Ingestion Architecture

Ingestion Architecture¶

How ingestion fits into your overall data architecture.

Overview¶

Ingestion architecture defines how data flows from source systems into your platform. It's the foundation that everything else builds on.

Architecture Layers¶

graph TB
    A[Source Systems] --> B[Ingestion Layer]
    B1[Batch] --> B
    B2[Streaming] --> B
    B3[CDC] --> B

    B --> C[Raw Storage<br/>Data Lake]
    C --> D[Transformation Layer]
    D --> E[Curated Storage<br/>Lakehouse/Warehouse]
    E --> F[Serving Layer]
    F1[Analytics] --> F
    F2[ML Models] --> F
    F3[APIs] --> F

    style A fill:#e3f2fd
    style B fill:#80deea
    style C fill:#b2dfdb
    style D fill:#80deea
    style E fill:#b2dfdb
    style F fill:#e3f2fd

Complete data flow from source systems to consumption.

Ingestion Patterns¶

Pattern 1: Batch Ingestion¶

Architecture:

Source → Scheduled Job → Raw Storage (Parquet)

Characteristics: - Scheduled execution (hourly, daily) - Full or incremental extracts - Higher latency (minutes to hours) - Lower cost per GB

Use when: - Historical loads - Large volumes - No real-time requirement

Pattern 2: Streaming Ingestion¶

Architecture:

Source → Message Queue (Kafka) → Stream Processor → Raw Storage

Characteristics: - Continuous processing - Low latency (seconds to minutes) - Higher cost per GB (3-5x batch) - More complex failure handling

Use when: - Real-time requirements - Event-driven architecture - Low-latency use cases

Pattern 3: Change Data Capture (CDC)¶

Architecture:

Database → Transaction Log → CDC Tool → Message Queue → Storage

Characteristics: - Captures inserts, updates, deletes - Maintains transaction consistency - Lower overhead than full extracts - Real-time or near real-time

Use when: - Database replication - Maintaining current state - Audit trails

Storage Architecture¶

Raw Layer Design¶

Purpose: Preserve source data exactly as received

Design principles: - Immutable - Never modify raw data (append-only) - Schema-on-read - Store in flexible formats - Partitioned - By ingestion time, source - Long retention - 7 years for compliance

Format: Parquet (analytics), Avro (streaming), JSON (flexible)

Example structure:

raw/
  source=web_events/
    date=2024-01-15/
      hour=10/
        data.parquet

Curated Layer Design¶

Purpose: Cleaned, validated, enriched data

Design principles: - Schema-on-write - Enforced schemas - Partitioned - By business keys - Optimized - For query patterns - Versioned - Track changes over time

Format: Delta Lake, Iceberg, Parquet

Example structure:

curated/
  events/
    date=2024-01-15/
      data.delta

Scalability Patterns¶

Horizontal Scaling¶

Multiple ingestion workers: - Partition sources across workers - Each worker handles subset of sources - Scale workers based on load

Vertical Scaling¶

Larger instances: - More CPU/memory per worker - Handle larger sources - Better for single large sources

Hybrid Approach¶

Combine both: - Horizontal for many small sources - Vertical for large sources

Error Handling¶

Retry Strategy¶

Exponential backoff:

max_retries = 5
base_delay = 1  # seconds

for attempt in range(max_retries):
    try:
        ingest(record)
        break
    except TransientError:
        if attempt < max_retries - 1:
            delay = base_delay * (2 ** attempt)
            sleep(delay)
        else:
            send_to_dlq(record)  # Dead letter queue

Dead Letter Queue (DLQ)¶

Purpose: Store records that failed after all retries

Implementation: - Separate storage (S3, BigQuery table) - Alert on DLQ size - Manual review and reprocessing

Monitoring¶

Key Metrics¶

Volume: - Records/second - GB/day - Partition count

Latency: - End-to-end latency - Processing time per record - Queue depth

Quality: - Schema validation failures - Duplicate rate - Missing data rate

Reliability: - Success rate - Error rate by type - DLQ size

Cost Optimization¶

Common Cost Traps¶

Over-ingestion - Ingesting unused data
Inefficient formats - JSON instead of Parquet
Redundant ingestion - Multiple pipelines for same source
Streaming when batch would suffice - 3-5x cost premium

