# Alex Merced's Lakehouse Blog

> Alex Merced's Lakehouse Blog (iceberglakehouse.com) is Alex Merced's independent blog on the open lakehouse. It has 474 articles on Apache Iceberg internals, lakehouse catalogs such as Apache Polaris and the Iceberg REST Catalog, table maintenance and performance, query engines, and the agentic lakehouse, where AI agents query governed data through semantic layers and open protocols such as MCP. Alex is Head of Developer Relations at Dremio and a co-author of Apache Iceberg: The Definitive Guide. The pillar pages below are the best starting points; the posts are listed newest first within each topic.

## Pillar Pages

- [Agentic Analytics and the Semantic Layer: Natural Language to SQL on Iceberg](https://iceberglakehouse.com/agentic-analytics/): Agentic analytics lets AI agents answer business questions by generating and executing SQL against governed lakehouse data. This guide explains how NL2SQL works, what role the semantic layer plays in accuracy, and how to build a production natural language analytics system on Apache Iceberg.
- [What Is an Agentic Lakehouse | Architecture, Governance, and AI Agents](https://iceberglakehouse.com/agentic-lakehouse/): An agentic lakehouse is an architecture where AI agents can safely query, reason over, and act on governed lakehouse data. Learn what the four required layers are, how governance and trust work, and why open standards matter for AI-ready data infrastructure.
- [Apache Iceberg Architecture: Metadata Tree, Snapshots, and Catalogs Explained](https://iceberglakehouse.com/apache-iceberg-architecture/): A technical deep dive into how Apache Iceberg works internally — the metadata JSON, manifest list, manifest files, data files, commit flow, query planning, and concurrency model.
- [Iceberg REST Catalog and Apache Polaris: Multi-Engine Catalog Guide](https://iceberglakehouse.com/apache-iceberg-rest-catalog/): The Iceberg REST Catalog specification is an open HTTP API that lets any engine connect to any catalog. This guide explains how the spec works, what Apache Polaris provides, how credential vending works, and how to connect Spark, Trino, and PyIceberg to a REST catalog.
- [Apache Iceberg Schema Evolution and Hidden Partitioning Explained](https://iceberglakehouse.com/apache-iceberg-schema-evolution/): Explore the inner workings of Apache Iceberg schema evolution, unique column ID tracking, hidden partitioning transforms, partition evolution, and Dremio performance optimizations.
- [Apache Iceberg Snapshots and Time Travel: How Table History Works](https://iceberglakehouse.com/apache-iceberg-snapshots-and-time-travel/): Every Apache Iceberg commit creates an immutable snapshot. This guide explains the snapshot model, how time travel queries work, how to use table branches and tags, and how to manage snapshot retention.
- [Apache Iceberg vs Delta Lake vs Apache Hudi: Complete Comparison](https://iceberglakehouse.com/apache-iceberg-vs-delta-lake-vs-hudi/): A neutral, technical comparison of Apache Iceberg, Delta Lake, and Apache Hudi covering architecture, multi-engine support, streaming, governance, and a decision framework by workload type.
- [Apache Iceberg Explained: Architecture, Snapshots, and Catalogs](https://iceberglakehouse.com/apache-iceberg/): Apache Iceberg is an open table format for huge analytic tables. Learn how it works internally, from the metadata tree and snapshot model to hidden partitioning, schema evolution, and the catalog API that makes multi-engine access possible.
- [Open Table Formats Benchmark: Iceberg vs Delta Lake vs Hudi](https://iceberglakehouse.com/benchmarks/open-table-formats/): An exhaustive, production-grade performance benchmark of open table formats. Analyze query scan speed, write throughput, maintenance overhead, and engine selection caveats across Apache Iceberg, Delta Lake, and Apache Hudi.
- [Books by Alex Merced](https://iceberglakehouse.com/books/): Books on Apache Iceberg, lakehouse architecture, catalogs, and agentic analytics, written by Alex Merced.
- [Data Lakehouse vs Data Lake vs Data Warehouse: Key Differences](https://iceberglakehouse.com/data-lakehouse-vs-data-lake-vs-data-warehouse/): A practical comparison of the three main data storage architectures — data lake, data warehouse, and data lakehouse — covering storage model, query performance, governance, cost, AI/ML support, and when each is the right choice.
- [What Is a Data Lakehouse | Apache Iceberg & Lakehouse Guide](https://iceberglakehouse.com/data-lakehouse/): A data lakehouse combines the low-cost storage of a data lake with the reliability and query performance of a data warehouse. Learn how the architecture works, what layers it includes, and when it makes sense to build one.
- [Lakehouse for AI Agents: Architecture and Implementation Guide](https://iceberglakehouse.com/lakehouse-for-ai-agents/): AI agents need governed, documented, queryable data to work reliably. This guide explains how to build a data architecture that serves AI agents through structured interfaces, covering the data layer, catalog, semantic layer, and agent connection patterns.
- [Open Table Formats Explained: Iceberg, Delta Lake, Hudi, and Paimon](https://iceberglakehouse.com/open-table-formats/): Open table formats add ACID transactions, schema evolution, and query optimization to raw object storage. This guide explains what they are, how they work, and how Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon each approach the problem.

The 101 newest articles are listed below. Every article is listed in [llms-full.txt](https://iceberglakehouse.com/llms-full.txt).

