The converged lakehouse paradigm: combining warehouse reliability with data lake flexibility using open table formats.
13 articles
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.
Databricks to Iceberg smooth migration opens a multi-engine lakehouse — not a platform exit. Databricks stays central for ML and Spark; Iceberg adds Trino, Snowflake, and open catalogs. Five tools: LakeOps, UC managed Iceberg, Delta UniForm, Spark, and Lakehouse Federation.
A practical guide for senior data engineers expanding Snowflake into a multi-engine Iceberg lakehouse. Covers five production tools — LakeOps, managed Iceberg, Open Catalog sync, Spark, and AWS Glue — with migration patterns, operational trade-offs, and a phased rollout sequence.
How to route queries across Trino, Spark, DuckDB, Snowflake, Athena, and Flink on shared Iceberg tables — covering the architecture of a SQL routing proxy, dialect translation, routing strategies, table-aware optimization, and the tooling that makes it work.
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.
Data lakes, warehouses, and lakehouses are not interchangeable — each has hard limits the others cannot cover. A practical guide for platform leaders: where each architecture wins, where it fails, cost and governance trade-offs, and how to choose (or combine) them in 2026.
Compaction keeps Apache Iceberg lakehouses fast and lean — but every tool approaches it differently. A side-by-side look at nine production options: LakeOps, AWS Glue, Amazon S3 Tables, Snowflake, Google BigLake, Cloudera, Starburst, Dremio, and Databricks.
A practical walkthrough of optimizing an Apache Iceberg lakehouse end to end — from connecting catalogs and diagnosing table health through autonomous compaction, lifecycle management, and multi-engine routing to measurable cost and performance outcomes.
Every data lake starts with a promise of unlimited flexibility — and most end up as a swamp. Stale files, broken schemas, no observability, and engineers spending more time maintaining pipelines than analyzing data. Apache Iceberg fixed the reliability gap. A lakehouse control plane fixes everything else. A practical guide to the full transition — component by component.
Compaction is the most impactful operation in an Apache Iceberg lakehouse — and the hardest to get right at scale. File merging is the easy part. Knowing when to trigger it, what sort strategy to apply per table, how to avoid conflicting with other maintenance, and how to do it without spinning up expensive JVM clusters — that is the real problem. A breakdown of what modern compaction actually requires.
Apache Iceberg gives your lakehouse warehouse-grade reliability on object storage — but the format does not optimize itself. A practical guide to every operational pillar a production Iceberg lakehouse needs — from lake-wide observability and query-aware compaction to snapshot lifecycle, metadata health, and governance — and how LakeOps runs it all from a single control plane.
For a decade, Snowflake and Databricks defined enterprise data. Then the lakehouse emerged — open formats on open storage. What was missing was the operational layer to make it work at scale. An autonomous control plane turns a lakehouse into a managed open data platform — without the lock-in.
ExternalA detailed comparison between Delta Lake and Apache Iceberg, exploring their architectures, performance characteristics, and ideal use cases to help you make the right choice.