Optimization Techniques¶

Compression - Use Snappy or Zstd (2-5x reduction)
Partitioning - Only process new partitions
Incremental loads - Only fetch changed data
Lifecycle policies - Move old data to cheaper storage

Agentic Controls & Data Zones in Ingestion Architecture¶

Self-Serve Contracts¶

Architecture pattern:

Ingestion platforms evolve to support contract-first, self-serve pipeline creation:

graph LR
    A[Contract Definition] --> B[Pipeline Generation]
    B --> C[Resource Provisioning]
    C --> D[Monitoring Setup]
    D --> E[Pipeline Active]

    F[Schema Registry] -.Validates.-> A
    G[Policy Engine] -.Enforces.-> B

    style A fill:#80deea
    style B fill:#80deea
    style C fill:#b2dfdb
    style D fill:#90caf9
    style E fill:#c8e6c9

Implementation: - Contract stored in schema registry - Pipeline templates for common patterns - Automated resource provisioning - Standard monitoring and alerting

Impact: - Time to value: Hours instead of weeks - Consistency: Standard patterns enforced - Quality: Contracts prevent issues

Autonomous Error Detection¶

Architecture pattern:

Ingestion systems detect and respond to errors autonomously:

Error detection: - Real-time monitoring of pipeline health - Pattern recognition for common failures - Anomaly detection for unusual behavior

Autonomous response: - Automatic retry with backoff - Root cause analysis - Preventive actions - Escalation when needed

Example flow:

Error Detected
    ↓
Pattern Matching (network timeout)
    ↓
Automatic Retry (exponential backoff)
    ↓
Success → Continue
Failure → Escalate

Policy-Gated Control Planes¶

Architecture pattern:

Ingestion control planes enforce policies automatically:

Policy types: - Schema policies - Enforce contract compliance - Cost policies - Prevent cost overruns - Quality policies - Enforce quality standards - Security policies - Access control, encryption

Enforcement: - Policies defined as code - Automatic validation at ingestion boundary - Rejection of non-compliant data - Alerting on policy violations

Data Zones in Ingestion Flows¶

Zone-based ingestion architecture:

graph TB
    A[Source Systems] --> B[Ingestion Layer]
    B --> C[Raw Zone<br/>Immutable<br/>Long Retention]
    C --> D[Curated Zone<br/>Validated<br/>Enriched]
    D --> E[Processed Zone<br/>Aggregated]
    D --> F[Feature/AI Zone<br/>ML-Ready]

    G[Contracts] -.Govern.-> B
    H[Policies] -.Enforce.-> C
    I[Quality Checks] -.Validate.-> D

    style C fill:#b2dfdb
    style D fill:#80deea
    style E fill:#90caf9
    style F fill:#64b5f6

Zone characteristics:

Raw Zone ingestion: - Minimal transformation - Schema-on-read - Long retention - Immutable storage

Curated Zone ingestion: - Quality validation - Schema enforcement - Enrichment - Optimized formats

Impact on pipeline design: - Clear boundaries between zones - Zone-specific transformation logic - Zone-appropriate storage formats - Zone-specific lifecycle policies

Impact on Observability¶

Zone-aware observability:

Raw Zone: Ingestion metrics, schema validation, volume
Curated Zone: Quality scores, freshness, completeness
Processed Zone: Query performance, usage patterns
Feature Zone: Serving latency, feature freshness

Lineage tracking: - Zone-to-zone data flow - Transformation lineage - Ownership tracking - Impact analysis

Impact on Lineage¶

Zone-based lineage:

Source → Raw Zone → Curated Zone → Processed Zone
                          ↓
                    Feature Zone

Benefits: - Clear data flow visualization - Zone-specific impact analysis - Ownership clarity - Compliance documentation

Data Ingestion - Ingestion patterns
Storage - Storage design
Data Processing - Processing ingested data
Platform Strategy - Strategic direction

Next: Data Orchestration →

Ingestion Architecture