## Articles: Apache Iceberg and Table Formats

- [Keeping Audit Snapshots Alive While Iceberg Snapshot Expiration Runs Every Night](https://iceberglakehouse.com/posts/iceberg-tags-regulatory-snapshot-retention/) (2026-09-28): How Iceberg snapshot tags keep audit snapshots alive through nightly expiration: retention calendars, RETAIN semantics, compaction cost, and erasure conflicts.
- [Why the Iceberg DataFusion Integration Is Moving to Apache DataFusion](https://iceberglakehouse.com/posts/datafusion-iceberg-moves-to-apache-datafusion/) (2026-09-21): Why the Iceberg DataFusion integration moved to the DataFusion project, and what the split means for users, Comet, and iceberg-rust contributors.
- [What Iceberg v4's Proposed FILE Type Means for Multimodal Tables](https://iceberglakehouse.com/posts/iceberg-v4-file-type-multimodal-tables/) (2026-09-21): Iceberg v4's proposed FILE type brings first-class media references to tables, via Parquet's FILE logical type, ranges, checksums, and pre-signed URLs.
- [CVE-2026-73334 and the Trust Boundary Inside an Encrypted Parquet File](https://iceberglakehouse.com/posts/parquet-kms-url-cve-2026-73334-iceberg/) (2026-09-21): CVE-2026-73334 lets a tampered Parquet footer route a reader's KMS token to an attacker. Here's the fix, Iceberg's safe path, and how to audit your lakehouse.
- [Parquet Page Indexes and the Last Mile of Pruning in Apache Iceberg](https://iceberglakehouse.com/posts/parquet-page-index-pruning-iceberg/) (2026-09-21): Parquet page indexes can cut selective Iceberg scans by an order of magnitude on sorted data. How they work, what they cost, and how to lay out tables.
- [How Apache Polaris Plans to Share Iceberg Tables Across Organizations](https://iceberglakehouse.com/posts/polaris-open-sharing-iceberg-tables/) (2026-09-21): Apache Polaris's Open Sharing proposal adds first-class shares, external consumers, and listings so any Iceberg REST engine can read shared tables.
- [Keeping Lakehouse Traffic Off the Public Internet With Apache Polaris](https://iceberglakehouse.com/posts/polaris-private-networking-lakehouse/) (2026-09-21): A private Polaris lakehouse still leaks data if the storage hop goes public. How to bind vended credentials to private networks on AWS, Azure, and GCP.
- [Vector Search Directly Over Iceberg Tables, and When You Still Need a Vector Database](https://iceberglakehouse.com/posts/vector-search-over-iceberg-tables/) (2026-09-21): Embeddings are just columns. When exact vector search over Iceberg scans beats a vector database, when it does not, and how to lay out tables.
- [Running Apache Polaris in Production](https://iceberglakehouse.com/posts/apache-polaris-in-production/) (2026-09-10): Apache Polaris past the quickstart: persistence backends, realm bootstrap, replica token signing, upgrades, backups, and which failures take the lakehouse offline.
- [Migrating Into Iceberg Without Moving Data](https://iceberglakehouse.com/posts/iceberg-in-place-migration/) (2026-09-10): add_files, snapshot, and migrate compared: the three in-place paths into Iceberg, the reconciliation each requires, the layout traps, and the rollback story.
- [What Iceberg Table Maintenance Actually Costs](https://iceberglakehouse.com/posts/iceberg-maintenance-budget/) (2026-09-10): A cost model for compaction, snapshot expiry, orphan cleanup, and manifest rewriting: what each operation spends, on which meter, and how to set a schedule.
- [Partition Statistics Files in Apache Iceberg](https://iceberglakehouse.com/posts/iceberg-partition-statistics-files/) (2026-09-10): The underused Iceberg metadata for planning: what the partition statistics file holds, what the spec guarantees, how to write one, and when it earns its slot.
- [The 2026 Iceberg REST Catalog Compatibility Report](https://iceberglakehouse.com/posts/iceberg-rest-catalog-compatibility-report-2026/) (2026-09-10): A repeatable test for what an Iceberg REST catalog actually serves, a scoring scheme that separates design from breakage, and the 2026 evidence across seven catalogs.
- [Serving Iceberg Tables From Two Regions](https://iceberglakehouse.com/posts/iceberg-two-region-serving/) (2026-09-10): Three multi-region topologies that work and one that mostly does not, what an Iceberg commit costs across regions, and where the catalog has to live.
- [Disaster Recovery for Iceberg Tables: Replication, Backup, and Restore](https://iceberglakehouse.com/posts/disaster-recovery-for-iceberg-tables/) (2026-09-02): Disaster recovery for Iceberg across four tiers: snapshots, object versioning, catalog backup, and cross-region replication.
- [Geospatial Data in Apache Iceberg: Geometry, Geography, and GeoParquet](https://iceberglakehouse.com/posts/geospatial-data-in-apache-iceberg/) (2026-09-02): How Iceberg v3 geometry and geography types, bounding boxes, and native Parquet types give spatial data first-class standing.
- [Default Column Values and Field IDs: How Iceberg Schema Evolution Works at the Spec Level](https://iceberglakehouse.com/posts/iceberg-default-values-and-field-ids/) (2026-09-02): How field IDs and initial and write defaults let Iceberg change schemas on large tables without rewriting data, at the spec level.
- [The Iceberg Table Properties That Actually Matter](https://iceberglakehouse.com/posts/iceberg-table-properties-that-matter/) (2026-09-02): The Iceberg table properties that decide file count, pruning, write amplification, retention, and metadata growth, by workload.
- [Inside the Puffin File Format](https://iceberglakehouse.com/posts/inside-the-puffin-file-format/) (2026-09-02): The Puffin file format inside out, byte by byte, covering Theta sketches for distinct values and deletion vectors.
- [Local Iceberg Development Environments: Docker, MinIO, and In-Memory Catalogs for CI](https://iceberglakehouse.com/posts/local-iceberg-development-environments/) (2026-09-02): Local Iceberg development environments: in-process catalogs, a Docker Compose stack with MinIO, and CI configurations that run either.
- [Moving Iceberg Tables Between Catalogs Without Rewriting Data](https://iceberglakehouse.com/posts/moving-iceberg-tables-between-catalogs/) (2026-09-02): Why moving Iceberg tables between catalogs is a pointer copy, and the protocol that makes a cutover safe for one table or thousands.
- [Storage-Partitioned Joins and the Bucket Transform](https://iceberglakehouse.com/posts/storage-partitioned-joins-and-the-bucket-transform/) (2026-09-02): How the spec-defined bucket transform lets engines skip the shuffle in joins, and how to set it up and keep it engaged in Spark.
- [Agent-Driven Storage Tiering for Apache Iceberg: Moving Cold Data Without Breaking Queries](https://iceberglakehouse.com/posts/agent-driven-iceberg-storage-tiering/) (2026-08-25): A background agent can move cold Iceberg partitions to cheaper tiers without breaking live queries. Heatmaps, path-safe moves, and restore paths.
- [DataFusion Comet 1.0 and What Native Rust Scans Change for Spark on Iceberg](https://iceberglakehouse.com/posts/datafusion-comet-1-spark-iceberg/) (2026-08-25): DataFusion Comet 1.0 replaces Spark Iceberg scans with native Rust. What speeds up, what still falls back to the JVM, and how to deploy it.
- [FSST and ALP: The Two Encodings Fixing Parquet's Weakest Compression Cases](https://iceberglakehouse.com/posts/fsst-alp-parquet-encodings/) (2026-08-25): ALP and FSST target Parquet's worst cases: floats and high-cardinality strings. How they work and what they change for Iceberg tables.
- [High-Throughput Branch Merging: Automating Concurrency and Conflict Resolution in Multi-Branch Iceberg Pipelines](https://iceberglakehouse.com/posts/multi-branch-iceberg-merge-automation/) (2026-08-25): High-throughput Iceberg branch merges need conflict detection and automation. How to reconcile concurrent writes without stalling pipelines.
- [Multi-Cloud REST Catalog Topologies: Running Apache Polaris Across AWS, Azure, and GCP](https://iceberglakehouse.com/posts/multi-cloud-polaris-rest-catalog-topologies/) (2026-08-25): Polaris can catalog Iceberg tables across AWS, Azure, and GCP. Four topologies, credential vending, and the tradeoffs of each design.
- [Parquet-Only Manifests in Iceberg v4: Why the Metadata Layer Is Going Columnar](https://iceberglakehouse.com/posts/parquet-manifests-iceberg-v4/) (2026-08-25): Iceberg v4 is moving manifests from Avro to Parquet so planners can read only the stats they need. Why the metadata layer is going columnar.
- [Serverless Iceberg Ingestion with PyIceberg and DuckDB: Micro-Batches Without a Spark Cluster](https://iceberglakehouse.com/posts/serverless-iceberg-microbatch-pyiceberg-duckdb/) (2026-08-25): Land small Iceberg micro-batches with PyIceberg and DuckDB in a serverless function. Commits, concurrency, and why Spark is the wrong default.
- [Zero-Copy Warehouse Modernization: Migrating Legacy Databases to Apache Iceberg Without Downtime](https://iceberglakehouse.com/posts/zero-copy-legacy-db-to-iceberg/) (2026-08-25): Move a legacy warehouse to Iceberg without downtime by virtualizing first. Consumer cutover, parity checks, and background copy without double-ETL.
- [The Hidden Cost of Tiny Iceberg Commits](https://iceberglakehouse.com/posts/hidden-cost-of-tiny-iceberg-commits/) (2026-08-24): Trace what one tiny Iceberg commit writes, then model hourly, per-minute, and per-second cadences so streaming costs become arithmetic, not adjectives.
- [Deletion Vectors vs Position Deletes vs Equality Deletes: The Iceberg Delete Story in 2026](https://iceberglakehouse.com/posts/iceberg-delete-story-2026/) (2026-08-24): Position deletes, equality deletes, and deletion vectors compared from the Iceberg spec: what each writes, how readers apply it, and when to use which.
- [Iceberg Is Becoming a Library, Not Just a Table Format](https://iceberglakehouse.com/posts/iceberg-is-becoming-a-library/) (2026-08-24): Iceberg is turning from a JVM table format into a library other systems embed. What that shift changes for engines, catalogs, and the spec itself.
- [Iceberg Is Escaping the JVM: Why Rust, Go, Python and C++ Implementations Matter](https://iceberglakehouse.com/posts/iceberg-is-escaping-the-jvm/) (2026-08-24): Rust, Go, Python, and C++ Iceberg implementations change who can write the format. Why multi-language clients matter more than another JVM engine.
- [The Iceberg REST Catalog Compatibility Test: One Suite of Operations Every Platform Should Pass](https://iceberglakehouse.com/posts/iceberg-rest-catalog-compatibility-test/) (2026-08-24): One suite of REST catalog operations every Iceberg platform should pass. What sameness means, where implementations diverge, and how to test it.
- [Iceberg REST Remote Scan Planning Changes More Than Query Performance](https://iceberglakehouse.com/posts/iceberg-rest-remote-scan-planning/) (2026-08-24): Remote scan planning moves Iceberg file selection into the catalog. What that changes for engines, governance, and operational cost beyond query speed.
- [Iceberg Row Lineage: The Feature AI and CDC Workloads Will Eventually Depend On](https://iceberglakehouse.com/posts/iceberg-row-lineage/) (2026-08-24): Iceberg row lineage gives rows a durable identity across rewrites. Why CDC pipelines and AI workloads will eventually depend on it.
- [Iceberg v4's Adaptive Metadata Tree, Explained From First Principles](https://iceberglakehouse.com/posts/iceberg-v4-adaptive-metadata-tree/) (2026-08-24): Iceberg v4's adaptive metadata tree, explained from first principles: why commits rewrite too much today and how the tree makes change cheaper.
- [Why Iceberg v4 Is Really About Making the Cost of Change Proportional to the Change](https://iceberglakehouse.com/posts/iceberg-v4-cost-of-change-principle/) (2026-08-24): Iceberg v4 is really about making the cost of a change proportional to the change. The principle, the current tax, and what the redesign pays down.
- [The Catalog Can Now Plan Your Iceberg Query: Inside REST Scan Planning](https://iceberglakehouse.com/posts/inside-iceberg-rest-scan-planning/) (2026-08-24): A mechanics walkthrough of Iceberg REST scan planning: client-side planning, remote endpoints, pagination, and where engine support stands in 2026.
- [Can Seven Different Iceberg REST Catalogs Really Run the Same DuckDB Code?](https://iceberglakehouse.com/posts/seven-rest-catalogs-one-duckdb-script/) (2026-08-24): Can seven Iceberg REST catalogs run the same DuckDB script? What the protocol makes portable, what still differs, and a test matrix you can rerun.
- [Stop Flattening Your JSON: How Iceberg Variant Changes Semi-Structured Analytics](https://iceberglakehouse.com/posts/stop-flattening-your-json-iceberg-variant/) (2026-08-24): Iceberg Variant stores JSON as navigable binary with shredding for columnar filters. Why flattening wide tables is no longer the only performance path.
- [What Actually Happens When Two Engines Write the Same Iceberg Table at Once?](https://iceberglakehouse.com/posts/two-engines-one-iceberg-table/) (2026-08-24): What happens when two engines write the same Iceberg table at once: snapshot isolation, optimistic commits, conflict detection, and when retries fail.
- [Variant Shredding Explained: How Iceberg Gets Columnar Performance From Messy JSON](https://iceberglakehouse.com/posts/variant-shredding-explained/) (2026-08-24): Variant shredding turns messy JSON into Parquet columns with statistics. How the layout works, how readers reassemble values, and why some queries prune.
- [Who Actually Owns an Iceberg Table? Managed, External, and the New Vocabulary of Lakehouse Control](https://iceberglakehouse.com/posts/who-owns-an-iceberg-table/) (2026-08-24): Managed and external Iceberg tables mean different things on every platform. Five ownership dimensions and a translation method for vendor vocabulary.
- [Mastering Apache Iceberg v3 Deletion Vectors for High-Throughput Streaming Ingest](https://iceberglakehouse.com/posts/apache-iceberg-v3-deletion-vectors-streaming-ingest/) (2026-08-19): Apache Iceberg v3 deletion vectors for high-throughput streaming ingest: how bitmaps and Puffin files fix CDC write amplification and read decay.
- [The Decoupled Data Lakehouse: Multi-Engine Freedom with Open REST Catalogs](https://iceberglakehouse.com/posts/decoupled-data-lakehouse-multi-engine-rest-catalogs/) (2026-08-19): The decoupled data lakehouse: multi-engine freedom with open REST catalogs, credential vending, and an estate that outlives its tools.
- [Building Lightweight Serverless Ingestion to Apache Iceberg with PyIceberg and DuckDB](https://iceberglakehouse.com/posts/serverless-iceberg-ingestion-pyiceberg-duckdb/) (2026-08-19): Build lightweight serverless ingestion to Apache Iceberg with PyIceberg and DuckDB, running small feeds in functions that bill for seconds.
- [Write-Audit-Publish with Apache Iceberg Branches: CI/CD for Your Data](https://iceberglakehouse.com/posts/write-audit-publish-iceberg-branches/) (2026-08-19): Write-Audit-Publish with Apache Iceberg branches brings CI/CD to your data: stage, audit, then fast-forward to main so consumers never see unvalidated bytes.
- [Zero-Copy Warehouse Modernization: Moving to Apache Iceberg Without Downtime](https://iceberglakehouse.com/posts/zero-copy-warehouse-modernization-iceberg/) (2026-08-19): A practical guide to modernizing a data warehouse to Apache Iceberg without downtime, using federation first, then redirecting new data, then.
- [Autonomous Table Optimization When Your Query Workload Stops Being Predictable](https://iceberglakehouse.com/posts/autonomous-table-optimization/) (2026-08-04): Autonomous table optimization when query workloads stop being predictable: observing file layout and query patterns, scoring compaction work, adaptive.

