Topic
Data Engineering
110 posts tagged “Data Engineering”.
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Apache Polaris 1.7.0 and the Quiet Work of Making a Catalog Trustworthy
A Spark job commits a table update. The catalog writes the change to Postgres. Then the network drops between the catalog and the client, and the…
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Designing Batch Pipelines That Write Well Into Apache Iceberg
The pipeline runs at 2 a.m. It reads yesterday's extract, does its transformations, and writes to an Apache Iceberg table. Six months later the same…
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Running an Apache Iceberg Lakehouse With No Internet Connection
An engineer runs pip install pyiceberg on a classified network and it fails. There is no PyPI. There is no Maven Central, no Docker Hub, no GitHub.…
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Why Iceberg V4 Wants to Retire Equality Deletes, and What Streaming Teams Should Do About It
A Flink job writes upserts into an Apache Iceberg table at a few thousand rows per second. The write side looks healthy. Checkpoints land on time,…
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Surviving Commit Conflicts When Dozens of Writers Hit the Same Iceberg Table
A compaction job runs for three hours, rewrites 4,000 files, and dies at the last step with CommitFailedException: Cannot commit changes based on…
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The Parquet Versioning Problem, and Why Iceberg Cares About It
A Spark job writes a table. A Trino query against the same table fails with a decoding error on one column. Nothing in the Iceberg metadata looks…
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The Apache Iceberg Market in the Middle of 2026
Two years ago the interesting question was whether your data platform supported Apache Iceberg. Today every platform claims it does, and the claim…
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Three Vendors Are Rebuilding the Path From Transaction to Agent
A customer changes their shipping address in your order system at 9:14 a.m. At 9:20 a.m. someone asks an AI agent where that order is going. The…
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Active Analytics Loops for AI Action Agents
Most analytics agents in production today answer questions. You type a request, the agent generates SQL, runs it, and hands back a chart or a…
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Hybrid Lakehouse Design for Regulated Markets
A bank, a hospital network, and a defense contractor walk into a cloud migration and all three stop at the same wall: their data is not allowed to…
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Iceberg Concurrency for AI Agent Writes
A single autonomous agent can attempt more table commits in an hour than a team of analysts produces in a week. Multiply that by a fleet of agents…
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REST Catalog V2: Fixing Iceberg Protocol Debt
A single BI dashboard refresh can trigger dozens of catalog calls before a single byte of table data is scanned. An AI agent investigating a revenue…
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The State of Apache Iceberg v4 in July 2026: What the Dev List Tells Us About the Format's Next Chapter
If you want to know where Apache Iceberg is headed, do not read the press releases. Read the dev mailing list. I say that as someone who reads it…
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The State of Agentic AI Standards in 2026: MCP, A2A, WebMCP, OSI, and the Protocol Stack Taking Shape
In 2023, an AI agent was a demo. In 2024, it was a framework. In 2025, it was a hundred incompatible frameworks. And in 2026, something genuinely new…
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The State of Apache Arrow in 2026: Ten Years In, the Invisible Standard Is Everywhere
In February 2026, Apache Arrow turned ten years old. The first commit landed on February 5th, 2016, and the anniversary passed the way Arrow itself…
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The State of Apache Parquet in 2026: The Quiet Format Enters Its Loudest Decade
Apache Parquet is thirteen years old, holds more of the world's analytical data than any format ever created, and for most of its life has been the…
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The State of Apache Polaris in July 2026: From Incubating Catalog to the Governance Layer of the Open Lakehouse
I have a personal stake in this one, so let me declare it up front. Apache Polaris was co-created by Snowflake and Dremio, I work at Dremio, and I…
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The State of Streaming to Apache Iceberg in July 2026: Every Path, Its Latency, and What to Do When Seconds Are Not Fast Enough
The most common architecture question I get in 2026 is no longer "should we use Iceberg." That one is settled. The question now is "how fresh can our…
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Deterministic Data Engineering With AI Harnesses: Using Claude Code, Codex, Antigravity, and OpenCode for Data Work You Can Actually Trust
There is an apparent contradiction at the heart of using AI agents for data work, and resolving it properly is worth an entire article, because the…
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Apache Iceberg v4 Roadmap: Adaptive Metadata Trees, Single-File Commits, and the Delta Convergence
Apache Iceberg v4 is not a single feature release. It is a set of architectural proposals: adaptive metadata trees, single-file commits, relative…
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Data Platform Native AI Agent Tooling in 2026
Every data platform vendor now offers some form of AI agent tooling. The approaches vary widely, from full agent authoring frameworks to MCP server…
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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.
