the Lakehouse: What Changes
Traditional data modeling assumed you controlled the database....
Apache Data Lakehouse Weekly: March 3–10, 2026
The Apache lakehouse ecosystem had another active week....
Apache Data Lakehouse Weekly: March 3–10, 2026
How to Use Dremio with Claude Code: Connect, Query, and Build Data Apps
How to Use Dremio with Claude Code: Connect, Query, and Build Data Apps
Learn about Dremio’s new Native Claude Connector...
Why Slow Queries Kill AI Analytics Adoption
AI Weekly: March 3–10, 2026
Claude Code has hit number one among developer tools....
AI Weekly: March 3–10, 2026
How to Use Dremio with Claude CoWork: Connect, Query, and Build Data Apps
Learn about Dremio’s new Native Claude Connector...
How to Use Dremio with Claude CoWork: Connect, Query, and Build Data Apps
Star Schema vs. Snowflake Schema: When to Use Each
Both star schemas and snowflake schemas are dimensional models....
Context Management Strategies for ChatGPT: A Complete Guide to Getting Better Results
Getting consistently useful results from ChatGPT requires more than writing good prompts. The real differentiator is how you manage context: the backg...
Context Management Strategies for Claude Code: A Complete Guide for Developers
Claude Code is a terminal-native agentic coding assistant that lives in your command line and operates directly on your codebase. Unlike chat-based in...
Context Management Strategies for Claude CoWork: A Complete Guide for Knowledge Workers
Claude CoWork represents a fundamentally different approach to AI context management. Unlike chat interfaces where you send messages and receive respo...
Context Management Strategies for Claude Desktop: A Complete Guide to MCP, Computer Use, and Local File Access
Claude Desktop takes everything available in Claude Web and adds three capabilities that fundamentally change how you manage context: MCP server conne...
Context Management Strategies for Claude Web: A Complete Guide to Projects, Artifacts, and Intelligent Context
Claude's web interface at claude.ai combines one of the largest context windows in the industry with a structured Project system that makes it genuine...
Context Management Strategies for Cursor: A Complete Guide to the AI-Native Code Editor
Cursor is an AI-native code editor built on the VS Code foundation that integrates AI deeply into every aspect of the development workflow. Its contex...
Context Management Strategies for Gemini CLI: A Complete Guide to Terminal-Native AI Development
Gemini CLI is an open-source terminal agent powered by Gemini models that operates directly in your command line. It brings Google's AI capabilities i...
Context Management Strategies for Gemini Web and NotebookLM: A Complete Guide to Google's AI Knowledge Ecosystem
Google's AI ecosystem for knowledge work consists of two deeply integrated tools: Gemini (the conversational AI at gemini.google.com) and NotebookLM (...
Context Management Strategies for Google Antigravity: A Complete Guide to the Agent-First IDE
Google Antigravity is an agent-first IDE built by Google DeepMind's Advanced Agentic Coding team. It approaches context management differently from ot...
Context Management Strategies for OpenAI Codex: A Complete Guide Across Browser, CLI, and App
OpenAI Codex is not a chatbot. It is an autonomous software engineering agent that runs tasks in isolated cloud sandboxes, operates across a browser i...
Context Management Strategies for OpenCode: A Complete Guide to the Open-Source Terminal AI Agent
OpenCode is an open-source terminal-based AI coding agent that prioritizes privacy, local-first operation, and broad model provider support. Built as ...
Context Management Strategies for OpenWork: A Complete Guide to the Desktop AI Agent Framework
OpenWork is a desktop-native AI agent framework designed for local, multi-step task execution on your computer. Unlike browser-based AI tools or termi...
Context Management Strategies for Perplexity AI: A Complete Guide to Research-First AI Conversations
Perplexity AI occupies a unique position in the AI landscape: it is a research-first tool that combines conversational AI with real-time web search to...
Context Management Strategies for T3 Chat: A Complete Guide to the Unified Multi-Model AI Interface
T3 Chat is a modern web-based AI chat interface that gives you access to multiple AI models through a single unified platform. Its primary value propo...
Context Management Strategies for VS Code with LLM Plugins: A Complete Guide to Building Your Own AI-Powered IDE
Visual Studio Code is the most widely used code editor in the world, and its extensibility means you can integrate AI capabilities through a growing e...
Context Management Strategies for Windsurf: A Complete Guide to the AI Flow IDE
Windsurf is an AI-powered IDE built on the VS Code foundation that introduces the concept of "Flows," a paradigm where the AI maintains deep awareness...