## Articles: Data Lakehouse Architecture

- [The Open Lakehouse Explained, Then Built on Your Laptop with Dremio and MinIO](https://iceberglakehouse.com/posts/open-lakehouse-on-your-laptop/) (2026-09-10): The five layers of the open lakehouse explained, then a lab: Parquet, Iceberg, Polaris, Arrow, and Ossie running in two containers on your own machine.
- [Postgres Meets the Lakehouse: pg_lake, pg_duckdb, and When Postgres Is Enough](https://iceberglakehouse.com/posts/postgres-meets-the-lakehouse/) (2026-09-02): What pg_lake, pg_duckdb, and pg_mooncake do at the Iceberg level, and honest thresholds for when Postgres is enough.
- [Building Apache Iceberg Lakehouses That Run Without an Internet Connection](https://iceberglakehouse.com/posts/building-air-gapped-iceberg-lakehouse/) (2026-08-04): How to build an Apache Iceberg lakehouse that runs fully offline: storage, catalog, compute, cross-zone transfer, compliance, and the failure modes that bite.

## Articles: Agentic Analytics and Semantic Layers

- [How Apache Ossie Is Deciding What Agents and BI Tools Can Ask a Semantic Layer](https://iceberglakehouse.com/posts/ossie-semantic-query-interface-agents/) (2026-09-21): How Apache Ossie's layered query design gives AI agents both a constrained dimensional interface and a grain-safe SQL interface for semantic layers.
- [Apache Ossie and Apache Polaris: Putting Semantic Models in the Open Catalog](https://iceberglakehouse.com/posts/apache-ossie-polaris-semantic-models/) (2026-08-25): Apache Ossie and Polaris put metric definitions in the open catalog. What the spec covers, what Polaris stores, and what is still unfinished.
- [Semantic Layer Federation: One Logical Model Over Data on Three Clouds](https://iceberglakehouse.com/posts/semantic-layer-federation-three-clouds/) (2026-08-25): One logical model over Iceberg and databases on three clouds. Pushdown, egress, Reflections, and where semantic federation still breaks.

