data-engineering

name	data-engineering
description	Data pipeline patterns, ETL/ELT best practices, data storage options, and data quality techniques

Data Engineering

Data Pipeline Patterns

Batch Processing

• Scheduled Jobs: Run data processing at fixed intervals (hourly, daily, weekly)
• Use Cases: Historical analysis, reporting, data warehousing
• Tools: Apache Spark, Hadoop, Airflow, dbt
• Design Considerations: Latency tolerance, resource efficiency, cost optimization

Streaming Processing

• Real-time Ingestion: Process data as it arrives with low latency
• Use Cases: Real-time analytics, monitoring, fraud detection
• Tools: Apache Kafka, Apache Flink, Apache Storm, Apache Beam
• Design Considerations: Event ordering, exactly-once semantics, backpressure

Lambda Architecture

• Batch Layer: Store immutable master dataset, compute batch views
• Speed Layer: Process real-time data for low-latency queries
• Serving Layer: Merge batch and real-time views for queries
• Use Cases: Systems requiring both batch and real-time capabilities
• Challenges: Complexity of maintaining two code paths

Kappa Architecture

• Unified Processing: Use a single stream processing framework
• Replay Capability: Reprocess data from the event log
• Use Cases: Simplified architecture when batch is just fast streaming
• Benefits: Reduced complexity, single codebase

ETL/ELT Best Practices

ETL (Extract, Transform, Load)

• Extract: Pull data from source systems with minimal impact
• Transform: Clean, validate, and transform data in a staging area
• Load: Load processed data into the target system
• Best Practices:
  • Minimize source system impact
  • Handle incremental updates efficiently
  • Validate data before loading
  • Document transformation logic

ELT (Extract, Load, Transform)

• Extract: Pull raw data from source systems
• Load: Load raw data into the target system (usually data warehouse)
• Transform: Transform data within the target system using SQL
• Best Practices:
  • Leverage data warehouse compute power
  • Maintain raw data for audit trails
  • Use dbt for transformation orchestration
  • Version control transformation logic

Data Ingestion Patterns

• Full Load: Load entire dataset each time
• Incremental Load: Load only changed records
• Change Data Capture (CDC): Capture data changes in real-time
• Bulk Load: High-volume batch loading for initial loads

Data Storage Options

SQL Databases

• Relational Data: Structured data with relationships
• ACID Compliance: Strong consistency guarantees
• Examples: PostgreSQL, MySQL, SQL Server, Oracle
• Use Cases: Transactional systems, operational data stores

NoSQL Databases

• Document Stores: JSON-like documents (MongoDB, CouchDB)
• Key-Value Stores: Simple key-value pairs (Redis, DynamoDB)
• Column-Family Stores: Wide-column storage (Cassandra, HBase)
• Graph Databases: Relationship-focused (Neo4j, Amazon Neptune)
• Use Cases: Semi-structured data, high scalability, specific data models

Data Lakes

• Raw Data Storage: Store data in native format
• Schema-on-Read: Define schema when reading data
• Examples: AWS S3, Azure Data Lake, Google Cloud Storage
• Use Cases: Data exploration, ML training, archiving

Data Warehouses

• Optimized for Analytics: Columnar storage, compression
• SQL Interface: Familiar query language
• Examples: Snowflake, BigQuery, Redshift, Azure Synapse
• Use Cases: Business intelligence, reporting, analytics

Data Quality and Validation

Data Quality Dimensions

• Completeness: No missing values or records
• Accuracy: Data reflects real-world values
• Consistency: No conflicting data across sources
• Timeliness: Data is up-to-date
• Validity: Data conforms to defined rules and formats
• Uniqueness: No duplicate records

Validation Techniques

• Schema Validation: Check data types, formats, and constraints
• Range Checks: Verify values fall within expected ranges
• Pattern Matching: Use regex for format validation (email, phone, etc.)
• Referential Integrity: Validate foreign key relationships
• Business Rules: Apply domain-specific validation logic

Data Profiling

• Statistical Analysis: Understand data distributions and patterns
• Pattern Discovery: Identify data formats and structures
• Anomaly Detection: Find outliers and unusual values
• Dependency Analysis: Discover relationships between fields

Data Lineage

• Source Tracking: Trace data back to original sources
• Transformation Tracking: Document all transformations applied
• Impact Analysis: Understand downstream effects of changes
• Compliance: Meet regulatory requirements for data tracking