Context Management Strategies for Zed: A Complete Guide to the High-Performance AI Code Editor
Zed is a high-performance code editor built in Rust that prioritizes speed, simplicity, and real-time collaboration. Its AI integration is designed to...
The Model Context Protocol (MCP) Explained: A Complete Guide to How Every Major AI Tool Connects to External Data
The Model Context Protocol (MCP) has become the universal standard for connecting AI models to external tools, data sources, and services. Originally ...
My Non-Fiction Library: Books on Data Lakehouses, Apache Iceberg, AI, and Beyond
I have spent the last several years writing, teaching, and speaking about data architecture, open table formats, and AI-assisted development....
Stories I Created: A Tour of My Fiction Books
I am a technologist and technical author by day, but I also create fiction....
Apache Data Lakehouse Weekly: February 26 – March 5, 2026
Get Data Lakehouse Books:...
Apache Data Lakehouse Weekly: February 26 — March 5, 2026
AI Weekly: Claude Code Dominates, MCP Goes Mainstream — Week of March 5, 2026
AI Weekly: Claude Code Dominates, MCP Goes Mainstream — Week of March 5, 2026
The past seven days confirmed what developers have been saying for months: AI coding tools are no longer optional, and the competition is reshaping ho...
How to Use Dremio with Amazon Kiro: Connect, Query, and Build Data Apps
Amazon Kiro is an agentic AI IDE from AWS that introduces spec-driven development to the coding workflow. Instead of jumping straight to code, Kiro he...
How to Use Dremio with Claude Code: Connect, Query, and Build Data Apps
Claude Code is Anthropic's terminal-based coding agent. It reads your files, writes code, runs commands, and maintains context across a session. Dremi...
How to Use Dremio with Claude CoWork: Connect, Query, and Build Data Apps
Claude CoWork is Anthropic's desktop agentic assistant. Unlike Claude Code (a terminal coding agent), CoWork operates as a general-purpose autonomous ...
How to Use Dremio with Cursor: Connect, Query, and Build Data Apps
Cursor is an AI-native code editor built as a fork of VS Code. It integrates AI directly into the editing experience with features like Chat, Composer...
How to Use Dremio with Gemini CLI: Connect, Query, and Build Data Apps
Gemini CLI is Google's open-source terminal-based AI agent. It runs directly in your terminal, powered by Gemini models with a 1-million token context...
How to Use Dremio with GitHub Copilot: Connect, Query, and Build Data Apps
GitHub Copilot is the most widely adopted AI coding assistant, integrated into VS Code, JetBrains IDEs, and the GitHub platform. Its agent mode allows...
How to Use Dremio with Google Antigravity: Connect, Query, and Build Data Apps
Google Antigravity is an agent-first IDE built by Google DeepMind. Its autonomous agents plan multi-step tasks, write code, browse documentation, and ...
How to Use Dremio with JetBrains AI Assistant: Connect, Query, and Build Data Apps
JetBrains AI Assistant is built into IntelliJ IDEA, PyCharm, DataGrip, and every JetBrains IDE. It provides AI chat, inline code generation, multi-fil...
How to Use Dremio with OpenAI Codex CLI: Connect, Query, and Build Data Apps
OpenAI Codex CLI is a terminal-based coding agent built in Rust. It reads your codebase, writes files, executes commands, and supports MCP for connect...
How to Use Dremio with OpenCode: Connect, Query, and Build Data Apps
OpenCode is an open-source, terminal-based AI coding agent released under the MIT license. It provides a TUI with split panes, uses the Language Serve...
How to Use Dremio with OpenWork: Connect, Query, and Build Data Apps
OpenWork is an open-source desktop AI agent built on the OpenCode engine. It runs entirely on your machine with your own API keys, giving you full con...
How to Use Dremio with Windsurf: Connect, Query, and Build Data Apps
Windsurf is an AI-native code editor built as a fork of VS Code. Its standout feature is Cascade, an agentic AI system that plans and executes multi-s...
How to Use Dremio with Zed: Connect, Query, and Build Data Apps
Zed is an open-source, GPU-accelerated code editor written in Rust. It is designed for speed and collaboration, with a built-in AI assistant that supp...
Conceptual, Logical, and Physical Data Models Explained
Most data teams jump straight from a stakeholder request to creating database tables....
