⏱️ Real-time Monitoring

⏱️ Real-time Monitoring#

Real-time data quality monitoring presents unique challenges that don’t exist in batch processing. Stream DaQ handles these complexities automatically while giving you control over the trade-offs.

Key Challenges#

Late Arrivals

Data doesn’t always arrive in perfect order. Network delays, processing bottlenecks, and system failures can cause events to arrive late.

Processing Time vs Event Time

The time when data is processed differs from when events actually occurred. Stream DaQ uses event time for accurate windowing.

Watermarks

Mechanisms to handle late data by defining how long to wait for delayed events before finalizing window results.

Backpressure

When data arrives faster than it can be processed, systems need strategies to handle the overflow.

Stream DaQ’s Approach#

Automatic Late Data Handling

# Configure tolerance for late arrivals
daq.configure(
    wait_for_late=30,  # Wait 30 seconds for late data
    time_column="event_timestamp"  # Use event time, not processing time
)

Flexible Watermarking

Stream DaQ automatically manages watermarks based on your wait_for_late setting. You don’t need to configure complex watermark strategies.

Built-in Backpressure Management

The system automatically handles varying data rates without manual intervention.

Trade-offs to Consider#

Latency vs Completeness

  • Lower wait_for_late = faster results, might miss late data

  • Higher wait_for_late = more complete results, slower processing

Memory vs Accuracy

  • Larger windows = more memory usage but better statistical accuracy

  • Smaller windows = less memory but potentially noisier results

Best Practices#

  1. Start with reasonable defaults: wait_for_late=30 works for most use cases

  2. Monitor your data patterns: Understand typical arrival delays in your system

  3. Use appropriate window sizes: Match window duration to your data frequency

  4. Test with realistic data: Validate behavior under actual load conditions

Next Steps#

Contents