## Articles: AI and Agents

- [Turning an Analytics Question Into a Verified Agentic Graph](https://iceberglakehouse.com/posts/ags-multi-agent-analytics-workflow/) (2026-09-28): A complete AGS 1.0 graph for governed metric questions, with verification gates, deterministic check scripts, and reconciliation against a semantic layer.
- [Twelve Queries That Pass Every Schema Check and Still Return the Wrong Number](https://iceberglakehouse.com/posts/semantic-failure-catalog-ai-agent-queries/) (2026-09-28): Twelve query patterns that pass schema validation and still return wrong numbers, with detectors, required metadata, and tested code for each.
- [Fast Classification Models, LLMs, and the Apache Iceberg Lakehouse](https://iceberglakehouse.com/posts/jev-classification-models-iceberg-lakehouse/) (2026-09-21): How fast classification models like Jev alongside open alternatives such as GLiClass compare with LLMs, and how to run both together inside an Apache Iceberg lakehouse.
- [Agentic Data Architecture](https://iceberglakehouse.com/posts/agentic-data-architecture/) (2026-09-10): A six-layer reference architecture for agents on company data: planners, tool boundaries, identity, the semantic layer, and what breaks when a layer is missing.
- [Context Engineering for Data Agents](https://iceberglakehouse.com/posts/context-engineering-for-data-agents/) (2026-09-10): Why text-to-SQL accuracy collapses on enterprise schemas, the five kinds of context an agent needs, where each one hides, and how to make the semantics legible.
- [The Data Team of the Agentic Era: Generalists Owning End-to-End Workflows](https://iceberglakehouse.com/posts/data-team-of-the-agentic-era/) (2026-09-02): The case for generalists owning end-to-end data workflows with agents, the counterargument, and how to make the transition work.
- [Will AI Replace Data Engineers?](https://iceberglakehouse.com/posts/will-ai-replace-data-engineers/) (2026-09-02): What the evidence shows about whether AI replaces data engineers, which parts of the job compress, and which parts do not.
- [Securing the Agentic Lakehouse Gateway: Preventing Prompt Injection and Data Exfiltration](https://iceberglakehouse.com/posts/agentic-lakehouse-gateway-prompt-injection/) (2026-08-25): Agentic lakehouse gateways face prompt injection and exfiltration through query results. A threat model and defenses for the layer in front of data.
- [Metric Contracts in Code: Testing, Versioning, and Serving Business Logic to Multi-Agent Systems](https://iceberglakehouse.com/posts/metric-contracts-code-multi-agent/) (2026-08-25): Metric contracts in code let teams test, version, and serve business logic to multi-agent systems without each agent inventing its own SQL.
- [The Agent Is Now a Named Coworker, and It Needs a File Format](https://iceberglakehouse.com/posts/agents-with-personalities/) (2026-08-24): Named, persistent agents need a file format. Open Agent Profile, Buzz, Grok Bot, and Hermes Bot Mode show why a portable agent identity matters.
- [Your Agent Should Answer the Phone: A Field Guide to AI Gateways on Slack, Discord, Telegram, Signal, and Teams](https://iceberglakehouse.com/posts/ai-gateways-field-guide/) (2026-08-24): A field guide to AI gateways on Slack, Discord, Telegram, Signal, and Teams: architecture, auth, cost, and the failure modes that matter.
- [Graphs in AI Engineering Have Solved Three Problems. The Fourth Is the Plan.](https://iceberglakehouse.com/posts/graphs-in-ai-engineering/) (2026-08-24): Knowledge graphs, GraphRAG, and LangGraph solved three problems. The fourth is the work itself: a reviewable graph of bounded agentic loops.
- [The Five Layers of an Agentic Lakehouse](https://iceberglakehouse.com/posts/five-layers-agentic-lakehouse/) (2026-08-19): The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
- [Goal-Directed Data Quality Agents: Anomaly Quarantine on Apache Iceberg](https://iceberglakehouse.com/posts/goal-directed-data-quality-agents-iceberg/) (2026-08-19): Goal-directed data quality agents that watch Apache Iceberg tables, detect anomalies, and quarantine suspect data safely with snapshot isolation and branches.
- [Managing the TCO of Agentic Analytics: Token Budgets, Query Throttles, and the Economics of Autonomy](https://iceberglakehouse.com/posts/managing-tco-agentic-analytics/) (2026-08-19): Managing the total cost of agentic analytics: token budgets, query throttles, unit economics, and the FinOps discipline that keeps AI spend under control.
- [Metric Contracts in 2026: Standardizing Business Logic Across Multi-Agent Frameworks](https://iceberglakehouse.com/posts/metric-contracts-2026/) (2026-08-19): Metric contracts in 2026: versioned, testable definitions of business logic that let multi-agent frameworks compute revenue identically, with OSI interchange.
- [Query Routing at Machine Scale: Multi-Engine Workload Distribution for the Agentic Lakehouse](https://iceberglakehouse.com/posts/query-routing-machine-scale/) (2026-08-19): Query routing at machine scale for the agentic lakehouse: engine selection, acceleration substitution, admission control, and placement.
- [Building Stateless AI Tool Gateways with FastMCP, the 2026 MCP Spec, and Kubernetes](https://iceberglakehouse.com/posts/stateless-mcp-tool-gateways-fastmcp/) (2026-08-19): How to build stateless AI tool gateways with FastMCP, the 2026 MCP specification, and Kubernetes, and why statelessness finally makes MCP scale.
- [The Plan and the Worker: Two Open Specifications for Agent Harnesses](https://iceberglakehouse.com/posts/agentic-graph-open-agent-profile-two-open-specs-agent-harnesses/) (2026-08-10): Two open specifications, the Agentic Graph Specification and the Open Agent Profile, turn agent plans and agent identity into portable, reviewable files.
- [Budgeting for Agentic Analytics When Every Question Costs Something Different](https://iceberglakehouse.com/posts/agentic-analytics-tco-token-budgets/) (2026-08-04): Budgeting for agentic analytics when every question costs something different: token economics, query economics, instrumentation, and the cost controls.
- [The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits](https://iceberglakehouse.com/posts/agentic-lakehouse-mcp-architecture/) (2026-08-04): The five layers of an agentic lakehouse and where the MCP server sits: storage, catalog, semantic layer, MCP gateway, and agent surface, plus identity.

