Data Ingestion
Moving data from sources to storage systems
🚚 Analogy
The transportation network that moves goods from suppliers to warehouses
Problems Solved
- Reliable data transfer
- Real-time vs batch processing
- Data quality and validation
- Error handling and retry logic
Understanding Data Ingestion
Types of Data Ingestion
Batch Processing
Scheduled jobs that process data in batches at regular intervals
Examples: Nightly ETL jobs, hourly data sync, daily report generation
Best for: Large datasets, cost optimization, scheduled processing
Stream Processing
Continuous flow of data processing as events arrive
Examples: Real-time analytics, fraud detection, live monitoring
Best for: Real-time insights, immediate response, event-driven systems
Change Data Capture
Capturing database changes in real-time
Examples: Database replication, audit logging, sync systems
Best for: Data synchronization, real-time warehousing, microservices
API-based Ingestion
Pulling data from external APIs and services
Examples: CRM data sync, social media analytics, financial data feeds
Best for: SaaS data, third-party integrations, web scraping
Recommended Tools
Apache Kafka
Use case: Event streaming, messaging
Debezium
Use case: Database change capture
Amazon Kinesis
Use case: AWS real-time data
Google Pub/Sub
Use case: Event streaming, messaging
Azure Event Hubs
Use case: Real-time data ingestion
Fivetran
Use case: SaaS data integration
Apache Airflow
Use case: ETL pipeline orchestration
Prefect
Use case: Python-native workflows
Dagster
Use case: Asset governance, lineage