Building Data Platforms at Billion-Event Scale: Lessons from 10+ Years

The Billion-Event Challenge

Over the past 10+ years, I've built data platforms for luxury brands and financial institutions that process more than a billion events per day. Not millions—billions. Every single day.

These aren't prototype systems or proof-of-concepts. They're production platforms handling real business transactions, customer interactions, and critical operational data where downtime costs millions and data loss is unacceptable.

This post shares the architecture patterns, technology choices, and hard-learned lessons from building systems at this scale.

Why Most Platforms Fail Before Reaching This Scale

Before diving into solutions, let's talk about why most data platforms never reach billion-event scale:

The Prototype Trap

Teams build systems that work perfectly with 1M events. They demo beautifully, stakeholders are happy, and everything looks great. Then you hit 10M events and latency jumps from 100ms to 10 seconds. At 100M events, the system falls over completely.

The problem: They optimized for demo, not production. Every architectural decision assumed small-scale operation.

The Technology Mismatch

I've seen teams use PostgreSQL for event streaming (wrong tool), Snowflake for real-time analytics (wrong use case), and monolithic Python scripts for billion-row transforms (fundamentally unscalable).

The problem: They chose familiar technologies instead of technologies designed for scale.

The "We'll Optimize Later" Fallacy

"Let's get it working first, then optimize for scale." This never works at billion-event scale. By the time you realize you need to optimize, you're rewriting everything.

The problem: Scale isn't an optimization—it's an architecture requirement from day one.

Architecture Patterns That Actually Work

1. Event Streaming as the Foundation

Kafka has become my default choice for any system approaching 100M+ events per day. Not a message queue. Not a database. A distributed log designed specifically for high-throughput event streaming.

Key architecture decisions:

• Topic partitioning: 50-100 partitions per topic for parallel processing
• Consumer groups: Multiple consumer groups for different use cases
• Retention policies: 7-day retention for replay capability
• Replication factor: 3x replication for fault tolerance

Real-world numbers:

• 300M events/day sustained throughput
• Sub-100ms publish latency (p99)
• Zero data loss over 2+ years of operation

2. Separation of Concerns: Hot vs Cold Paths

Mixing real-time and batch processing in the same system is a recipe for disaster at scale.

Hot path (real-time):

• Kafka → Stream processing (Kafka Streams/Flink)
• Sub-second latency requirements
• Simple transformations only
• Direct to real-time dashboards/alerts

Cold path (batch):

• Kafka → Data lake (S3/GCS)
• DBT for complex transformations
• Scheduled processing (hourly/daily)
• Analytics warehouse (BigQuery/Snowflake)

This separation keeps real-time systems fast while allowing complex analytics in batch mode.

3. Data Lake Architecture

S3/GCS as the single source of truth. Everything else is derived.

Structure:

• /raw/: Direct from Kafka, zero transformation
• /staged/: Light cleaning, still immutable
• /processed/: Business logic applied, ready for analytics

Why this works:

• Unlimited scalability (we've stored 500TB+)
• Cost-effective ($0.023/GB/month for GCS)
• Time travel (historical data always available)
• Schema evolution (new fields don't break old data)

4. Compute Separation from Storage

Critical lesson: Never couple compute and storage at billion-event scale.

Bad pattern: Running Spark on HDFS Good pattern: Running Spark on S3/GCS

This separation allows:

• Independent scaling (scale compute for processing, scale storage for retention)
• Cost optimization (shut down compute clusters when not needed)
• Multiple compute engines on same data (Spark, Presto, BigQuery)

Technology Stack: What Actually Works

After building multiple billion-event platforms, here's what I trust in production:

Event Streaming

Primary: Kafka (Confluent Cloud or self-managed) Alternative: Kinesis (if you're all-in on AWS and under 1M events/sec)

Why Kafka wins at scale:

• Battle-tested at LinkedIn, Uber, Netflix
• True distributed log architecture
• Excellent operational tooling
• Replay capability (critical for debugging)

Stream Processing

Primary: Kafka Streams (for simple transformations) Secondary: Apache Flink (for complex event processing)

Why: Both are proven at massive scale. Kafka Streams is simpler for 80% of use cases. Flink handles the complex 20%.