## Articles: Data Engineering

- [Running an Iceberg Lakehouse on Kubernetes](https://iceberglakehouse.com/posts/iceberg-on-kubernetes/) (2026-09-10): Catalog, maintenance, and compaction as Kubernetes workloads: scheduling classes, job structure, credential flow, and the failures that come from the interaction.
- [What to Assert When You Test an Iceberg Pipeline](https://iceberglakehouse.com/posts/iceberg-pipeline-assertions/) (2026-09-10): Fixtures, in-memory catalogs, and golden metadata: the assertions that catch wrong rows, unsafe reruns, schema drift, and concurrent-write corruption in CI.
- [Kafka Connect to Iceberg: How the Commit Actually Works](https://iceberglakehouse.com/posts/kafka-connect-iceberg-commits/) (2026-09-10): Exactly-once semantics in the Iceberg sink connector: the coordinator, the control topic, offsets stored inside Iceberg snapshots, and where duplicates still get in.
- [What a Query Costs](https://iceberglakehouse.com/posts/lakehouse-unit-economics/) (2026-09-10): Four meters, their real proportions, and how to attribute compute to a query, a table, and a team so a platform can answer what a dashboard costs to run.
- [Data Quality Tooling Compared: Great Expectations, Soda, dbt Tests, and Anomaly Detection](https://iceberglakehouse.com/posts/data-quality-tooling-compared/) (2026-09-02): A comparison of Great Expectations, Soda, dbt tests, and anomaly detection, and a layered design that uses each where it fits.
- [dbt on Iceberg: Incremental Models on Open Tables](https://iceberglakehouse.com/posts/dbt-on-iceberg-incremental-models/) (2026-09-02): How dbt incremental materializations map to Iceberg operations, and the configuration, predicates, and maintenance that keep them healthy.
- [The Lakehouse Ingestion Tool Landscape: Fivetran, Airbyte, dlt, and CDC vs Batch](https://iceberglakehouse.com/posts/lakehouse-ingestion-tools/) (2026-09-02): How Fivetran, Airbyte, dlt, and CDC and streaming tools land well-behaved Apache Iceberg tables, and how to choose and maintain them.
- [Logs, Traces, and Metrics as Tables: Building an OpenTelemetry Data Lake on Iceberg](https://iceberglakehouse.com/posts/opentelemetry-data-lake-on-iceberg/) (2026-09-02): Building an OpenTelemetry data lake on Iceberg: schemas for spans, logs, and metrics, ingestion, query patterns, and retention.
- [Orchestration in 2026: Airflow 3 vs Dagster vs Prefect vs Event-Driven](https://iceberglakehouse.com/posts/orchestration-in-2026/) (2026-09-02): Where Airflow 3, Dagster, Prefect, and event-driven triggering stand for lakehouse pipelines in 2026, after the Prefect acquisition of Dagster.
- [Schema Registries and Event Schemas: Avro, Protobuf, and JSON Schema on the Way Into the Lakehouse](https://iceberglakehouse.com/posts/schema-registries-and-event-schemas/) (2026-09-02): How Avro, Protobuf, and JSON Schema evolve through a registry, and how that maps to the schema evolution rules of Iceberg.
- [Synthetic Data in the Lakehouse: Generation, Governance, and Testing](https://iceberglakehouse.com/posts/synthetic-data-in-the-lakehouse/) (2026-09-02): What synthetic data in a lakehouse is for, the generation methods, how to preserve fidelity, and where synthetic tables belong.
- [Arrow Flight SQL and ADBC: Why the Database Driver Is the Slowest Part of Your Query](https://iceberglakehouse.com/posts/arrow-flight-sql-adbc-connectivity/) (2026-08-25): JDBC and ODBC often dominate large-result time. Flight SQL and ADBC keep data columnar from server to client, with Python, Go, and Rust examples.
- [Query Routing at Machine Scale: Dynamic Workload Distribution Across Lakehouse Engines](https://iceberglakehouse.com/posts/query-routing-lakehouse-engines/) (2026-08-25): Route each lakehouse query by shape, not by sender. Signals, rules, and how to keep dashboards, batch jobs, and agents from sharing one engine.
- [Semantic Layer Federation: One Meaning for Data That Lives Everywhere](https://iceberglakehouse.com/posts/semantic-layer-federation-multi-cloud/) (2026-08-19): Build a federated semantic layer across multi-cloud data so one set of governed metric definitions serves BI tools, dashboards, and AI agents identically.
- [Apache Arrow Flight and ADBC, and Why Database Connectivity Finally Went Columnar](https://iceberglakehouse.com/posts/arrow-flight-adbc-explained/) (2026-08-06): Arrow Flight and ADBC move database results as columnar data, ending the row-oriented bottleneck between engines and applications. Here's how.

## Articles: Governance and Security

- [Guardrails for AI on Company Data](https://iceberglakehouse.com/posts/guardrails-ai-company-data/) (2026-09-10): The control surfaces that actually contain damage once an agent is fooled: identity, permissions, audit trails, and prompt injection at the query layer.
- [How Iceberg Catalogs Hand Engines Storage Access](https://iceberglakehouse.com/posts/iceberg-vended-credentials/) (2026-09-10): Credential vending end to end: the wire protocol, scoped access on each cloud, remote signing, credential lifetime on long jobs, and failures that look like bugs.
- [Deleting User Data From an Immutable Lakehouse: GDPR Hard Deletes on Iceberg](https://iceberglakehouse.com/posts/gdpr-hard-deletes-on-iceberg/) (2026-09-02): How to turn a logical delete on immutable Iceberg into a physical erasure across snapshots, versions, replicas, and downstream copies.
- [Metadata Platforms in 2026: DataHub, OpenMetadata, Atlan, and Catalog Convergence](https://iceberglakehouse.com/posts/metadata-platforms-in-2026/) (2026-09-02): How the technical catalog and the metadata platform are converging in 2026, and how to arrange the two layers for a lakehouse.
- [Governance-as-Code for the Lakehouse: Managing REST Catalog RBAC and Masking in Git](https://iceberglakehouse.com/posts/governance-as-code-lakehouse-rest-catalog/) (2026-08-25): Put REST catalog RBAC and masking in Git. How to review grants, apply them safely, and keep lakehouse access from drifting.
- [Policy-Aware Lakehouse Telemetry: Building Auditable AI Records on Apache Iceberg for the EU AI Act Era](https://iceberglakehouse.com/posts/policy-aware-lakehouse-telemetry-eu-ai-act/) (2026-08-19): Policy-aware lakehouse telemetry for the EU AI Act era: build auditable, immutable, governed AI decision records on Apache Iceberg.
- [Securing the Agentic Lakehouse Gateway: A Threat Model for Prompt Injection, Exfiltration, and the Firewall That Reads Sentences](https://iceberglakehouse.com/posts/securing-agentic-lakehouse-gateway/) (2026-08-19): A threat model for the agentic lakehouse gateway covering prompt injection, exfiltration, and the firewall that reads sentences.

## Articles: Other

- [Where Lock-In Went](https://iceberglakehouse.com/posts/where-lock-in-went/) (2026-09-10): The format war ended and exit cost did not: where lock-in relocated after open tables won, how to measure it, and which costs are worth keeping down.

