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Data Pipelinescomplex complexity
Data Pipeline Architecture
Scalable data pipeline for ingestion, processing, and analytics with stream and batch capabilities.
Architecture
System Components
Key building blocks of this architecture, layered from infrastructure up
01
Data Ingestion
High-throughput event ingestion with schema validation. See the event-driven playbook.
KafkaSchema RegistryAPI Gateway
02
Stream Processing
Real-time data transformation and enrichment.
FlinkKafka StreamsksqlDB
03
Data Warehouse
Analytical data storage with fast query performance - common in finance.
ClickHouseSnowflakeBigQuery
04
Orchestration
Workflow orchestration for batch processing jobs.
AirflowDagsterPrefect
05
Data Quality
Monitoring and alerting for data quality issues - pair with monitoring.
Great Expectationsdbt testsAnomaly Detection
Planning
Key Considerations
Important factors to keep in mind when implementing this architecture
Design for exactly-once processing semantics where required
Implement data lineage tracking for compliance and debugging
Plan for schema evolution with backwards compatibility
Start a project for a data architecture review.
Options
Alternatives to Consider
Other approaches that might fit your specific needs
Fivetran for managed data integration
dbt Cloud for managed transformations
Databricks for unified analytics platform
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