Data Engineering Guide

Appearance

Data Quality 🧪 (Trust in Data Systems) ​

Data quality ensures that data is accurate, complete, consistent, and reliable before it is consumed.

🧠 If pipelines move data, data quality ensures the data is correct enough to trust.

🎯 Why Data Quality Matters ​

Even perfectly running pipelines can produce bad data.

Without data quality:

  • Dashboards show incorrect metrics
  • ML models get corrupted inputs
  • Business decisions become unreliable
  • Financial reports become inaccurate

🧭 Dimensions of Data Quality ​

1. Accuracy ​

Does the data correctly represent reality?

Example:

  • wrong price in transactions → incorrect revenue

2. Completeness ​

Are all required fields present?

Example:

  • missing user_id in logs

3. Consistency ​

Is data consistent across systems?

Example:

  • same user has different names in different tables

4. Validity ​

Does data follow expected rules?

Example:

  • negative age values
  • invalid timestamps

5. Uniqueness ​

Are there duplicate records?

Example:

  • duplicate transaction entries

⚙️ Where Data Quality is Applied ​

1. Ingestion Layer ​

  • schema validation
  • format checks
  • null checks

2. Processing Layer ​

  • transformation validation
  • aggregation correctness
  • deduplication

3. Storage Layer ​

  • constraint enforcement
  • uniqueness checks
  • referential integrity

🧱 Data Quality Architecture ​

Source → Validation → Processing → Quality Checks → Storage

🔍 Common Data Quality Checks ​

1. Schema Validation ​

Ensures data structure is correct:

  • column types
  • required fields
  • format consistency

2. Null Checks ​

Detect missing critical values:

  • user_id
  • transaction_id
  • timestamps

3. Range Checks ​

Ensure values are valid:

  • age > 0
  • price >= 0

4. Duplicate Detection ​

Identify repeated records:

  • event_id based deduplication
  • composite key checks

5. Freshness Checks ​

Ensure data is up-to-date:

  • last updated timestamp
  • ingestion lag monitoring

⚡ Data Quality in Batch vs Streaming ​

Batch Systems ​

✔ Easier validation
✔ Full dataset available
✔ Strong consistency checks

Streaming Systems ​

❌ Harder validation
❌ Continuous validation required
❌ Late data complicates checks

🧠 Tools for Data Quality ​

  • Great Expectations
  • Deequ (AWS)
  • Apache Griffin
  • Custom Spark validations
  • dbt tests

🚨 Common Data Quality Failures ​

  • Silent schema changes
  • Missing upstream data
  • Duplicate ingestion
  • Partial pipeline execution
  • Late arriving inconsistent data

🔗 How Data Quality Connects ​

  • Pipelines → enforce correctness in flow
  • Processing → validates transformations
  • Storage → enforces constraints
  • System Design → ensures reliability layer
  • Advanced Concepts → depend on correctness guarantees

🎯 Goal of Data Quality ​

You should be able to:

  • Design validation layers in pipelines
  • Detect and handle bad data
  • Prevent incorrect analytics
  • Build monitoring systems
  • Ensure trustworthy data systems

🔥 Interview Insight ​

If you talk about data quality clearly:

You show real production experience beyond just coding pipelines

💡 Mental Model ​

Think of data quality as:

“The immune system of a data platform — it detects and blocks bad data before it spreads.”

“A data pipeline is only as good as the quality of data it allows through.”