Classify Your Data with SQL: A Hands-On Guide to Dremio's AI_CLASSIFY Function
Most classification workflows require exporting data to Python, running a model, and importing results back into your warehouse. Dremio's AI_CLASSIFY ...
Connect Amazon Redshift to Dremio Cloud: Extend Your Warehouse with Federation and AI Analytics
Amazon Redshift is AWS's managed data warehouse, designed for petabyte-scale analytics. If your organization chose Redshift for analytical workloads, ...
Connect Amazon S3 to Dremio Cloud: Query Your Data Lake with SQL, Federation, and AI
Amazon S3 is the default landing zone for data in the cloud. Log files, Parquet datasets, CSV exports, JSON events, IoT telemetry, and raw data dumps ...
Connect Any Iceberg REST Catalog to Dremio Cloud: Universal Lakehouse Access
The Apache Iceberg REST Catalog specification defines a standard HTTP API for managing Iceberg table metadata. Any catalog implementation that conform...
Connect Apache Druid to Dremio Cloud: Add SQL Joins, AI, and Governance to Your Real-Time Analytics
Apache Druid is a real-time analytics database designed for sub-second queries on high-ingestion-rate event data. Clickstream analytics, application m...
Connect AWS Glue Data Catalog to Dremio Cloud: Query and Manage Your AWS Iceberg Tables
AWS Glue Data Catalog is AWS's managed metadata service for data lakes. It stores table definitions, schemas, partition information, and statistics fo...
Connect Azure Storage to Dremio Cloud: Query Your Microsoft Data Lake with SQL and AI
Azure Storage is Microsoft's cloud storage platform, spanning Blob Storage, Azure Data Lake Storage Gen2 (ADLS Gen2), and Azure Files. If your organiz...
Connect Azure Synapse Analytics to Dremio Cloud: Multi-Cloud Data Warehouse Federation
Microsoft Azure Synapse Analytics combines big data analytics and enterprise data warehousing into a single Azure-integrated platform. If your organiz...
Connect Databricks Unity Catalog to Dremio Cloud: Query Delta Lake Tables with Federation and AI
Databricks Unity Catalog is Databricks' governance layer for data and AI assets. It manages Delta Lake tables, machine learning models, feature stores...
Connect Dremio Software to Dremio Cloud: Hybrid Federation Across Deployments
Dremio Cloud can connect to Dremio Software (self-managed) instances as a federated data source. This creates a hybrid deployment where Dremio Cloud s...
Connect Google BigQuery to Dremio Cloud: Cross-Cloud Analytics Without Data Movement
Google BigQuery is Google Cloud's serverless data warehouse. If your organization uses Google Cloud Platform, BigQuery is where your analytics data, m...
Connect IBM Db2 to Dremio Cloud: Modernize Mainframe Analytics with Federation and AI
IBM Db2 is the relational database that powers critical applications across banking, insurance, government, healthcare, and manufacturing. For organiz...
Connect Microsoft SQL Server to Dremio Cloud: Federate Enterprise Data Without ETL
Microsoft SQL Server is one of the most widely deployed enterprise databases in the world. ERP systems, CRM platforms, financial applications, and cus...
Connect MongoDB to Dremio Cloud: SQL Analytics on Document Data
MongoDB is the most popular NoSQL document database. It stores data in flexible JSON-like documents, making it ideal for applications with evolving sc...
Connect MySQL to Dremio Cloud: Federated Analytics Without ETL
MySQL runs more web applications, SaaS platforms, and e-commerce backends than any other database. It's fast for transactional reads and writes, but i...
Connect Oracle Database to Dremio Cloud: Enterprise Analytics Without Data Movement
Oracle Database runs the most critical enterprise applications in the world — ERP systems, financial ledgers, supply chain management, and HR platform...
Connect PostgreSQL to Dremio Cloud: Query, Federate, and Accelerate Your Data
PostgreSQL powers more production applications than almost any other open-source database. It's where your customer records, transaction logs, product...
Connect SAP HANA to Dremio Cloud: Unlock Analytics Beyond the SAP Ecosystem
SAP HANA is the in-memory database platform that powers SAP S/4HANA, SAP BW/4HANA, and custom enterprise applications across finance, manufacturing, l...
Connect Snowflake Open Catalog to Dremio Cloud: Multi-Engine Iceberg Analytics
Snowflake Open Catalog is Snowflake's managed implementation of the Apache Iceberg REST catalog specification, based on the open-source Apache Polaris...