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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 data management and enhance productivity for modern data teams.
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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 leverage its intuitive API to streamline your data engineering workflows.
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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
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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.
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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.
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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.
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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.
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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
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The Ultimate Guide to Open Table Formats - Iceberg, Delta Lake, Hudi, Paimon, and DuckLake
Understanding Iceberg, Delta Lake, Hudi, Paimon, and DuckLake
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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 architecture, open table formats like Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon, and the modern data ecosystem that supports them.
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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 tools for a self-healing lakehouse.
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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 and performance.
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Unlocking the Power of Agentic AI with Apache Iceberg and Dremio
Unlocking the Power of Agentic AI with Apache Iceberg and Dremio
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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 performance. Learn how to handle it effectively.
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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.
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How to Discover or Organize Lakehouse & Apache Iceberg Meetups
Guide on How to Be Part of the Lakehouse Community
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Introduction to Data Engineering Concepts | What is Data Engineering?
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Understanding Data Sources and Ingestion
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | ETL vs ELT – Understanding Data Pipelines
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Batch Processing Fundamentals
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Streaming Data Fundamentals
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Data Modeling Basics
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Data Warehousing Fundamentals
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Data Lakes Explained
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Storage Formats and Compression
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Data Quality and Validation
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Metadata, Lineage, and Governance
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Scheduling and Workflow Orchestration
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Building Scalable Pipelines
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | DevOps for Data Engineering
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Cloud Data Platforms and the Modern Stack
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Data Lakehouse Architecture Explained
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | Apache Iceberg, Arrow, and Polaris
Introduction to the terms in data engineering
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Introduction to Data Engineering Concepts | The Power of Dremio in the Modern Lakehouse
Introduction to the terms in data engineering
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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
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Dremio, Apache Iceberg and their role in AI-Ready Data
The Role of Dremio and Apache Iceberg in AI-Ready Data
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Hands-on with Apache Iceberg & Dremio on Your Laptop within 10 Minutes
How to get hands-on with Apache Iceberg
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Data Modeling - Entities and Events
How to Model Events and Entities
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All About Parquet Part 01 - An Introduction
All about the Apache Parquet File Format
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All About Parquet Part 02 - Parquet's Columnar Storage Model
All about the Apache Parquet File Format
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All About Parquet Part 03 - Parquet File Structure | Pages, Row Groups, and Columns
All about the Apache Parquet File Format
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All About Parquet Part 04 - Schema Evolution in Parquet
All about the Apache Parquet File Format
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All About Parquet Part 05 - Compression Techniques in Parquet
All about the Apache Parquet File Format
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All About Parquet Part 06 - Encoding in Parquet | Optimizing for Storage
All about the Apache Parquet File Format
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All About Parquet Part 07 - Metadata in Parquet | Improving Data Efficiency
All about the Apache Parquet File Format
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All About Parquet Part 08 - Reading and Writing Parquet Files in Python
All about the Apache Parquet File Format
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All About Parquet Part 09 - Parquet in Data Lake Architectures
All about the Apache Parquet File Format
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All About Parquet Part 10 - Performance Tuning and Best Practices with Parquet
All about the Apache Parquet File Format
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A Guide to dbt Macros - Purpose, Benefits, and Usage
Learning about dbt Macros
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Data Lakehouse Roundup 1 - News and Insights on the Lakehouse
What's Going on in the Data Lakehouse Space
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Getting Started with Data Analytics Using PyArrow in Python
Learning to work with PyArrow to run analytics
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What is Three-Tier Data (Bronze, Silver, Gold) and How Dremio Simplifies It
Process Data from Raw to Clean Aggregated Data
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A Brief Guide to the Governance of Apache Iceberg Tables
Controlling Access to your Apache Iceberg Tables
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Exploring Data Operations with PySpark, Pandas, DuckDB, Polars, and DataFusion in a Python Notebook
Learning to work with Python to ingest and query data
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Ultimate Directory of Apache Iceberg Resources
Apache Iceberg Education, Tutorials and more!
