Going to Iceberg Summit 2025? Here's Why Data Observability Should Be on Your Radar

If you're heading to Iceberg Summit 2025, you're already part of the conversation shaping the future of data infrastructure. This summit gathers the brightest minds building, scaling, and optimizing modern data stacks using technologies like Apache Iceberg.

But as your data stack becomes more advanced, the stakes rise too. Silent data failures, broken pipelines, schema drift, and costly outages can wreak havoc—especially when they go undetected.

This is where Data Observability becomes essential. As organizations adopt decoupled, modular, and cloud-native architectures, maintaining trust in data across platforms like Iceberg is no longer optional—it's mission-critical.

In this blog, we'll explore:

• The unique data challenges with Iceberg
• What Data Observability actually means (and why it's different from monitoring)
• How Rakuten SixthSense helps teams eliminate data downtime
• Why summit attendees should prioritize observability in 2025

Let's dive in.

The Real Challenges with Modern Data Platforms

Apache Iceberg is revolutionizing data storage and analytics.

• Iceberg brings open-table format innovation with ACID compliance, schema evolution, partition pruning, and time-travel queries.

But that power comes with trade-offs. More moving parts mean more blind spots.

Top challenges data teams face with Iceberg include:

• Silent Failures: A schema changes upstream, and suddenly your dashboard returns nulls—with no alerts.
• Inconsistent Lineage: With multiple ingestion points, lineage from raw to report is often murky.
• Pipeline Breaks: A job fails midway, and partial data is written—but no one knows.
• Schema Drift: A field changes from string to int, breaking downstream logic.
• Data Freshness Lags: The nightly load didn't run, and teams are making decisions off stale data.

These aren't edge cases. They happen every day in production environments. And while Iceberg offers some built-in monitoring, it doesn't deliver end-to-end, data-layer visibility.

What is Data Observability (and Why Should You Care)?

Data Observability is the ability to understand, monitor, and trust your data across the entire data lifecycle. It's not about system health or pipeline uptime. It's about data reliability.

The 5 Pillars of Data Observability:

1. Freshness – Is my data up to date?
2. Distribution – Does the data look as expected (ranges, nulls, uniqueness)?
3. Volume – Did we receive the right number of records?
4. Schema – Has the structure changed unexpectedly?
5. Lineage – Can we trace where this data came from and how it was transformed?

Together, these ensure that data is trustworthy before it reaches business teams.

Unlike traditional monitoring that flags only pipeline failures or infra issues, observability flags data issues in real-time, helping teams debug and resolve before damage is done.

Why Iceberg Requires Data Observability

The very things that make Iceberg powerful also make observability non-negotiable.

1. High Volume, High Velocity Ingestion

Iceberg tables often ingest data from streaming platforms like Kafka, Spark, or Flink. In this setup, a small bug upstream can silently corrupt massive amounts of data.

2. Modular, Decoupled Pipelines

Most organizations use Airflow, dbt, or Dagster to stitch pipelines together. But when something breaks in one stage, no one knows unless it surfaces downstream.

3. Schema Evolution

Iceberg supports schema evolution natively. But what happens when a column type changes and breaks downstream logic? Without observability, you'll never know until users complain.

4. Layered Querying

Teams often layer analytics tools on top of Iceberg-based systems. A mismatch in metadata or partitioning logic can return incorrect aggregates. Without automated anomaly detection, these errors go unnoticed.

Bottom line: If your data stack includes Iceberg, observability isn't a nice-to-have—it's critical to production data health.

How Rakuten SixthSense Solves the Data Observability Problem

Rakuten SixthSense is purpose-built to tackle the unique challenges of today's cloud-native data environments.

Here's how we help:

1. Real-Time Anomaly Detection

We monitor Iceberg tables continuously for unusual behavior—row count drops, null surges, schema mismatches, and more.

2. AI-Powered Scoring

Our proprietary scoring system tells you which issues matter most, helping data teams prioritize based on impact.

3. Pipeline Monitoring

SixthSense tracks every pipeline stage across Airflow, dbt, Spark, etc. One failure? We trace it back to root cause fast.

4. Schema Drift Alerts

Instant notifications when schemas evolve unexpectedly—even if jobs still technically run.

5. End-to-End Lineage

From Iceberg raw ingestion to analytics dashboards, our visual lineage maps show how data flows and where things break.

6. High-Speed Integrations

With fast connectors for Kafka, Iceberg, S3, GCS, BigQuery, and more, you can onboard in hours, not weeks.

7. Smart Dashboards

Get tailored views for data engineers, platform teams, and business users. Everyone gets what they need—no noise.

Result: Fewer surprises. Faster resolution. Happier teams.

Real-World Scenario: Iceberg Observed in Real-Time

Let's walk through a common scenario:

• A Kafka stream pushes real-time logs into an Apache Iceberg table partitioned by region.
• A dev mistakenly changes the schema, converting a timestamp field into a string.
• Iceberg accepts it, but downstream analytics tools return null values or errors.
• Dashboards go blank. Nobody knows why.

Without observability: Hours of debugging.

With Rakuten SixthSense:

• Alert fires immediately.
• Schema drift is identified.
• Downstream lineage impact is visualized.
• Fix is deployed in minutes.

That's the SixthSense difference.

Preparing for Iceberg Summit 2025? Ask the Right Questions

Whether you're a speaker, attendee, or sponsor, bring these questions to the table:

• How are you ensuring data trust in your Iceberg setup?
• Do you have visibility into data quality across your pipeline?
• Can you trace data lineage from source to dashboard?
• How do you catch schema changes before they break analytics?
• Are you alerted in real-time when data freshness drops?

These are the questions that separate reactive teams from proactive data leaders.

And if you don't have good answers yet, now's the time to explore Data Observability.

🚀 Try the Interactive Demo

Ready to see Rakuten SixthSense Data Observability in action?

👉 Try the interactive demo today — no forms, no wait.

Explore how we:

• Detect anomalies in Iceberg tables
• Monitor end-to-end pipelines
• Trace lineage from ingestion to insight
• Score data quality in real time

Whether you're modernizing your data stack or scaling it, SixthSense is the observability layer you can rely on.

Final Thoughts

Iceberg Summit 2025 is all about what's next in data. But no matter how innovative your stack becomes, bad data breaks everything.

With Rakuten SixthSense, you get:

• Full-stack Data Observability
• Real-time alerts
• AI-driven prioritization
• A single source of truth for data reliability

Don't let invisible failures undo months of engineering effort.

Trust your data. Empower your teams. Eliminate downtime.

Try our interactive demo today and see how Rakuten SixthSense makes your Iceberg data stack bulletproof.