Connect Snowflake to Dremio Cloud: Federate, Govern, and Accelerate Beyond Snowflake
Snowflake is a popular cloud data warehouse known for its separation of storage and compute, near-zero maintenance, and broad ecosystem. Many organiza...
Connect Vertica to Dremio Cloud: Federation for Analytics-Optimized Data
Vertica is a columnar analytics database engineered for fast aggregate queries on large datasets. It was built from the ground up for analytical workl...
Dremio's Built-in Open Catalog: Your Zero-Configuration Apache Iceberg Lakehouse
Every Dremio Cloud account starts with a built-in Open Catalog — a fully managed Apache Iceberg catalog with integrated storage. When you create a Dre...
Extract Structured Data from Text with Dremio's AI_GENERATE Function
Unstructured text is the most underused data in most organizations. Customer emails sit in inboxes. Contract notes live in text fields. Meeting summar...
Generate Summaries and Insights with Dremio's AI_COMPLETE Function
Every data team has a version of this problem: a table full of raw data that needs human-readable summaries, translations, or narrative descriptions. ...
Apache Data Lakehouse Weekly: February 18–25, 2026
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AI Weekly: Rubin GPUs, Vibe Coding Debates, and MCP Goes Global
This week brought major hardware news from NVIDIA, heated debates about AI coding productivity, and the MCP protocol expanding from a developer tool i...
What Is Data Modeling? An Introduction
Every database, data warehouse, and data lakehouse starts with the same question: how should this data be organized?...
Apache Data Lakehouse Weekly: February 11–17, 2026
Get Data Lakehouse Books:...
AI Weekly: Feb 11–17, 2026
Developers stopped writing code by hand....
Batch vs. Streaming: Choose the Right Processing Model
!Batch processing in scheduled groups vs streaming in continuous flow(/assets/images/2026/debp/06-batch-vs-streaming/batch-vs-streaming.png)......
Conceptual, Logical, and Physical Data Models Explained
!Three layers of data modeling from business concepts to database implementation(/assets/images/2026/data_modeling/02-types-of-data-models/types-of-da...
Data Engineering Best Practices: The Complete Checklist
!Comprehensive data engineering checklist organized by categories with status indicators(/assets/images/2026/debp/10-de-best-practices-checklist/de-ch...
Data Modeling Best Practices: 7 Mistakes to Avoid
!Checklist of data modeling quality markers with warning symbols on common mistakes(/assets/images/2026/data_modeling/10-data-modeling-best-practices/...
Data Modeling for Analytics: Optimize for Queries, Not Transactions
!OLTP normalized model vs. OLAP denormalized model side by side(/assets/images/2026/data_modeling/07-data-modeling-for-analytics/analytics-data-modeli...
Data Modeling for the Lakehouse: What Changes
!Traditional data warehouse model vs. open lakehouse model with flexible schema and views(/assets/images/2026/data_modeling/04-data-modeling-lakehouse...
Data Quality Is a Pipeline Problem, Not a Dashboard Problem
!Data quality checks enforced at the pipeline validation stage before data reaches consumers(/assets/images/2026/debp/03-data-quality-first/data-quali...
Data Vault Modeling: Hubs, Links, and Satellites
!Data Vault model showing Hubs, Links, and Satellites as interconnected components(/assets/images/2026/data_modeling/09-data-vault-modeling/data-vault...
Data Virtualization and the Semantic Layer: Query Without Copying
!Data virtualization — connecting sources to a unified semantic layer without copying(/assets/images/2026/semantic_layer_seo/07-data-virtualization-se...
Denormalization: When and Why to Flatten Your Data
!Normalized model with many interconnected tables vs. denormalized wide flat table(/assets/images/2026/data_modeling/08-denormalization-when-why/denor...
Dimensional Modeling: Facts, Dimensions, and Grains
!Dimensional model showing a central fact table connected to surrounding dimension tables(/assets/images/2026/data_modeling/05-dimensional-modeling/di...
Headless BI: How a Universal Semantic Layer Replaces Tool-Specific Models
!Headless BI — one semantic layer serving all consumers(/assets/images/2026/semantic_layer_seo/08-headless-bi-semantic-layer/headless-bi.png)......
How a Self-Documenting Semantic Layer Reduces Data Team Toil
!Self-documenting semantic layer — AI generating descriptions and labels automatically(/assets/images/2026/semantic_layer_seo/09-self-documenting-sema...
