Building a Petabyte-Scale Log Analytics Platform on AWS

In modern distributed systems, logs are more than just debugging artifacts - they are the lifeblood of observability. As organizations scale into hundreds of microservices, millions of events per second, and multi-region architectures, traditional logging stacks struggle to keep up.

That’s where a Petabyte-Scale Log Analytics Platform becomes essential.

In this post, we break down a production-grade architecture built using:

• Amazon S3 as an infinitely scalable data lake
• OpenSearch for fast, real-time search
• AWS serverless + managed services for cost-efficient ingestion
• Terraform for complete Infrastructure-as-Code

This blog is based on the project “log-analytics-petabyte-scale”, which provides the foundation for an enterprise-grade log analytics solution.

Why Build Log Analytics at Petabyte Scale?

Modern organizations need to analyze logs for:

• Real-time monitoring
• Security events and threat detection
• Compliance audits
• Troubleshooting production issues
• Business analytics (KPIs hidden in logs)

But storing logs in traditional databases becomes expensive and slow.

So we combine:

• Hot tier: OpenSearch → fast search
• Cold tier: Amazon S3 → cheap long-term storage

This architecture allows you to Search today’s logs in milliseconds, and search last year’s logs for pennies.

High-Level Architecture

Let’s start with the macro view - how logs flow from applications to storage and analytics tools.

High-Level Diagram

This architecture separates real-time search from long-term retention, keeping the system fast and cost-efficient.

Component Breakdown (Explained Simply)

1. Log Ingestion Layer

Logs originate from microservices, servers, containers, or on-prem systems.
Tools like FluentBit, Filebeat, or CloudWatch Logs forward them to:

→ Kinesis Data Streams

• Handles millions of log events per second
• Fully managed
• Durable and fault-tolerant

→ Firehose Delivery Stream

• Optional transformation (JSON → structured logs)
• Batching & compression (GZIP, Parquet)
• Automatic delivery to S3 and OpenSearch

2. S3 Data Lake - The Heart of Long-Term Storage

S3 acts as the ultimate log archive.
Benefits:

• Infinite scalability
• Lifecycle policies (move data to Glacier after X days)
• Cheap ($0.021/GB → Petabyte storage for low cost)
• Searchable Snapshots integrate directly with OpenSearch

S3 Folder Structure Example:

s3://logs-bucket/
    raw/
        year=2025/month=01/day=28/
    processed/
        year=2025/month=01/day=28/
    archive/

3. OpenSearch - Real-Time Log Search

OpenSearch gives you:

• Fast querying
• Kibana-style dashboards
• Full-text search
• Aggregation analytics

To manage petabytes cost-effectively, we use tiered storage:

Hot Tier: SSD-backed nodes (fast, expensive)
Warm Tier: Slower storage nodes
Cold Tier: Searchable snapshots stored in S3

This drastically reduces cost while keeping all data searchable.

4. Athena + Glue - SQL on Top of S3

For historical analysis:

• Glue crawlers create table metadata
• Athena queries logs directly in S3
• No servers, no clusters, no maintenance

Example queries:

SELECT *
FROM logs_processed
WHERE status = '500'
  AND service = 'payment-service'
  AND date >= current_date - interval '30' day;

This complements OpenSearch by providing long-range, cost-efficient analytics.

5. Infrastructure as Code - Terraform

Your project contains the Terraform skeleton that will eventually create:

• S3 buckets
• OpenSearch cluster
• Kinesis + Firehose
• IAM roles
• VPC networking
• Monitoring & alerts

Terraform ensures:

• Repeatable deployments
• Multi-environment support (dev, staging, prod)
• Version-controlled infrastructure
• Easy rollback and upgrade paths

Putting It All Together

Here’s the full flow in a clean, simple way:

Data Flow Diagram

This sequence shows the step-by-step lifecycle of log data from generation, ingestion, routing, indexing, and storage, to historical querying through Athena.

Why This Architecture Works at Petabyte Scale

Cost Optimization Strategies

• Use S3 Intelligent-Tiering for dynamic savings
• Move logs from hot → warm → cold → frozen tiers
• Batch logs in Firehose (reduces OpenSearch cost)
• Compress logs using GZIP or Parquet
• Use ARM instances (Graviton) for OpenSearch
• Offload historical queries to Athena

This architecture is built to be scalable, fast, and budget-friendly.

Current Status of the Project

• README contains complete conceptual design
• Terraform project is initialized
• Documentation placeholders are ready (ARCHITECTURE.md, SECURITY.md, etc.)
• Next step: Implement infrastructure modules

The project is intentionally starting as a blueprint, meant to expand into a full enterprise-ready platform.

What’s Coming Next?

Over the next iterations, this project will add:

• Terraform modules for all major components
• Automated ingestion pipeline
• Monitoring + dashboards
• Security hardening
• CI/CD pipeline for infra deployments
• End-to-end samples and test logs

If you follow this architecture, you’ll be able to build your own high-throughput, petabyte-scale log analytics platform on AWS.

Final Thoughts

Building a logging platform at petabyte scale is no longer optional - it’s a requirement for modern cloud-native systems. The combination of OpenSearch, S3, Kinesis, and serverless compute provides an elegant and powerful solution.
This project is the groundwork for an enterprise-class observability platform:

• Scalable
• Cost-efficient
• Secure
• Built entirely on AWS managed services
• 100% Infrastructure-as-Code

As the Terraform implementation evolves, this platform will become a complete production-ready solution.

Project Repository

GitHub Repo: https://github.com/rahulladumor/log-analytics-petabyte-scale

Project Author

Author: Rahul Ladumor
Portfolio: https://acloudwithrahul.in
GitHub: https://github.com/rahulladumor
LinkedIn: https://linkedin.com/in/rahulladumor
Email: rahul.ladumor@infratales.com