Topic
Apache Polaris
29 posts tagged “Apache Polaris”.
-
How Apache Polaris Plans to Share Iceberg Tables Across Organizations
A retailer wants to give a supplier live access to three sales tables. Nothing else. The supplier runs its own query engine in its own cloud account.…
-
Keeping Lakehouse Traffic Off the Public Internet With Apache Polaris
A bank's security team reviews its new lakehouse design. Apache Polaris serves as the Iceberg REST catalog. Query engines run in private subnets. The…
-
Running Apache Polaris in Production
The Polaris quickstart takes about four minutes. Pull a container, hit the OAuth endpoint, create a catalog, point Spark at it, write a table. It…
-
Running an Iceberg Lakehouse on Kubernetes
A team moves their lakehouse onto Kubernetes because everything else already runs there. The catalog goes into a Deployment, Spark jobs run through…
-
The 2026 Iceberg REST Catalog Compatibility Report
Every lakehouse vendor now ships an Apache Iceberg REST catalog. Every one of them says it implements the same specification. Then you point…
-
How Iceberg Catalogs Hand Engines Storage Access
Look at how most lakehouses were wired in 2023. Spark had an IAM role with read and write on the whole data bucket. Trino had another one. The Python…
-
The Open Lakehouse Explained, Then Built on Your Laptop with Dremio and MinIO
Most people learn the open lakehouse backwards. They read five vendor pages, collect a stack of Apache project names, and still cannot answer a basic…
-
Apache Ossie and Apache Polaris: Putting Semantic Models in the Open Catalog
Ask four systems in the same company what "monthly active users" means and you get four answers. The BI tool counts distinct user IDs with at least…
-
Governance-as-Code for the Lakehouse: Managing REST Catalog RBAC and Masking in Git
A security audit asks a simple question: who can read the customers.pii table, and when was that last changed? The data platform team opens four…
-
Multi-Cloud REST Catalog Topologies: Running Apache Polaris Across AWS, Azure, and GCP
A global company has analytics data in three places. Its retail arm runs on AWS in Virginia and Frankfurt. An acquisition brought a Google Cloud…
-
The Decoupled Data Lakehouse: Multi-Engine Freedom with Open REST Catalogs
Every few years a data team discovers, mid-contract-renewal, exactly how much of their platform they do not control. The data sits in the vendor's…
-
The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits
Someone on your team connects an AI desktop client to a query engine, asks a question about last quarter, and gets an answer in fifteen seconds. It…
-
Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
An executive asks an AI assistant what revenue looked like last quarter. The assistant writes SQL against the warehouse, sums an amount column, and…
-
Cross-Cloud Credential Vending in Apache Polaris and the End of Permanent Storage Keys
Pull up the configuration for any Spark cluster that reads a data lake and look for the storage credentials. In most organizations you find an IAM…
-
How the Iceberg REST Catalog Turned Into the Lakehouse Control Plane
A dbt run updates a fact table and two dimension tables. The fact table commit succeeds. The second dimension commit fails on a conflict. For the…
-
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…
-
Wiring an AI Agent to Apache Polaris with the Model Context Protocol
An engineer opens Cursor, types "what tables do we have in the sales namespace, and which ones have a customerid column," and gets an answer in four…
-
Governing Iceberg Tables Across Regions Without Three Sets of Permissions
An engineer needs to join sales data in eu-west-1 with product data in us-east-1. The sales tables live in a Polaris instance the European team runs.…
-
The Breakdown of the Open Lakehouse in 2026: Iceberg, Arrow, Polaris, Parquet, and Ossie, and How to Actually Build One
If you have followed the data world for the past few years, you have heard the phrase "open lakehouse" often enough that it may have started to sound…
-
Apache Polaris and Multi-Engine Iceberg Catalogs
Apache Iceberg solved a hard problem: it gave analytics teams a table format that multiple engines can read and write without corrupting each other's…
-
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…
-
Multi-Engine Catalog Federation with Apache Polaris: Syncing Google Cloud, AWS, and Azure Metadata
Open table formats changed the data lakehouse conversation, but they did not finish it. A table can be stored in an open format and still be hard to…
-
Unity AI Gateway vs Apache Polaris Control Planes
The right comparison is not vendor scoreboard. It is closed AI governance gateway versus open catalog control plane. For enterprise architects…
-
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.
-
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.