Production-ready ETL/ELT templates and streaming framework comparisons
Ready-to-use pipeline templates for common source-target-scheduler combinations. Updated monthly with latest best practices.
| Source | Target | Scheduler | Pattern | Template |
|---|---|---|---|---|
| PostgreSQL | Snowflake | Airflow | Incremental (CDC) | Download |
| MySQL | BigQuery | Prefect | Full Refresh | Download |
| MongoDB | Redshift | Dagster | Incremental (Timestamp) | Download |
| S3 (JSON) | Snowflake | Airflow | Batch Processing | Download |
| REST API | PostgreSQL | Prefect | API Polling | Download |
| Kafka | BigQuery | Python | Stream Processing | Download |
| Salesforce | Snowflake | Airflow | SaaS Connector | Download |
| CSV Files (S3) | Redshift | Dagster | File Watcher | Download |
| Google Sheets | PostgreSQL | Prefect | Scheduled Sync | Download |
| DynamoDB | S3 (Parquet) | Python | Export & Transform | Download |
Performance benchmarks for Apache Flink, Spark Streaming, and Apache Beam on production workloads (October 2025).
| Use Case | Recommended Framework | Reason |
|---|---|---|
| Real-time analytics with complex event patterns | Apache Flink | Superior CEP capabilities and lowest latency |
| Unified batch and streaming workloads | Spark Streaming | Best ecosystem integration and mature tooling |
| Multi-cloud or portable pipelines | Apache Beam | Runner abstraction supports multiple backends |
| High-throughput with low resource costs | Apache Flink | Most efficient memory usage at scale |
Track time lag between source updates and destination availability
Compare source and target row counts for data completeness
Alert on unexpected schema changes in source systems
Null checks, format validation, and business rule compliance