Batch Processing

Primary: DBT + BigQuery (or Snowflake) Alternative: Spark for extremely large transforms

Why DBT won:

• SQL-based (your analysts can contribute)
• Built-in testing and documentation
• Incremental processing (only transform new data)
• Git-based workflow

Storage

Hot storage: BigQuery (queries on recent data) Cold storage: S3/GCS (historical archives) Never: Traditional databases for event storage at this scale

Scaling Strategies: 1M to 1B Events

Phase 1: 1M-10M Events/Day

• Single Kafka cluster (3 brokers)
• Basic partitioning (10-20 partitions per topic)
• Simple stream processing
• Cost: ~$2-3k/month
• Team: 1-2 engineers

Phase 2: 10M-100M Events/Day

• Multi-broker Kafka cluster (6-9 brokers)
• Aggressive partitioning (50+ partitions)
• Dedicated stream processing cluster
• Data lake implementation
• Cost: ~$10-15k/month
• Team: 2-3 engineers

Phase 3: 100M-1B Events/Day

• Multi-region Kafka deployment
• 100+ partitions per topic
• Separate clusters per domain
• Advanced monitoring and alerting
• Dedicated SRE
• Cost: ~$30-50k/month
• Team: 3-5 engineers

Key insight: Cost scales linearly, but architectural complexity increases exponentially. Plan accordingly.

Production Lessons You Can't Learn from Tutorials

Lesson 1: Monitoring is Not Optional

At billion-event scale, you need monitoring before you need the system. If you can't see what's happening, you're flying blind.

Must-have metrics:

• Kafka lag per consumer group
• End-to-end latency (publish to consume)
• Error rates per topic
• Storage growth rates
• Cost per million events

Tools I trust: Prometheus + Grafana, Datadog, Confluent Control Center

Lesson 2: Backpressure Will Kill You

One slow consumer can bring down your entire system. Design for backpressure from day one.

Strategies:

• Independent consumer groups
• Dead letter queues for failed messages
• Circuit breakers for downstream dependencies
• Aggressive timeouts

Lesson 3: Schema Evolution is Harder Than You Think

Adding a field seems simple. At billion-event scale with 2 years of historical data, it's a multi-week migration.

What works:

• Avro for schema registry
• Backward compatibility always
• Plan migrations weeks in advance
• Test on subset of data first

Lesson 4: Cost Optimization is Continuous

At billion-event scale, a 10% inefficiency costs thousands per month.

Regular optimization:

• Partition pruning in queries
• Compression algorithms (Snappy → Zstd saved us 40%)
• Storage tiering (hot → warm → cold)
• Compute right-sizing

Real example: Switching to lifecycle policies on GCS saved $15k/month by automatically moving old data to cheaper storage tiers.

Lesson 5: Team Size Doesn't Scale Linearly

You don't need 50 engineers to handle billion-event systems. You need the right 3-5 engineers who understand distributed systems.

Critical skills:

• Deep understanding of Kafka internals
• Experience with distributed systems debugging
• Production operations mindset
• Cost consciousness

When You Actually Need Billion-Event Scale

Reality check: Most companies don't need billion-event platforms. If you're processing under 10M events/day, simpler architectures work fine.

You need billion-event scale if:

• You're in e-commerce with millions of daily transactions
• You're processing IoT sensor data at scale
• You're in fintech with real-time transaction processing
• You're doing real-time personalization for millions of users

You don't need it if:

• You're a startup with 1000 users
• Your "real-time" can actually be 5-minute batch
• You're still validating product-market fit

Scale the architecture to your actual needs, not your aspirational ones.

Conclusion

Building data platforms at billion-event scale requires different thinking than smaller systems. It's not just "more of the same"—it's fundamentally different architecture patterns, technology choices, and operational approaches.

The key lessons:

1. Architecture for scale from day one (not optimization later)
2. Separate hot and cold paths (real-time vs batch)
3. Choose technologies designed for scale (Kafka, not databases)
4. Monitor everything (you can't fix what you can't see)
5. Plan for failure (it will happen)

If you're building systems at this scale and need help, reach out. I work with select companies that need enterprise-grade platforms without enterprise timelines.

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Abdelkader Bekhti has 10+ years of experience building enterprise-scale data platforms. He's architected systems processing over 1 billion events per day and founded Nestorchat, a suite of production AI assistants. Based in Dubai, he works with select growth companies globally.