## Apache Iceberg Knowledge Base

- [Knowledge Base Index](https://iceberglakehouse.com/iceberg/)
- [Amazon S3 Tables for Apache Iceberg](https://iceberglakehouse.com/iceberg/amazon-s3-tables/)
- [Apache DataFusion](https://iceberglakehouse.com/iceberg/apache-datafusion/)
- [Apache Polaris Catalog](https://iceberglakehouse.com/iceberg/apache-polaris-catalog/)
- [Apache XTable Translations](https://iceberglakehouse.com/iceberg/apache-xtable-translations/)
- [AWS Athena and Apache Iceberg](https://iceberglakehouse.com/iceberg/aws-athena-iceberg/)
- [Amazon EMR and Apache Iceberg](https://iceberglakehouse.com/iceberg/aws-emr-iceberg/)
- [AWS Glue Catalog for Apache Iceberg](https://iceberglakehouse.com/iceberg/aws-glue-catalog/)
- [Microsoft Fabric and Apache Iceberg](https://iceberglakehouse.com/iceberg/azure-fabric-iceberg/)
- [Bauplan](https://iceberglakehouse.com/iceberg/bauplan/)
- [BigQuery and Apache Iceberg](https://iceberglakehouse.com/iceberg/bigquery-apache-iceberg/)
- [Catalog Federation](https://iceberglakehouse.com/iceberg/catalog-federation/)
- [Catalog Namespaces](https://iceberglakehouse.com/iceberg/catalog-namespaces/)
- [CDC Log Ingestion Pipelines](https://iceberglakehouse.com/iceberg/cdc-log-ingestion-pipelines/)
- [ClickHouse](https://iceberglakehouse.com/iceberg/clickhouse/)
- [Columnar Memory Layouts](https://iceberglakehouse.com/iceberg/columnar-memory-layouts/)
- [Data Lakehouse](https://iceberglakehouse.com/iceberg/data-lakehouse/)
- [Data Lineage Tracking](https://iceberglakehouse.com/iceberg/data-lineage-tracking/)
- [Databricks and Apache Iceberg](https://iceberglakehouse.com/iceberg/databricks-iceberg/)
- [dbt and Apache Iceberg](https://iceberglakehouse.com/iceberg/dbt-apache-iceberg/)
- [Debezium CDC Engines](https://iceberglakehouse.com/iceberg/debezium-cdc-engines/)
- [Decoupled Compute and Storage](https://iceberglakehouse.com/iceberg/decoupled-compute-and-storage/)
- [Delta Lake UniForm Metadata](https://iceberglakehouse.com/iceberg/delta-lake-uniform-metadata/)
- [Apache Doris and Apache Iceberg](https://iceberglakehouse.com/iceberg/doris-apache-iceberg/)
- [Dremio Acceleration Engine](https://iceberglakehouse.com/iceberg/dremio-acceleration-engine/)
- [Dremio Aggregation Reflections](https://iceberglakehouse.com/iceberg/dremio-aggregation-reflections/)
- [Dremio and Apache Iceberg](https://iceberglakehouse.com/iceberg/dremio-apache-iceberg/)
- [Dremio Arrow Flight SQL](https://iceberglakehouse.com/iceberg/dremio-arrow-flight-sql/)
- [Dremio Column-Level Masking](https://iceberglakehouse.com/iceberg/dremio-column-level-masking/)
- [Dremio Columnar Cloud Cache (C3)](https://iceberglakehouse.com/iceberg/dremio-columnar-cloud-cache-c3/)
- [Dremio Coordinator Node](https://iceberglakehouse.com/iceberg/dremio-coordinator-node/)
- [Dremio Data Reflections Matching](https://iceberglakehouse.com/iceberg/dremio-data-reflections-matching/)
- [Dremio Engine Auto-scaling](https://iceberglakehouse.com/iceberg/dremio-engine-auto-scaling/)
- [Dremio External Queries](https://iceberglakehouse.com/iceberg/dremio-external-queries/)
- [Dremio Iceberg Metadata Sync](https://iceberglakehouse.com/iceberg/dremio-iceberg-metadata-sync/)
- [Dremio Join Co-segmentation](https://iceberglakehouse.com/iceberg/dremio-join-co-segmentation/)
- [Dremio LDAP Integration](https://iceberglakehouse.com/iceberg/dremio-ldap-integration/)
- [Dremio Metadata Caching](https://iceberglakehouse.com/iceberg/dremio-metadata-caching/)
- [Dremio Parquet Vectorized Reader](https://iceberglakehouse.com/iceberg/dremio-parquet-vectorized-reader/)
- [Dremio Physical Datasets (PDS)](https://iceberglakehouse.com/iceberg/dremio-physical-datasets-pds/)
- [Dremio Raw Reflections](https://iceberglakehouse.com/iceberg/dremio-raw-reflections/)
- [Dremio Reflections](https://iceberglakehouse.com/iceberg/dremio-reflections/)
- [Dremio Row-Level Security (RLS)](https://iceberglakehouse.com/iceberg/dremio-row-level-security-rls/)
- [Dremio Sabot Engine](https://iceberglakehouse.com/iceberg/dremio-sabot-engine/)
- [Dremio Spaces](https://iceberglakehouse.com/iceberg/dremio-spaces/)
- [Dremio SQL Runner](https://iceberglakehouse.com/iceberg/dremio-sql-runner/)
- [Dremio User Defined Functions (UDFs)](https://iceberglakehouse.com/iceberg/dremio-user-defined-functions-udfs/)
- [Dremio Virtual Datasets (VDS)](https://iceberglakehouse.com/iceberg/dremio-virtual-datasets-vds/)
- [DuckDB and Apache Iceberg](https://iceberglakehouse.com/iceberg/duckdb-apache-iceberg/)
- [Dynamic Filter Pushdown](https://iceberglakehouse.com/iceberg/dynamic-filter-pushdown/)
- [Apache Flink and Apache Iceberg](https://iceberglakehouse.com/iceberg/flink-apache-iceberg/)
- [Glue Catalog IAM Policies](https://iceberglakehouse.com/iceberg/glue-catalog-iam-policies/)
- [Glue Catalog Lake Formation](https://iceberglakehouse.com/iceberg/glue-catalog-lake-formation/)
- [Google Cloud BigLake](https://iceberglakehouse.com/iceberg/google-cloud-biglake/)
- [Google Cloud and Apache Iceberg](https://iceberglakehouse.com/iceberg/google-cloud-iceberg/)
- [Apache Gravitino](https://iceberglakehouse.com/iceberg/gravitino-catalog/)
- [Hive and Apache Iceberg](https://iceberglakehouse.com/iceberg/hive-apache-iceberg/)
- [Iceberg Access Control Patterns](https://iceberglakehouse.com/iceberg/iceberg-access-control/)
- [ACID Transactions in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-acid-transactions/)
- [Agentic Lakehouse](https://iceberglakehouse.com/iceberg/iceberg-agentic-lakehouse/)
- [Iceberg AI Readiness](https://iceberglakehouse.com/iceberg/iceberg-ai-readiness/)
- [Iceberg AI Semantic Layer](https://iceberglakehouse.com/iceberg/iceberg-ai-semantic-layer/)
- [Apache Airflow and Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-apache-airflow/)
- [Iceberg Apache Arrow Flight](https://iceberglakehouse.com/iceberg/iceberg-arrow-flight/)
- [Iceberg Audit Logging](https://iceberglakehouse.com/iceberg/iceberg-audit-logging/)
- [Iceberg Avro Metadata Format](https://iceberglakehouse.com/iceberg/iceberg-avro-format/)
- [Iceberg Bin-Packing Compaction](https://iceberglakehouse.com/iceberg/iceberg-bin-packing-compaction/)
- [Iceberg Bloom Filters](https://iceberglakehouse.com/iceberg/iceberg-bloom-filters/)
- [Iceberg Branching and Tagging](https://iceberglakehouse.com/iceberg/iceberg-branching-tagging/)
- [Iceberg Bucket Partition Transform](https://iceberglakehouse.com/iceberg/iceberg-bucket-partition-transform/)
- [Iceberg Catalog Migration](https://iceberglakehouse.com/iceberg/iceberg-catalog-migration/)
- [What is an Iceberg Catalog?](https://iceberglakehouse.com/iceberg/iceberg-catalog/)
- [Iceberg CDC (Change Data Capture)](https://iceberglakehouse.com/iceberg/iceberg-cdc/)
- [Iceberg Table Clustering](https://iceberglakehouse.com/iceberg/iceberg-clustering/)
- [Iceberg Column Mapping](https://iceberglakehouse.com/iceberg/iceberg-column-mapping/)
- [Iceberg Column Projection](https://iceberglakehouse.com/iceberg/iceberg-column-projection/)
- [Iceberg Table Compaction](https://iceberglakehouse.com/iceberg/iceberg-compaction/)
- [Iceberg Concurrent Write Handling](https://iceberglakehouse.com/iceberg/iceberg-concurrent-writes/)
- [Copy-on-Write (CoW) in Iceberg](https://iceberglakehouse.com/iceberg/iceberg-copy-on-write/)
- [Iceberg Cost Optimization](https://iceberglakehouse.com/iceberg/iceberg-cost-optimization/)
- [Iceberg Data Files](https://iceberglakehouse.com/iceberg/iceberg-data-files/)
- [Iceberg Data Lineage](https://iceberglakehouse.com/iceberg/iceberg-data-lineage/)
- [Iceberg Data Masking](https://iceberglakehouse.com/iceberg/iceberg-data-masking/)
- [Iceberg Data Mesh Architecture](https://iceberglakehouse.com/iceberg/iceberg-data-mesh/)
- [Iceberg Data Skipping](https://iceberglakehouse.com/iceberg/iceberg-data-skipping/)
- [Iceberg Date/Time Partition Transforms](https://iceberglakehouse.com/iceberg/iceberg-date-time-partition-transforms/)
- [Iceberg Decimal Type Widening](https://iceberglakehouse.com/iceberg/iceberg-decimal-type-widening/)
- [Iceberg Delete Files](https://iceberglakehouse.com/iceberg/iceberg-delete-files/)
- [Iceberg Deletion Vectors](https://iceberglakehouse.com/iceberg/iceberg-deletion-vectors/)
- [Iceberg DynamoDB Catalog](https://iceberglakehouse.com/iceberg/iceberg-dynamodb-catalog/)
- [Iceberg Encryption](https://iceberglakehouse.com/iceberg/iceberg-encryption/)
- [Iceberg Equality Deletes](https://iceberglakehouse.com/iceberg/iceberg-equality-deletes/)
- [Expire Snapshots in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-expire-snapshots/)
- [Iceberg Feature Store](https://iceberglakehouse.com/iceberg/iceberg-feature-store/)
- [Iceberg File Content Type](https://iceberglakehouse.com/iceberg/iceberg-file-content-type/)
- [Iceberg FileIO API](https://iceberglakehouse.com/iceberg/iceberg-file-io/)
- [Iceberg File Path Spec](https://iceberglakehouse.com/iceberg/iceberg-file-path-spec/)
- [Hidden Partitioning in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-hidden-partitioning/)
- [Iceberg Hive Catalog Lock Manager](https://iceberglakehouse.com/iceberg/iceberg-hive-catalog-lock-manager/)
- [Hive Metastore Catalog for Iceberg](https://iceberglakehouse.com/iceberg/iceberg-hive-metastore/)
- [Iceberg Identity Partition Transform](https://iceberglakehouse.com/iceberg/iceberg-identity-partition-transform/)
- [Iceberg Incremental Reads](https://iceberglakehouse.com/iceberg/iceberg-incremental-read/)
- [Iceberg JDBC Catalog Locks](https://iceberglakehouse.com/iceberg/iceberg-jdbc-catalog-locks/)
- [Iceberg JDBC Catalog](https://iceberglakehouse.com/iceberg/iceberg-jdbc-catalog/)
- [Iceberg Lakehouse Federation](https://iceberglakehouse.com/iceberg/iceberg-lakehouse-federation/)
- [Iceberg LLM Grounding and RAG for Structured Data](https://iceberglakehouse.com/iceberg/iceberg-llm-grounding/)
- [Iceberg Lock Manager](https://iceberglakehouse.com/iceberg/iceberg-lock-manager/)
- [Iceberg Maintenance Scheduling](https://iceberglakehouse.com/iceberg/iceberg-maintenance-scheduling/)
- [Iceberg Manifest Entry Schema](https://iceberglakehouse.com/iceberg/iceberg-manifest-entry-schema/)
- [Iceberg Manifest Entry Status](https://iceberglakehouse.com/iceberg/iceberg-manifest-entry-status/)
- [Iceberg Manifest File](https://iceberglakehouse.com/iceberg/iceberg-manifest-file/)
- [Iceberg Manifest List Schema](https://iceberglakehouse.com/iceberg/iceberg-manifest-list-schema/)
- [Iceberg Manifest List](https://iceberglakehouse.com/iceberg/iceberg-manifest-list/)
- [Iceberg Manifest Merging](https://iceberglakehouse.com/iceberg/iceberg-manifest-merging/)