How to Build a Semantic Layer: A Step-by-Step Guide
!Building a semantic layer — Bronze, Silver, and Gold tiers(/assets/images/2026/semantic_layer_seo/02-how-to-build-semantic-layer/build-semantic-layer...
How to Design Reliable Data Pipelines
!Data pipeline architecture with four layers flowing from ingestion through staging, transformation, and serving(/assets/images/2026/debp/02-design-da...
How to Think Like a Data Engineer
!Data flowing through a system of interconnected pipeline stages from sources to consumers(/assets/images/2026/debp/01-think-like-data-engineer/data-e...
Idempotent Pipelines: Build Once, Run Safely Forever
!Pipeline running multiple times and converging to the same result(/assets/images/2026/debp/04-idempotent-pipelines/idempotent-pipeline.png)......
Partition and Organize Data for Performance
!Table data split into partitions by date with query scanning only the relevant partition(/assets/images/2026/debp/07-partition-and-organize/partition...
Pipeline Observability: Know When Things Break
!Pipeline observability dashboard showing metrics, logs, and data lineage(/assets/images/2026/debp/09-observability-monitoring/observability-dashboard...
Schema Evolution Without Breaking Consumers
!Schema as a contract between producers and consumers with version tracking(/assets/images/2026/debp/05-schema-evolution/schema-contract.png)......
Semantic Layer Best Practices: 7 Mistakes to Avoid
!Semantic layer best practices checklist — checks and mistakes(/assets/images/2026/semantic_layer_seo/10-semantic-layer-best-practices/best-practices....
Semantic Layer vs. Data Catalog: Complementary, Not Competing
!Data catalog and semantic layer — complementary systems bridged together(/assets/images/2026/semantic_layer_seo/04-semantic-layer-vs-data-catalog/cat...
Semantic Layer vs. Metrics Layer: What's the Difference?
!Semantic layer vs metrics layer — the metrics layer is a subset(/assets/images/2026/semantic_layer_seo/03-semantic-layer-vs-metrics-layer/semantic-vs...
Slowly Changing Dimensions: Types 1-3 with Examples
!Dimension timeline showing attribute values changing across time periods(/assets/images/2026/data_modeling/06-slowly-changing-dimensions/slowly-chang...
Star Schema vs. Snowflake Schema: When to Use Each
!Star schema with central fact table surrounded by denormalized dimension tables(/assets/images/2026/data_modeling/03-star-schema-vs-snowflake/star-vs...
Testing Data Pipelines: What to Validate and When
!Data pipeline testing pyramid with schema tests at the base, contract tests in the middle, and regression tests at the top(/assets/images/2026/debp/0...
The Role of the Semantic Layer in Data Governance
!Data governance through a semantic layer — centralized policies and documentation(/assets/images/2026/semantic_layer_seo/06-semantic-layer-data-gover...
What Is a Semantic Layer? A Complete Guide
!Semantic layer concept — translating raw data into business terms(/assets/images/2026/semantic_layer_seo/01-what-is-a-semantic-layer/semantic-layer-c...
What Is Data Modeling? A Complete Guide
!Data entities connected by relationship lines forming a structured data model(/assets/images/2026/data_modeling/01-what-is-data-modeling/data-modelin...
Why Your AI Initiatives Fail Without a Semantic Layer
!AI with vs without a semantic layer — failure modes and fixes(/assets/images/2026/semantic_layer_seo/05-why-ai-fails-without-semantic-layer/ai-semant...
A 2026 Introduction to Apache Iceberg
Apache Iceberg is an open-source table format for large analytic datasets....
A 2026 Introduction to Apache Iceberg
An updated introduction to Apache Iceberg...
A Practical Guide to AI-Assisted Coding Tools
An in-depth guide to understanding, choosing, and using AI-assisted coding tools effectively....
What Are Recursive Language Models?
Recursive Language Models (RLMs) are a new class of language models that can call themselves to break down complex tasks into manageable parts. This a...
2025 Year in Review Apache Iceberg, Polaris, Parquet, and Arrow
A look back at key developments in Apache Iceberg, Polaris, Parquet, and Arrow in 2025....
dremioframe & iceberg - Pythonic interfaces for Dremio and Apache Iceberg
Discover DremioFrame and IceFrame, two new Python libraries that simplify working with Dremio and Apache Iceberg. Learn how these tools streamline dat...
