Iceberg is harder than you think

2026-01-06 · Source: MLOps.community · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Implementing Apache Iceberg, a table format for large analytic datasets, presents significant challenges despite its popularity. Organizations struggle with a series of choices, including transporting data from online event buses or databases to offline file systems like S3 or GCS. A major hurdle is selecting and integrating a suitable catalog, which remains an unsolved problem across different cloud environments and must interoperate with existing data systems, such as BigQuery. Furthermore, merely storing data in Iceberg files does not guarantee full optimization benefits; users must actively implement features like clustering and views, which are often not automatically supported by cloud-native offerings like Google Cloud. This complexity leads many to develop custom catalogs or intricate ETL processes to convert data into Iceberg format.

Key takeaway

For data architects and engineering leaders evaluating or planning Apache Iceberg adoption, recognize that its implementation is not plug-and-play. Your team should anticipate significant effort in selecting and integrating a data catalog, developing custom ETL processes, and actively configuring engine-level optimizations like clustering and views to fully realize Iceberg's performance benefits. Do not underestimate the operational complexity involved.

Key insights

Iceberg implementation is complex, requiring careful data transport, catalog integration, and engine optimization.

Principles

• Iceberg benefits require active optimization.
• Catalog integration is a key challenge.

Method

Data moves from event buses/databases to S3/GCS, then ETL processes convert it to Iceberg format, followed by custom catalog integration and engine optimization for clustering and views.

In practice

• Evaluate catalog solutions carefully.
• Plan for custom ETL pipelines.
• Implement clustering for performance.

Topics

• Apache Iceberg
• Data Catalogs
• Data Infrastructure
• Data Optimization
• ETL Processes

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, AI Architect, MLOps Engineer

Related on AIssential

• Advancing Apache Iceberg on Databricks: Iceberg v3 GA, Open Sharing, and Unified Governance — Unity Catalog unifies Apache Iceberg governance, sharing, and performance across diverse engines and external catalogs.
• Main Data Warehouse Components Explained (2025) — Data warehouses organize vast datasets into actionable insights using a layered architecture and dedicated processing.
• Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability — Lakehouse architectures can provide scalable, cost-efficient observability by centralizing diverse telemetry data.
• Building a Data Mesh in the Real World — Real-world Data Mesh implementation requires pragmatic adaptation of theoretical principles to organizational realities.
• The Next Era of the Open Lakehouse: Apache Iceberg™ v3 in Public Preview on Databricks — Iceberg v3 enhances open lakehouse capabilities with features for incremental processing and semi-structured data.
• Slack Eliminates SSH in EMR Pipelines, Migrates 700+ Jobs to Rest-Based Architecture — Replacing direct SSH access with a REST-driven orchestration layer significantly enhances data pipeline security, reliability, and observability.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MLOps.community.