Building Real-Time Data Pipelines at Scale

When we first started building DataForge, our streaming engine could handle about 10,000 events per second. Fast enough for a demo, nowhere near enough for production workloads. Today, we process over 2 million events per second for customers like Stripe, Shopify, and Airbnb. This is the story of how we got there.

The Architecture Challenge

Real-time data pipelines have fundamentally different requirements than batch processing. You need guaranteed ordering, exactly-once semantics, and the ability to handle backpressure gracefully. Most importantly, you need to do all of this with sub-100ms end-to-end latency.

"The biggest mistake teams make with streaming is treating it like batch processing with shorter intervals. It's a fundamentally different paradigm that requires different thinking."

Our Three-Layer Approach

We settled on a three-layer architecture that separates concerns while maintaining low latency:

Ingestion Layer

Handles initial event capture with automatic schema detection and validation at wire speed.

Processing Layer

Transforms, enriches, and routes events using a DAG-based execution engine.

Delivery Layer

Guarantees exactly-once delivery to 200+ destinations with automatic retry and DLQ.

Code Example: Defining a Pipeline

Here's how you define a real-time pipeline in DataForge using our declarative YAML config:

pipeline:
  name: user-events-streaming
  mode: real-time

source:
  type: kafka
  config:
    brokers: ${KAFKA_BROKERS}
    topic: user-events
    group: dataforge-pipeline

transforms:
  - type: filter
    condition: event.type IN ('click', 'purchase')
  - type: enrich
    lookup: user_profiles
    key: event.user_id

sink:
  type: snowflake
  config:
    warehouse: ANALYTICS_WH
    database: EVENTS
    schema: STREAMING

Performance Results

After implementing this architecture, we saw dramatic improvements across all key metrics:

2M+

Events/sec

<50ms

P99 Latency

99.99%

Uptime

0

Data Loss Events

Key Takeaways

Separate concerns early. The three-layer approach let us scale each component independently.
Backpressure is your friend. Proper backpressure handling prevented cascading failures during traffic spikes.
Test with production-scale data. Synthetic benchmarks don't reveal the bugs that real-world data distributions expose.
Monitor everything. Every stage of the pipeline emits metrics. You can't optimize what you can't measure.

If you're building real-time data pipelines and want to skip the months of infrastructure work, try DataForge free. We've done the hard engineering so you don't have to.