- [MCP and Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-mcp/)
- [Medallion Architecture with Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-medallion-architecture/)
- [Merge-on-Read (MoR) in Iceberg](https://iceberglakehouse.com/iceberg/iceberg-merge-on-read/)
- [Iceberg Metadata File](https://iceberglakehouse.com/iceberg/iceberg-metadata-file/)
- [Iceberg Metadata Pruning](https://iceberglakehouse.com/iceberg/iceberg-metadata-pruning/)
- [Iceberg Metadata Table Files](https://iceberglakehouse.com/iceberg/iceberg-metadata-table-files/)
- [Iceberg Metadata Table History](https://iceberglakehouse.com/iceberg/iceberg-metadata-table-history/)
- [Iceberg Metadata Table Snapshots](https://iceberglakehouse.com/iceberg/iceberg-metadata-table-snapshots/)
- [Iceberg Metrics Mode](https://iceberglakehouse.com/iceberg/iceberg-metrics-mode/)
- [Iceberg Table Migration from Hive](https://iceberglakehouse.com/iceberg/iceberg-migration-hive/)
- [Iceberg Multi-Catalog Architecture](https://iceberglakehouse.com/iceberg/iceberg-multi-catalog/)
- [Iceberg Multi-Tenancy Patterns](https://iceberglakehouse.com/iceberg/iceberg-multi-tenant/)
- [Iceberg Natural Language Analytics](https://iceberglakehouse.com/iceberg/iceberg-natural-language/)
- [Iceberg Nested Type System](https://iceberglakehouse.com/iceberg/iceberg-nested-type-system/)
- [Iceberg Open Table Format vs. Delta Lake vs. Apache Hudi](https://iceberglakehouse.com/iceberg/iceberg-open-table-format/)
- [Iceberg Optimistic Concurrency Control (OCC)](https://iceberglakehouse.com/iceberg/iceberg-optimistic-concurrency-control-occ/)
- [Apache Iceberg ORC Format](https://iceberglakehouse.com/iceberg/iceberg-orc-format/)
- [Iceberg Orphan Files Penalty](https://iceberglakehouse.com/iceberg/iceberg-orphan-files-penalty/)
- [Iceberg Orphan Files](https://iceberglakehouse.com/iceberg/iceberg-orphan-files/)
- [Iceberg Parent Snapshot ID](https://iceberglakehouse.com/iceberg/iceberg-parent-snapshot-id/)
- [Apache Parquet and Iceberg](https://iceberglakehouse.com/iceberg/iceberg-parquet/)
- [Partition Evolution in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-partition-evolution/)
- [Iceberg Partition-Level Compaction](https://iceberglakehouse.com/iceberg/iceberg-partition-level-compaction/)
- [Iceberg Performance Tuning Guide](https://iceberglakehouse.com/iceberg/iceberg-performance-tuning/)
- [Iceberg Positional Deletes](https://iceberglakehouse.com/iceberg/iceberg-positional-deletes/)
- [Iceberg Predicate Pushdown](https://iceberglakehouse.com/iceberg/iceberg-predicate-pushdown/)
- [Iceberg Puffin Files](https://iceberglakehouse.com/iceberg/iceberg-puffin-files/)
- [Iceberg REST Catalog API Reference](https://iceberglakehouse.com/iceberg/iceberg-rest-catalog-api/)
- [Iceberg REST Catalog](https://iceberglakehouse.com/iceberg/iceberg-rest-catalog/)
- [Iceberg Rewrite Manifests](https://iceberglakehouse.com/iceberg/iceberg-rewrite-manifests/)
- [Iceberg Rollback Snapshot Procedures](https://iceberglakehouse.com/iceberg/iceberg-rollback-snapshot-procedures/)
- [Iceberg Table Rollback](https://iceberglakehouse.com/iceberg/iceberg-rollback/)
- [Row-Level Deletes in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-row-level-deletes/)
- [Schema Evolution in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-schema-evolution/)
- [Iceberg Sequence Number Inheritance](https://iceberglakehouse.com/iceberg/iceberg-sequence-number-inheritance/)
- [Iceberg Sequence Number](https://iceberglakehouse.com/iceberg/iceberg-sequence-number/)
- [Small File Problem in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-small-file-problem/)
- [Iceberg Snapshot Expiration Age](https://iceberglakehouse.com/iceberg/iceberg-snapshot-expiration-age/)
- [Iceberg Snapshot References](https://iceberglakehouse.com/iceberg/iceberg-snapshot-references/)
- [Iceberg Snapshot Summary](https://iceberglakehouse.com/iceberg/iceberg-snapshot-summary/)
- [Apache Iceberg Snapshot](https://iceberglakehouse.com/iceberg/iceberg-snapshot/)
- [Iceberg Sort-Based Compaction](https://iceberglakehouse.com/iceberg/iceberg-sort-based-compaction/)
- [Iceberg Sort Order](https://iceberglakehouse.com/iceberg/iceberg-sort-order/)
- [Iceberg Spark Procedure add_files](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-add-files/)
- [Iceberg Spark Procedure expire_snapshots](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-expire-snapshots/)
- [Iceberg Spark Procedure register_table](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-register-table/)
- [Iceberg Spark Procedure remove_orphan_files](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-remove-orphan-files/)
- [Iceberg Spark Procedure rewrite_data_files](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-rewrite-data-files/)
- [Iceberg Spark Procedure rewrite_manifests](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-rewrite-manifests/)
- [Iceberg Spark Procedure rewrite_position_deletes](https://iceberglakehouse.com/iceberg/iceberg-spark-procedure-rewrite-position-deletes/)
- [Apache Iceberg Spec v1 vs v2](https://iceberglakehouse.com/iceberg/iceberg-spec-v1-vs-v2/)
- [Iceberg Spec V3 File Encryption](https://iceberglakehouse.com/iceberg/iceberg-spec-v3-file-encryption/)
- [Iceberg Spec V3 Object-Store Storage Layout](https://iceberglakehouse.com/iceberg/iceberg-spec-v3-object-store-storage-layout/)
- [Apache Iceberg Spec v3](https://iceberglakehouse.com/iceberg/iceberg-spec-v3/)
- [Apache Iceberg Spec v4 (Current State)](https://iceberglakehouse.com/iceberg/iceberg-spec-v4/)
- [Iceberg Table Statistics (Puffin)](https://iceberglakehouse.com/iceberg/iceberg-statistics/)
- [Iceberg Streaming Ingestion](https://iceberglakehouse.com/iceberg/iceberg-streaming/)
- [Iceberg Table Design Best Practices](https://iceberglakehouse.com/iceberg/iceberg-table-design/)
- [Apache Iceberg Table Format](https://iceberglakehouse.com/iceberg/iceberg-table-format/)
- [Iceberg Table Metadata Schema](https://iceberglakehouse.com/iceberg/iceberg-table-metadata-schema/)
- [Iceberg Table Properties](https://iceberglakehouse.com/iceberg/iceberg-table-properties/)
- [Time Travel in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-time-travel/)
- [Iceberg Truncate Partition Transform](https://iceberglakehouse.com/iceberg/iceberg-truncate-partition-transform/)
- [Iceberg Upsert (MERGE INTO)](https://iceberglakehouse.com/iceberg/iceberg-upsert/)
- [Iceberg Views](https://iceberglakehouse.com/iceberg/iceberg-views/)
- [Apache Iceberg vs Apache Hudi](https://iceberglakehouse.com/iceberg/iceberg-vs-apache-hudi/)
- [Apache Iceberg vs Delta Lake](https://iceberglakehouse.com/iceberg/iceberg-vs-delta-lake/)
- [Write-Audit-Publish (WAP) Pattern](https://iceberglakehouse.com/iceberg/iceberg-wap-pattern/)
- [Iceberg Write Distribution Modes](https://iceberglakehouse.com/iceberg/iceberg-write-distribution/)
- [Iceberg Z-Order Compaction](https://iceberglakehouse.com/iceberg/iceberg-z-order-compaction/)
- [Z-Order Clustering in Apache Iceberg](https://iceberglakehouse.com/iceberg/iceberg-zorder/)
- [Apache Kafka and Apache Iceberg](https://iceberglakehouse.com/iceberg/kafka-apache-iceberg/)
- [LangChain and Apache Iceberg](https://iceberglakehouse.com/iceberg/langchain-iceberg/)
- [Microsoft Fabric OneLake](https://iceberglakehouse.com/iceberg/microsoft-fabric-onelake/)
- [Modern Data Stack (MDS)](https://iceberglakehouse.com/iceberg/modern-data-stack-mds/)
- [Nessie Git-like Branching](https://iceberglakehouse.com/iceberg/nessie-git-like-branching/)
- [Nessie Merging](https://iceberglakehouse.com/iceberg/nessie-merging/)
- [Nessie Tagging](https://iceberglakehouse.com/iceberg/nessie-tagging/)
- [Object Storage Prefix Hashing](https://iceberglakehouse.com/iceberg/object-storage-prefix-hashing/)
- [Open Table Format Comparison (Iceberg, Delta Lake, Hudi, Paimon)](https://iceberglakehouse.com/iceberg/open-table-format-comparison/)
- [Polaris Catalog Sharing](https://iceberglakehouse.com/iceberg/polaris-catalog-sharing/)
- [Polaris RBAC Model](https://iceberglakehouse.com/iceberg/polaris-rbac-model/)
- [Polaris Service Principals](https://iceberglakehouse.com/iceberg/polaris-service-principals/)
- [Presto and Apache Iceberg](https://iceberglakehouse.com/iceberg/presto-apache-iceberg/)
- [Project Nessie](https://iceberglakehouse.com/iceberg/project-nessie/)
- [PuppyGraph](https://iceberglakehouse.com/iceberg/puppygraph/)
- [PyIceberg: Python Library for Apache Iceberg](https://iceberglakehouse.com/iceberg/pyiceberg/)
- [Read Amplification](https://iceberglakehouse.com/iceberg/read-amplification/)
- [REST Catalog Credential Vending](https://iceberglakehouse.com/iceberg/rest-catalog-credential-vending/)
- [REST Catalog OAuth2 Token Flow](https://iceberglakehouse.com/iceberg/rest-catalog-oauth2-token-flow/)
- [Single Source of Truth (SSOT)](https://iceberglakehouse.com/iceberg/single-source-of-truth-ssot/)
- [Snowflake External Table Catalog Sync](https://iceberglakehouse.com/iceberg/snowflake-external-table-catalog-sync/)
- [Snowflake Iceberg Tables](https://iceberglakehouse.com/iceberg/snowflake-iceberg-tables/)
- [Snowflake Managed Iceberg Tables](https://iceberglakehouse.com/iceberg/snowflake-managed-iceberg-tables/)
- [Snowflake Open Catalog](https://iceberglakehouse.com/iceberg/snowflake-open-catalog/)
- [Space Amplification](https://iceberglakehouse.com/iceberg/space-amplification/)
- [Apache Spark and Apache Iceberg](https://iceberglakehouse.com/iceberg/spark-apache-iceberg/)
- [Spice.ai](https://iceberglakehouse.com/iceberg/spiceai/)
- [Split Planning Loops](https://iceberglakehouse.com/iceberg/split-planning-loops/)
- [StarRocks and Apache Iceberg](https://iceberglakehouse.com/iceberg/starrocks-apache-iceberg/)
- [Apache Superset and Apache Iceberg](https://iceberglakehouse.com/iceberg/superset-apache-iceberg/)
- [Time To First Byte (TTFB)](https://iceberglakehouse.com/iceberg/time-to-first-byte-ttfb/)
- [Trino and Apache Iceberg](https://iceberglakehouse.com/iceberg/trino-apache-iceberg/)
- [Unity Catalog Delta-Iceberg Compatibility](https://iceberglakehouse.com/iceberg/unity-catalog-delta-iceberg-compatibility/)
- [VeloDB](https://iceberglakehouse.com/iceberg/velodb/)
- [Vortex File Format](https://iceberglakehouse.com/iceberg/vortex-file-format/)
- [What is Apache Iceberg?](https://iceberglakehouse.com/iceberg/what-is-apache-iceberg/)
- [Write Amplification](https://iceberglakehouse.com/iceberg/write-amplification/)
- [Zero-Copy Cloning](https://iceberglakehouse.com/iceberg/zero-copy-cloning/)