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Change Data Capture (CDC) when there is no CDC
Handling Synching Changing Data Across Systems
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Virtualization + Lakehouse + Mesh = Data At Scale
Combining Centralization and Decentralization for Data at Scale
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Hands-on with Apache Iceberg on Your Laptop - Deep Dive with Apache Spark, Nessie, Minio, Dremio, Polars and Seaborn
The Evolving Data Lakehouse World
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Why Data Analysts, Engineers, Architects and Scientists Should Care about Dremio and Apache Iceberg
The Evolving Data Lakehouse World
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5 Trends in the Data Lakehouse Space
The Evolving Data Lakehouse World
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Using the alexmerced/datanotebook Docker Image
Setting up a quick and easy data environment for data science and analytics
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Understanding Apache Iceberg Delete Files
Continuing the Understand Apache Iceberg series, this article delves into the Manifest, a critical component of Apache Iceberg's architecture.
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Understanding the Apache Iceberg Manifest
Continuing the Understand Apache Iceberg series, this article delves into the Manifest, a critical component of Apache Iceberg's architecture.
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Understanding the Apache Iceberg Manifest List (Snapshot)
Continuing the Understand Apache Iceberg series, this article delves into the Manifest List, a critical component of Apache Iceberg's architecture.
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Understanding Apache Iceberg's Metadata.json
The role and content of the metadata.json
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What Apache Iceberg REST Catalog is and isn't
Understanding Iceberg Catalog Interoperability
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ACID Guarantees and Apache Iceberg - Turning Any Storage into a Data Warehouse
What are ACID Guarantees? WHy do they matter?
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Data Lakehouse 101 - The Who, What and Why of Data Lakehouses
The Who, What and Why of Data Lakehouses
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Understanding the Polaris Iceberg Catalog and Its Architecture
Learn about the new open source Iceberg Catalog in Town
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Apache Iceberg Reliability
Why Apache Iceberg Works
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Upcoming Data Talks from Alex Merced (And how to follow)
Come see me talk live at these events
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Databases Deconstructed - The Value of Data Lakehouses and Table Formats
Building up the Data Lakehouse
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Video Course - Basics of Lakehouse Engineering - Apache Iceberg, Nessie, Dremio
Introductory Course to Data Engineering for Apache Iceberg Lakehouses
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What is the Data Lakehouse and the Role of Apache Iceberg, Nessie and Dremio?
Understanding the Value of the Data Lakehouse
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No Code - Convert XLS/CSV files into Parquet with Dremio
Convert XLS/CSV Files without having to write python
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An In-Depth Overview of Open Lakehouse Tech: Apache Iceberg & Nessie
Organizations are seeking innovative solutions to harness the full potential of their data while maintaining flexibility and avoiding vendor lock-in.…
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Overview of the Open Lakehouse: Why Dremio?
My cloud infrastructure bill has run wild My datasets have many derived copies for different use cases, which can be complex to maintain and keep…
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An Approach to Architecting a Lower Cost, Fast and Self-Service Data Lakehouse
There are several goals data architects are perpetually trying to improve upon: Speed: Data Analysts and scientists need data to derive insights to…
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Handling Cross-Origin Cookies with ExpressJS
Data is becoming the cornerstone of modern businesses. As businesses scale, so does their data, and this leads to the need for efficient data…
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Creating a Local Data Lakehouse using Spark/Minio/Dremio/Nessie
Data is becoming the cornerstone of modern businesses. As businesses scale, so does their data, and this leads to the need for efficient data…
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Project Nessie: A Look in the Depths
Once upon a time, in the mystical realm of data lakes, there was a growing problem. The inhabitants of this realm, data scientists, and engineers,…
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Overview of File Encryption Algorithms for Everyone
Welcome to the thrilling world of file encryption! In this blog post, we'll unravel the secrets of file encryption algorithms and why they are the…
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Parquet File Compression for Everyone (zstd, brotli, lz4, gzip, snappy)
You know how when you're packing for a trip, you try to stuff as many clothes as you can into your suitcase without breaking the zipper? That's kind…
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Dremio and Modern Data Architecture: Data Lakes, Data Lakehouses and Data Mesh
Today it can seem like a buzzword onslaught in the data space with terms like Data Mesh, Data Lakehouse, and many more being thrown out with every…
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What is Nessie and Why as a Data Engineer or Architect you should care?
We need to establish a few things to understand why the open-source data catalog, Project Nessie, matters so much. The amount of data and use cases…
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Resources for Learning more about Catalog level versioning with Project Nessie & Dremio Arctic (Rollbacks, Branching, Tagging and Multi-Table Txns)
Data Quality, Governance, Observability, and Disaster Recovery are issues that are still trying to discover best practices in the world of the data…
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5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
How your data lakehouse can expand what's possible with Dremio Cloud.
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Brief Hands on Intro to Apache Iceberg
Engineer a Data Lakehouse with Apache Iceberg