SYSTEM_STATUS: ONLINE

Engineered for Scale

A look inside the streaming engine that powers Content Atlas.
Cloud-native, event-driven, and built to handle data at petabyte scale.

Data Ingestion Pipeline

Source

S3 Bucket

Raw files land here. S3 Events trigger the pipeline instantly via Webhooks.

Ingestion Node

Stream Reader

Python generators read files in 50MB chunks. RAM usage remains constant regardless of file size.

Processing Grid

Async Workers

Schema Validation
Type Casting
Hash Deduplication

Storage

Postgres

Clean, structured data is committed in batches. Ready for SQL or API query.

Zero-Copy Streaming

Traditional ETL tools load entire files into memory, causing crashes with datasets larger than available RAM. Content Atlas uses a streaming architecture. We pull data from S3 in buffered streams, meaning a 10GB CSV file consumes the same 512MB of RAM as a 1MB file during processing.

# Pseudo-code logic

def process_stream(s3_object):
  with smart_open(s3_object) as stream:
    for chunk in stream.read_chunks(size=1024*1024):
      worker_queue.push(chunk) // Non-blocking

Private AI Context

When you use the AI Assistant to map columns or query stats, we never send your raw row data to the LLM. We generate a statistical metadata summary (column names, types, sample variance) and send only that context. Your customer PII never leaves your isolated environment.

# What the LLM sees

{
  "table": "clients",
  "columns": ["id", "email", "rev"],
  "meta": { "email_is_pii": true }
}

Defense-in-Depth Security

Encryption

AES-256 encryption at rest for all Postgres tables and S3 buckets. TLS 1.3 for all data in transit.

Network Isolation

Processing workers run in isolated VPC subnets with no public internet access, communicating only via internal queues.

RBAC

Granular role-based access control. Define exactly who can trigger imports, view schemas, or access PII columns.