LakeOpsLakeOps
Demo
Back to all articles

Data Governance articles

Data governance, compliance, and policy enforcement — GDPR, access control, lineage, and catalog management.

AllData GovernanceApache IcebergLakeOpsLakehouseData PlatformsData LakeCompactionObservabilityCloud CostStreaming

8 articles

Apache Iceberg lakehouse governance — separation of concerns with Polaris and policy engines
Apache IcebergData GovernanceLakehouseLakeOps

Apache Iceberg Lakehouse Governance: Separation of Concerns with Polaris and Policy Engines

Iceberg deliberately avoids embedding governance into its table format — access control, classification, and policy enforcement belong in the catalog and policy engine layers. This article lays out the three-layer model: table format for data portability, catalog control plane for enforcement, and pluggable policy engines for rules. How Polaris, OPA, and Ranger fit together in production multi-engine lakehouses.

Chris P
Chris P
26 min read
Apache Iceberg V3 for streaming — row-level lineage, schema evolution, and governance
Apache IcebergStreamingData GovernanceLakeOps

Apache Iceberg V3 for Streaming: Row-Level Lineage, Schema Evolution, and Governance

Iceberg V3 brings row-level lineage, default column values, and deletion vectors — the features streaming pipelines need for governance without downtime. How V3 changes the streaming governance story on Flink, Kafka, and CDC sources.

Rob M
Rob M
26 min read
Multi-table transactions in Apache Iceberg — cross-table atomicity for the open lakehouse
Apache IcebergLakeOpsMulti-Table TransactionsREST Catalog

Iceberg Multi-Table Transactions: Cross-Table Atomicity for Production Lakehouses

Star schema ETL and multi-table CDC need atomic commits across Iceberg tables — not just single-table ACID. How the REST Catalog transaction API, Polaris, Nessie, and Gravitino enable cross-table atomicity, and what teams run in production today.

Chris P
Chris P
16 min read
Apache Iceberg Schema Evolution in Production — best practices and pitfalls, with schema version progression from v1 through v4
Apache IcebergData GovernanceLakeOpsLakehouse

Apache Iceberg Schema Evolution in Production: Best Practices and Pitfalls

Schema evolution is one of Iceberg's most powerful features — but misusing it in production causes silent downstream failures, broken statistics, and multi-engine inconsistencies. A practical guide to safe schema changes, column ID mechanics, partition evolution, branch-based testing, rollback strategies, and monitoring schema drift across the lakehouse.

Rob M
Rob M
28 min read
Apache Iceberg Production Readiness Checklist for Enterprise Data Lakes — security, storage, operations, and governance
Apache IcebergProduction ReadinessLakeOpsLakehouse

Apache Iceberg Production Readiness Checklist for Enterprise Data Lakes

Taking Apache Iceberg from proof-of-concept to enterprise production requires decisions across ten operational dimensions — catalog architecture, table design, write path tuning, maintenance automation, observability, multi-engine coordination, security, disaster recovery, cost management, and on-call readiness. This checklist covers each one with concrete configurations, SQL examples, and the automation patterns that keep large-scale lakehouses healthy.

Rob M
Rob M
24 min read
Iceberg Lakehouse with AI Agents: A Guide — AI agent robots navigating an Apache Iceberg lakehouse with analytics dashboards, AI brain, and governance shield icons, Build like Netflix subtitle
AIApache IcebergLakehouseLakeOps

Iceberg Lakehouse with AI Agents: A Guide

AI agents are becoming primary consumers of Iceberg lakehouse data — querying tables iteratively, at high frequency, and without human review. This guide walks through the five components your infrastructure needs to support agentic workloads — MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and observability — and shows how LakeOps provides each one.

Jonathan Saring
Jonathan Saring
24 min read
Diagram showing seven Iceberg catalog options — Polaris, Nessie, Glue, Unity, Gravitino, Lakekeeper, and Hive — connected to a central Apache Iceberg symbol
Apache IcebergLakehouseData LakeData Governance

Best Catalog for Apache Iceberg? A Useful Comparison

A technical comparison of the seven major Apache Iceberg catalogs — Hive Metastore, AWS Glue, Apache Polaris, Project Nessie, Databricks Unity Catalog, Apache Gravitino, and Lakekeeper — across protocol support, access control, multi-engine interoperability, credential vending, and production readiness.

Chris P
Chris P
21 min read
LakeOps Data Lake Insights showing metadata health alerts across Iceberg tables — manifest fragmentation, snapshot accumulation, and partition skew
Apache IcebergData PlatformsData LakeLakeOps

Iceberg Metadata Lifecycle: Maintenance and Optimization

A deep technical guide to managing the metadata layer that makes Apache Iceberg fast — snapshots, manifests, metadata.json files, and Puffin statistics — covering expiration, consolidation, orphan cleanup, and the sequencing that prevents production incidents.

Jonathan Saring
Jonathan Saring
19 min read