Topic
Lakehouse
29 posts tagged “Lakehouse”.
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Defending the Lakehouse Gateway Against Prompt Injection and Data Exfiltration
A support ticket contains a customer's message. Somewhere in that message is a sentence addressed to nobody who works at your company: ignore your…
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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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Federating Oracle With an Open Lakehouse Instead of Migrating It
A team is eighteen months into moving reporting off Oracle. Forty percent of the tables are in Iceberg. The remaining sixty percent are the hard…
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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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The Whole Lakehouse Fits on Your Laptop Now
A colleague spent forty minutes last month provisioning a cluster to profile a 90 GB Parquet dataset. Startup, dependency resolution, a permissions…
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Iceberg's Next Version Depends on Decisions Being Made in Parquet
Follow an Apache Iceberg design discussion about column-level updates long enough and it stops being an Iceberg discussion. The question of whether a…
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Building a Lakehouse That Stays Inside the Border
A manufacturer in southern Germany asked me a question during an architecture review that I have thought about since. Their data sat in a Frankfurt…
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Table Maintenance Stopped Being a Product
A team I worked with had a dashboard that loaded in three seconds in January and forty seconds in June. Data volume grew 20 percent over that period.…
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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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Data Lakehouse Open Standards for AI Agents
A single analyst running a report might touch three tables and issue a dozen queries in an afternoon. An AI agent working the same problem can issue…
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Dremio Lakehouse AI Report: Agentic Lessons
Most lakehouse roadmaps written in the last few years had the same top items: migrate off the expensive warehouse, cut storage costs, consolidate on…
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Zero-Copy Mirroring to Open Iceberg Tables
The most expensive part of moving off a proprietary warehouse is usually not the compute contract. It is the copy. Rewriting terabytes of data into a…
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Migrating Proprietary Warehouses to Open Lakehouses: The 2026 Playbook for Zero-Copy Metadata Translation
Every warehouse migration sounds simpler before the first inventory. Then the team discovers old dashboards, hidden dependencies, undocumented stored…
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Lakehouse as the Operating Layer for Agentic AI
Agentic AI announcements are useful when they validate the need for governed data, semantic context, and cost-aware execution. For data leaders…
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What Is LTAP in the Lakehouse?
Lakehouse transactional analytical processing is useful only when teams define freshness, isolation, and workload boundaries clearly. For data…
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Agentic Lakehouse Concurrency and Isolation
Agentic writes need isolation contracts, not just write permissions. That is the useful lens for agentic lakehouse concurrency in June 2026. The…
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Anatomy of an Agentic Lakehouse
An Agentic Lakehouse is storage, catalog governance, semantic context, and agents working as one operating model. That is the useful lens for agentic…
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Lakehouse Context Layers with Atlan and Iceberg v3
The context layer explains what lakehouse data means, which is the part table formats do not solve alone. That is the useful lens for lakehouse…
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Goal-Directed Analytics Agents on Apache Iceberg
The next step after text-to-SQL is a governed action loop with checks before every external effect. That is the useful lens for goal-directed…
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Iceberg Remote Signing for Regulated Datasets
Remote signing is the stricter pattern for lakehouse storage security because clients request signed file operations instead of receiving storage…
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Apache Iceberg v3 Deletion Vectors on Snowflake
Deletion vectors matter because row-level changes should not require a full rewrite of every affected data file. That is the useful lens for Apache…
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CDC Without Complexity Using Iceberg v3 Row Lineage
Row lineage gives Iceberg a native way to tell incremental consumers which rows changed and when they changed. That is the useful lens for Iceberg v3…
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The 2026 Guide to Iceberg View Federation
Portable views are the missing logic layer between open tables and multi-engine analytics. That is the useful lens for Iceberg view federation in…
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Implementing MCP in the Lakehouse
MCP gives AI clients a standard way to call governed lakehouse tools instead of guessing how to query your data. That is the useful lens for MCP…
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Modern Python Tooling for Apache Iceberg
Python has become a practical Iceberg control plane for metadata work, catalog automation, and smaller operational workflows. That is the useful lens…
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REST Catalog Credential Vending for Lakehouse Security
Credential vending lets the catalog issue short-lived storage access instead of spreading permanent cloud keys across every engine. That is the…
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Securing Agent Identities in the Lakehouse
Every lakehouse agent needs its own identity, scope, and audit trail. That is the useful lens for agent identities lakehouse in June 2026. The market…
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Bidirectional Iceberg Writes with Horizon Catalog
Bidirectional Iceberg interoperability changes managed Iceberg from a read surface into a shared write contract. That is the useful lens for…
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Zero-Copy Mirroring for Modern Lakehouse Migration
Zero-copy mirroring gives teams a safer migration path because they can expose a lakehouse surface before they duplicate every byte or rewrite every…