Introducing dremioframe - A Pythonic DataFrame Interface for Dremio
Discover dremioframe, a new Python library that offers a DataFrame-like experience for interacting with Dremio's data lakehouse platform. Learn how to...
Comprehensive Hands-on Walk Through of Dremio Cloud Next Gen (Hands-on with Free Trial)
Walkthrough with the new trial of the Dremio Cloud Platform...
2025-2026 Guide to Learning about Apache Iceberg, Data Lakehouse & Agentic AI
A curated guide to mastering Apache Iceberg, data lakehouse architectures, and the emerging field of Agentic AI for data professionals....
An Exploration of the Commercial Iceberg Catalog Ecosystem
Dive into the world of commercial Iceberg catalogs and discover how they enhance data lakehouse architectures for modern data engineering....
Building a Universal Lakehouse Catalog - Beyond Iceberg Tables
Exploring paths to a universal lakehouse catalog that supports multiple data formats and engines, building on Apache Iceberg's success....
Intro to Apache Iceberg with Apache Polaris and Apache Spark
Learn how to leverage Apache Iceberg with Apache Polaris and Apache Spark to build scalable and efficient data lakehouses....
The State of Apache Iceberg v4 - October 2025 Edition
What's Coming in Apache Iceberg v4: A Deep Dive into the Future of Open Table Formats...
The Ultimate Guide to Open Table Formats - Iceberg, Delta Lake, Hudi, Paimon, and DuckLake
Understanding Iceberg, Delta Lake, Hudi, Paimon, and DuckLake...
The 2025 & 2026 Ultimate Guide to the Data Lakehouse and the Data Lakehouse Ecosystem
What is the Data Lakehouse and the Data Lakehouse Ecosystem? This comprehensive guide covers everything you need to know about the Data Lakehouse arch...
The Endgame — Building an Autonomous Optimization Pipeline for Apache Iceberg
Learn how to automate compaction, snapshot expiration, and layout optimization in Apache Iceberg using metadata-driven triggers and orchestration tool...
Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery
Learn how to scale Apache Iceberg table optimizations across large datasets using parallelism, checkpointing, and fail recovery to ensure reliability ...
Unlocking the Power of Agentic AI with Apache Iceberg and Dremio
Unlocking the Power of Agentic AI with Apache Iceberg and Dremio...
Hidden Pitfalls — Compaction and Partition Evolution in Apache Iceberg
Partition evolution in Apache Iceberg is a powerful feature, but if not managed carefully, it can introduce fragmentation and impact compaction perfor...
Using Iceberg Metadata Tables to Determine When Compaction Is Needed
Discover how to use Apache Iceberg's metadata tables to proactively detect small files, bloated manifests, and table fragmentation—so you can trigger ...
Designing the Ideal Cadence for Compaction and Snapshot Expiration
Learn how to design an effective schedule for compaction and snapshot expiration in Apache Iceberg to balance cost, performance, and data freshness....
Avoiding Metadata Bloat with Snapshot Expiration and Rewriting Manifests
Learn how to prevent and clean up metadata bloat in Apache Iceberg by expiring snapshots and rewriting manifests for better performance and manageabil...
Smarter Data Layout — Sorting and Clustering Iceberg Tables
Improve query performance in Apache Iceberg by organizing your data layout with sorting and Z-order clustering. Learn how to reduce scan cost and impr...
Optimizing Compaction for Streaming Workloads in Apache Iceberg
Learn how to design fast, incremental compaction strategies in Apache Iceberg to support high-throughput streaming pipelines without disrupting freshn...
The Basics of Compaction — Bin Packing Your Data for Efficiency
Learn how standard compaction works in Apache Iceberg and why bin packing your data files is essential for maintaining query performance and cost effi...
The Cost of Neglect — How Apache Iceberg Tables Degrade Without Optimization
Learn how Apache Iceberg tables can degrade over time without optimization and what issues this causes for performance, cost, and governance....
How to Discover or Organize Lakehouse & Apache Iceberg Meetups
Guide on How to Be Part of the Lakehouse Community...
Introduction to Data Engineering Concepts | What is Data Engineering?