## Events

- [Agentic Lakehouse Events](https://luma.com/agenticlakehouse): global meetups and webinars on agentic analytics
- [Data Lakehouse Hub Events](https://luma.com/DataLakehouseHub): global lakehouse meetups, linkups and webinars

## Community

- [Data Lakehouse Hub Slack](https://join.slack.com/t/thedatalakehousehub/shared_invite/zt-274yc8sza-mI2zhCW8LGkOh1uxuf8T5Q): practitioner community for lakehouse architecture
- [Data Events Slack](https://join.slack.com/t/data-events/shared_invite/zt-38vgrooy9-U9ral_gr3NAz_Siih1QwmQ): announcements for data conferences and meetups
- [Data & Tech Slack](https://join.slack.com/t/datatechcommunity/shared_invite/zt-12xrk4qmd-y~6jUFFd7kdaLhgLURKwoA): broader data and technology community
- [r/datalakehouseandai](https://www.reddit.com/r/datalakehouseandai/): subreddit for data lakehouse and AI discussion
- [Data Lakehouse Hub on LinkedIn](https://www.linkedin.com/company/data-lakehouse-hub/): company page for the Data Lakehouse Hub
- [Alex Merced Tech on YouTube](https://www.youtube.com/@AlexMercedCoder): software development and engineering channel
- [Alex Merced Data & AI on YouTube](https://www.youtube.com/@alexmerceddata): data lakehouse and AI channel