Scaling a SaaS Analytics Platform: 10x Data Volume Growth
Client Background
Our client is a B2B SaaS company providing analytics and business intelligence tools to mid-market companies. They had grown rapidly from 1,000 to 10,000 customers over 18 months, but their data infrastructure wasn't keeping pace.
The Challenge
- PostgreSQL database as the primary data store
- Application writes directly to the database
- Analytics queries run against the same database
- Basic Redis caching for frequently accessed data
As customer count and data volume grew, they hit critical bottlenecks:
Performance Issues:
- Query latency increased from 200ms to 8+ seconds
- Analytics dashboards were timing out
- Real-time features became near-real-time (30+ second delays)
Scalability Issues:
- Database CPU constantly at 95%+ utilization
- Vertical scaling (bigger instances) provided temporary relief but costs were unsustainable
- Read replicas helped but created data consistency challenges
Business Impact:
- Customer churn increased due to poor performance
- New customer onboarding was delayed
- Engineering team spent 60% of time on performance firefighting
Our Solution
We architected a modern, cloud-native data platform that separated concerns and enabled independent scaling.
Architecture Overview
1. Event-Driven Data Pipeline
- All customer events flow through Apache Kafka topics
- Topics partitioned by customer ID for parallel processing
- Event schema versioning for backward compatibility
Benefits:
- Application performance improved (no longer blocked by analytics queries)
- Multiple consumers can process the same events
- Can replay events for reprocessing or debugging
2. Data Lake for Raw Data
- Parquet format for efficient compression and querying
- Partitioned by date and customer ID
- Lifecycle policies automatically move old data to cheaper storage (Glacier)
Benefits:
- Cost-effective storage for historical data
- Can reprocess historical data with new logic
- Separation of storage and compute
3. Modern Data Warehouse
- Separated compute and storage for independent scaling
- Clustering keys optimized for common query patterns (customer_id, date)
- Materialized views for frequently accessed aggregations
Benefits:
- Query performance improved dramatically
- Can scale compute up/down based on workload
- Pay only for compute time used
4. Processing Layers
Real-Time Processing:
- Kafka Streams for real-time aggregations
- Results stored in Redis for fast dashboard queries
- Updates pushed to Snowflake for historical analysis
Batch Processing:
- Spark jobs (on EMR) run hourly/daily aggregations
- Pre-compute common metrics and dimensions
- Store results in Snowflake for fast querying
5. Data Transformation with dbt
- Raw events → cleaned events
- Cleaned events → aggregated metrics
- Metrics → business KPIs
Benefits:
- Version-controlled transformations
- Automated testing and documentation
- Easy to modify and extend
6. Intelligent Caching
- Redis for hot data (last 24 hours)
- Snowflake materialized views for warm data (last 30 days)
- S3 for cold data (historical)
- Pre-compute metrics for top customers
- Predictable query patterns cached proactively
Technical Implementation
Migration Strategy
We used a dual-write pattern to migrate without downtime:
- Week 1-2: Set up Kafka infrastructure and event schemas
- Week 3-4: Implement dual-write (write to both old DB and Kafka)
- Week 5-6: Build Kafka consumers to populate Snowflake
- Week 7-8: Migrate analytics queries to Snowflake
- Week 9-10: Gradually shift traffic, monitor, decommission old system
Key Technical Decisions
Why Kafka?
- Proven at scale (handles millions of events per second)
- Strong ecosystem and community
- Supports exactly-once semantics
- Can replay events for debugging
Why Snowflake?
- Separated compute and storage
- Excellent performance for analytical queries
- Auto-scaling capabilities
- Strong security and compliance features
Why Spark?
- Industry standard for large-scale data processing
- Can process terabytes of data efficiently
- Rich ecosystem of libraries
- Can run on managed services (EMR)
Results
Performance Improvements
- Query Latency: Reduced by 70% (from 8s average to 2.4s)
- Dashboard Load Time: Improved from timeout to <3 seconds
- Real-Time Features: Latency reduced from 30s to <1s
Scalability Improvements
- Data Processing Capacity: 10x increase (can now handle 100M events/day)
- Concurrent Users: Can handle 5x more concurrent dashboard users
- Data Retention: Can store 2 years of data (up from 3 months)
Cost Improvements
- Infrastructure Costs: Reduced by 40% despite 10x data growth
- Database Costs: Eliminated need for expensive database instances
- Storage Costs: Optimized with lifecycle policies
Business Impact
- Customer Churn: Reduced by 25% (better performance)
- New Customer Onboarding: Time reduced from 2 weeks to 2 days
- Engineering Productivity: Team now spends 20% of time on infrastructure (down from 60%)
Lessons Learned
- Separate concerns early: Don't mix transactional and analytical workloads
- Event-driven architecture scales: Decoupling enables independent scaling
- Right tool for the job: Use data warehouses for analytics, not transactional databases
- Migration can be gradual: Dual-write pattern enables zero-downtime migration
- Monitor everything: Comprehensive observability is essential for distributed systems
Conclusion
By moving from a monolithic database architecture to a modern, cloud-native data platform, we enabled the client to handle 10x data growth while improving performance and reducing costs. The new architecture provides a foundation for continued growth and enables new features that weren't possible before.