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Understanding Data Sources and Ingestion
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | ETL vs ELT – Understanding Data Pipelines
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Batch Processing Fundamentals
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Streaming Data Fundamentals
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Data Modeling Basics
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Data Warehousing Fundamentals
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Data Lakes Explained
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Storage Formats and Compression
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Data Quality and Validation
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Metadata, Lineage, and Governance
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Scheduling and Workflow Orchestration
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Building Scalable Pipelines
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | DevOps for Data Engineering
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Cloud Data Platforms and the Modern Stack
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Data Lakehouse Architecture Explained
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | Apache Iceberg, Arrow, and Polaris
Introduction to the terms in data engineering...
Introduction to Data Engineering Concepts | The Power of Dremio in the Modern Lakehouse
Introduction to the terms in data engineering...
A Journey from AI to LLMs and MCP - 10 - Sampling and Prompts in MCP — Making Agent Workflows Smarter and Safer
Sampling and Prompts in MCP — Making Agent Workflows Smarter and Safer...
A Journey from AI to LLMs and MCP - 9 - Tools in MCP — Giving LLMs the Power to Act
Tools in MCP — Giving LLMs the Power to Act...
A Journey from AI to LLMs and MCP - 8 - Resources in MCP — Serving Relevant Data Securely to LLMs
Resources in MCP — Serving Relevant Data Securely to LLMs...
A Journey from AI to LLMs and MCP - 7 - Under the Hood — The Architecture of MCP and Its Core Components
Under the Hood — The Architecture of MCP and Its Core Components...
Journey from AI to LLMs and MCP - 6 - Enter the Model Context Protocol (MCP) — The Interoperability Layer for AI Agents
Enter the Model Context Protocol (MCP) — The Interoperability Layer for AI Agents...
A Journey from AI to LLMs and MCP - 5 - AI Agent Frameworks — Benefits and Limitations
AI Agent Frameworks — Benefits and Limitations...
A Journey from AI to LLMs and MCP - 4 - What Are AI Agents — And Why They're the Future of LLM Applications
What Are AI Agents — And Why They're the Future of LLM Applications...
A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance — Fine-Tuning, Prompt Engineering, and RAG
Boosting LLM Performance — Fine-Tuning, Prompt Engineering, and RAG...
A Journey from AI to LLMs and MCP - 2 - How LLMs Work — Embeddings, Vectors, and Context Windows
How LLMs Work — Embeddings, Vectors, and Context Windows...
A Journey from AI to LLMs and MCP - 1 - What Is AI and How It Evolved Into LLMs
What Is AI and How It Evolved Into LLMs...
Building a Basic MCP Server with Python
The Basics of Building a Basic MCP Server...
Using Helm with Kubernetes - A Guide to Helm Charts and Their Implementation
A Guide on when to use Helm Charts for Kubernetes Deployment...
Crash Course on Developing AI Applications with LangChain
A guide on building AI applications with LangChain, a framework for developing AI applications powered by Large Language Models (LLMs)....
The Data Lakehouse - The Benefits and Enhancing Implementation
Understanding the value of a lakehouse and how to get that value faster...
2025 Comprehensive Guide to Apache Iceberg
What is Apache Iceberg, How it Works, and Why it Matters!...
When to use Apache Xtable or Delta Lake Uniform for Data Lakehouse Interoperability
A Guide on when to use Apache Xtable or Delta Lake Uniform for Data Lakehouse Interoperability...
RAG Isn’t a Modeling Problem. It’s a Data Engineering Problem.
Why retrieval-augmented generation systems fail in enterprises—and what to do about it....
Building Pangolin - My Holiday Break, an AI IDE, and a Lakehouse Catalog for the Curious
A personal story of how I built Pangolin Catalog over a holiday break using an AI-powered IDE....
2025 Guide to Architecting an Iceberg Lakehouse
A Comprehensive Guide to Building a Data Lakehouse with Apache Iceberg...
10 Future Apache Iceberg Developments to Look forward to in 2025
What is cool about Apache Iceberg's Future...
Deep Dive into Dremio's File-based Auto Ingestion into Apache Iceberg Tables
Auto ingesting data from JSON, CSV, and Parquet files into Apache Iceberg Tables...
Intro to SQL using Apache Iceberg and Dremio
Intro to SQL using Apache Iceberg and Dremio...
Dremio, Apache Iceberg and their role in AI-Ready Data
The Role of Dremio and Apache Iceberg in AI-Ready Data...
Introduction to Cargo and cargo.toml
Getting Started with Cargo and cargo.toml...
Leveraging Python's Pattern Matching and Comprehensions for Data Analytics
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Big List of Hosted Headless CMS Providers with Free or Developer Tier in 2021
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