Google Cloud Unifies Logs & Traces with SQL-Powered Observability Analytics

Google Cloud just dropped a big update: Log Analytics is now Observability Analytics, and it brings logs and traces together under one SQL-powered roof. If you’ve been wrestling with separate tools for logs and traces, this is like finding a calm, deep pool in the middle of a swamp—everything you need is right there.

What’s New?

• Observability Analytics (formerly Log Analytics) now includes trace data, generally available.
• The Observability API for management and configuration is also GA.
• You can now run SQL queries that join logs and traces in a single view.

This means you can finally answer questions like “Which checkout requests took longer than 5 seconds?” or “Which AI agent tool calls fail most often?” without hopping between consoles. It’s like having a capybara guide you through the reeds—smooth and efficient.

Why It Matters

For developers and SREs, the ability to correlate logs and traces with SQL is a game-changer. You can now:

• Unify telemetry: JOIN high-volume log and trace data in one place.
• Correlate with business data: Join observability datasets with BigQuery tables (e.g., conversion rates) to quantify business impact.
• Analyze in-place: No need to export data—query it where it lives, reducing costs and complexity.

Think of it as turning your observability data into a searchable, analytical powerhouse. No more swimming through murky waters to find the root cause.

Real-World Use Cases

AI Agent Optimization

AI agents often call multiple tools. With Observability Analytics, you can run aggregate queries across millions of spans to find failure rates and P95 latency per tool. For example:

SELECT tool_name, failure_rate_percentage, p95_latency_ms FROM ...

Then drill down into logs to see the exact LLM prompt that caused the failure. It’s like having a caiman’s eye view of your system’s health.

Customer Latency Impact

If you don’t propagate customer IDs in traces but log them in access logs, you can join traces and logs to find which customers are suffering. Query example:

SELECT customer_id, p95_latency_ms FROM ...

This turns raw telemetry into actionable business intelligence—no more guessing which users are affected.

How It Compares to Existing Tools

Cloud Logging and Trace explorers are still there for investigating individual issues. Observability Analytics is for aggregation and deep analysis—answering broad questions like “What’s the P95 latency for my checkout service this week?”

It’s the difference between looking at a single lily pad and surveying the whole swamp.

Get Started

Head over to the Observability Analytics page in the Google Cloud console. Enable the Observability API to unlock management capabilities. For more query examples, check out the GitHub repo.

If you’re exploring observability platforms, check out our reviews of Google Cloud, Supabase, and Neon Database for more insights.

Original announcement published on Google Cloud.