📚 Concepts

📚 Concepts#

Welcome to the conceptual heart of Stream DaQ! Understanding these core concepts will help you design effective data quality monitoring for any streaming scenario.

Why concepts matter

Stream processing and data quality monitoring have unique challenges that don’t exist in batch processing. These concepts will help you think in “streaming mode” and design monitoring that actually works in real-time environments.

🎯 Data Quality for Streams

Why streaming data quality is different - Understand the unique challenges and dimensions of quality in unbounded data streams.

🌊 Stream-first Data Quality
🪟 Stream Windows

Bounded computations over unbounded streams - Learn how tumbling, sliding, and session windows make infinite data streams manageable.

🪟 Stream Windows
📏 Measures & Assessments

Building blocks of quality checks - Discover how measures extract insights and assessments determine pass/fail criteria.

Measures and Assessments
⚡ Real-time Monitoring

Stream processing principles - Understand late arrivals, watermarks, and how Stream DaQ handles the complexity of real-time data.

⏱️ Real-time Monitoring

The Big Picture#

Data quality monitoring in streaming environments involves four key concepts working together:

Stream DaQ concepts overview diagram

Fig. 1 How Stream DaQ concepts work together#

  1. Your streaming data flows continuously into Stream DaQ

  2. Windows group data into manageable time-bounded chunks

  3. Measures extract meaningful metrics from each window

  4. Assessments evaluate whether those metrics meet your quality standards

  5. Real-time results flow out as a quality monitoring stream

From Batch to Stream Thinking#

If you’re coming from batch data quality monitoring, here are the key mindset shifts:

Table 1 Batch vs Stream Quality Monitoring#

Aspect

Batch Processing

Stream Processing

Data Scope

Complete dataset

Continuous windows

Quality Assessment

After data is complete

As data arrives

Time Handling

Data is “already there”

Must handle late/out-of-order data

Results

Single quality report

Continuous quality stream

Action

Reprocess if needed

Alert and adapt in real-time

Learning Path#

We recommend exploring these concepts in order:

1. Start with Data Quality (🌊 Stream-first Data Quality)

Understanding what makes streaming data quality unique sets the foundation for everything else.

2. Master Windows (🪟 Stream Windows)

Windows are the key abstraction that makes infinite streams manageable. Get this right, and everything else follows.

3. Build with Measures & Assessments (Measures and Assessments)

Learn the building blocks you’ll use to construct your quality monitoring.

4. Deploy with Real-time Principles (⏱️ Real-time Monitoring)

Understand the production considerations for reliable streaming quality monitoring.

Real-World Application#

Each concept page includes:

  • 🎯 Core theory - What you need to understand

  • 💡 Practical examples - How it applies to real scenarios

  • 🔧 Stream DaQ implementation - How to use these concepts with our API

  • ⚠️ Common pitfalls - What to watch out for

  • 🔗 Related examples - Links to complete use cases

Quality Monitoring Patterns#

As you explore these concepts, you’ll start recognizing common patterns:

Volume Monitoring

Track data arrival rates, detect drops or spikes in volume

Value Validation

Ensure data values fall within expected ranges and formats

Freshness Checking

Monitor data timeliness and detect stale or delayed data

Consistency Verification

Check relationships between fields and detect anomalies

Completeness Assessment

Identify missing data, null values, and incomplete records

Ready to dive deeper? Start with 🌊 Stream-first Data Quality to understand why streaming data quality monitoring is a unique challenge that requires specialized approaches.

Made with ❤️ by the Stream DaQ team at Datalab AUTh

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