Why observability needs Apache Iceberg
Hi, I'm Jon. Today, I'd like to talk about a hot topic in the world of data management. Based on a recent article in InfoWorld, I'll explain how Apache Iceberg, an open source technology, can help with a system monitoring technique called observability. Observability is an approach to understanding the internal state of software or systems from the outside, using logs and metrics to quickly identify problems. Apache Iceberg, on the other hand, is a table format for efficiently handling large amounts of data. In this article, I'll explain how these two technologies are connected in a way that's easy to understand, even for beginners.
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What is Apache Iceberg? Understanding the Basics
First, let's explain the basics of Apache Iceberg. Apache Iceberg is an open-source table format for handling big data. It is used in data lakes (repositories that store large amounts of data) and is characterized by its ability to easily manipulate data using SQL-like queries. Traditional data lakes are like collections of files, which makes management complex, but Iceberg adds reliability and simplicity as a table.
For example, it works with engines such as Spark, Trino, and Flink to support commands to add, update, and delete data. This allows for efficient handling of even large amounts of data. Since being officially established as a project by the Apache Software Foundation in 2022, it has been adopted by major companies such as Apple, LinkedIn, and Netflix. At X (formerly Twitter), Iceberg is being recommended alongside PySpark and ETL (Extract, Transform, Load: the process of extracting, transforming, and loading data) as part of its 2025 data engineering roadmap.
Furthermore, Apache Iceberg V3, the latest release in 2025, has enhanced functionality to solve data lake issues, and is a hot topic for innovations that will allow data engineers to "ride the Iceberg wave from data lakes." For example, a Medium article emphasizes that Iceberg will scale AI and analytics as the foundation for a data lakehouse (integration of a data lake and a data warehouse).
Key Benefits of Iceberg
- Durability: Store data safely and prevent loss.
- Queryability: Easily searchable with SQL.
- Sharability: Data can be easily shared between multiple tools.
- Performance: Optimize your data files for faster loading.
These features are particularly useful when dealing with large-scale telemetry data (system monitoring data). Next, we'll take a closer look at how it relates to observability.
What is Observability and Why Is It Important?
Observability is a concept for monitoring the health of a system. Unlike traditional monitoring (simply collecting data), observability utilizes logs (event records), metrics (numerical data), and traces (processing flow) to deeply analyze the cause of problems. As a trend for 2025, the Hydrolix blog highlights the focus on cost-effective approaches to log monitoring, with AI-driven automation becoming increasingly important.
X's post also lists structured logging and distributed tracing (tracing requests using OpenTelemetry) as observability patterns for 2025, and they are essential tools for improving productivity. Guillermo Rauch's post also points out that advances in autonomous infrastructure and AI may make traditional graph monitoring obsolete.
Here, I would like to recommend Gamma as an example of how to use AI tools. Gamma is a new standard for instantly creating documents, slides, and websites using AI. It is useful when you want to create materials that visualize observability data. For more information,This article .
Observability trends for 2025
- AI integration: Automated pattern detection reduces human intervention.
- Cost-effective: Efficient management of large-scale logs.
- Advancing OpenTelemetry: Standardized tracing makes it easier for tools to work together.
- Platform market growth: The market for companies like Datadog and New Relic is expected to expand through 2032 (OpenPR report).
Among these trends, the explosive growth in data volume presents a challenge, which is where Apache Iceberg comes in.
Why do we need Apache Iceberg for observability? Learn from this InfoWorld article
According to an article in InfoWorld published on October 2, 2025, "Why observability needs Apache Iceberg," telemetry data and business data do not need to be treated separately. Iceberg transforms logs, metrics, and traces into a durable, queryable, and enterprise-wide shareable data, allowing observability data to generate business value.
While telemetry data has traditionally been treated as temporary, Iceberg manages it as a permanent table. For example, using Iceberg in a data lakehouse strengthens protection for AI analytics and compliance. In a Clumio announcement (October 2025), Iceberg-compatible backups were introduced as a measure against ransomware, and improving data lakehouse security is also a trend at X.
Additionally, a September 2025 article in The New Stack dispels the myth of open source complexity, stating that Iceberg offers simplicity and reliability.A March 2025 post by Jay Davé on Medium positions Iceberg as a tool that lays the foundation for observability in modern data lakes.
Iceberg brings tangible benefits to observability
- Data unification: Telemetry and business data can be handled in the same tables.
- Scalability: Fast queries even on enterprise-scale data.
- Improved durability: Updates are more efficient with deletion deltas and file optimization.
- Ease of sharing: can be accessed by multiple engines simultaneously.
This makes Iceberg the new standard for observability in data management in 2025.
Jon's Summary
Apache Iceberg is a powerful tool that transforms observability data into something more valuable. Even if you're a beginner, try it out with basic data management first. If you want to use AI tools to streamline document creation, we recommend Gamma. For more information,This articlePlease check.
Overall, the combination of observability and Iceberg will be at the heart of data trends in 2025 and will be key to increasing system reliability. I'm personally excited about the evolution of these technologies, so I encourage you to keep up with the latest developments.
Reference sources
- InfoWorld: Why observability needs Apache Iceberg (October 2, 2025)
- Medium: Apache Iceberg and Modern Data Lakes (March 19, 2025)
- The New Stack: Dispelling Myths of Open Source Complexity With Apache Iceberg (September 11, 2025)
- Hydrolix Blog: Observability in 2025 (September 2025)
- OpenPR: Observability Platform Market Projections 2025-2032 (October 3, 2025)
- Apache Iceberg official website (updated June 24, 2022)
- Related posts from X (formerly Twitter): Data Engineering Roadmap and Observability Patterns (various dates in 2025)