Databricks System Tables Overview — Usage, Billing & Audit Data

Imagine having a command center for your Databricks workspace — a place where every job, cluster, query, and billing detail is logged, monitored, and actionable.
This is the power of Databricks System Tables.

In this guide, we’ll explore what system tables are, how they can help you monitor usage, track billing, and audit actions across your workspace — all in a professional yet easy-to-understand narrative.

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🏛️ What Are Databricks System Tables?

System tables are pre-defined, managed tables in Databricks that store workspace metadata.
They provide a structured view of:

• Job and cluster usage
• SQL query history
• Billing and cost allocation
• Audit and security events

These tables are usually accessible via Databricks SQL, Notebooks, or API integrations, enabling teams to make informed, data-driven decisions about cost, performance, and compliance.

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🔍 Key Categories of System Tables

1️⃣ Usage Data

Tracks how clusters, jobs, and queries are utilized:

• Clusters: uptime, auto-scaling events, node usage
• Jobs: start/end times, success/failure, resource consumption
• SQL Queries: execution time, affected tables, query owner

Benefit: Identify idle clusters, optimize workloads, and reduce costs.

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2️⃣ Billing & Cost Data

Provides detailed cost attribution:

• Compute cost per cluster or job
• Storage cost per database or table
• Cost trends over days, weeks, months

Benefit: Track spending, forecast budgets, and enforce cost accountability across teams.

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3️⃣ Audit & Security Data

Captures who did what and when:

• User actions on clusters, jobs, and notebooks
• Access changes and permission modifications
• API activity logs

Benefit: Ensure compliance, investigate incidents, and maintain secure governance.

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🚀 How to Access Databricks System Tables

1. Using Databricks SQL

SELECT *
FROM system.information_schema.jobs
WHERE creation_time >= CURRENT_DATE - INTERVAL 30 DAYS;

2. Using Notebooks (Python / PySpark)

df = spark.sql("SELECT * FROM system.information_schema.clusters")
df.show()

3. APIs and Dashboards

• Databricks REST API exposes usage and audit metrics
• System tables can feed dashboards for finance, governance, or operations teams

Pro Tip: Combine usage and billing tables to build a cost optimization dashboard.

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🧩 Story: How a Team Uses System Tables

Meet Ravi, a cloud operations manager. His organization noticed a spike in monthly Databricks costs.

Without System Tables:

• Manual logging of jobs and clusters
• Cost attribution was slow and error-prone

With System Tables:

• Queried cluster usage for idle nodes
• Tracked job runtimes and SQL query costs
• Generated automated cost reports for finance

Result: 20% reduction in cloud spend and faster incident investigations.

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📌 Best Practices for System Tables

1. Automate monitoring – Use scheduled queries for usage, cost, and audit metrics
2. Integrate with BI tools – Power BI, Tableau, or Looker can visualize trends
3. Combine datasets – Usage + billing + audit tables for comprehensive governance
4. Maintain historical snapshots – Helps with forecasting and compliance

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🏁 Summary

Databricks System Tables are your workspace’s telemetry center:

• Track resource usage efficiently
• Control costs and forecast budgets
• Maintain security and audit compliance

By leveraging system tables, teams move from reactive management to proactive workspace optimization.

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📌 Continue to Next Topic

👉 Databricks Admin Console — Workspace Management Essentials