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Data Ingestion in Machine Learning
Data ingestion is the process of importing and moving data from various sources into a system where it can be stored, processed, and eventually used to train machine learning models.
Types of Data Ingestion
| Type | Description |
|---|---|
| Batch Ingestion | Data is collected and loaded at scheduled intervals |
| Streaming Ingestion | Data flows continuously in real time |
| Hybrid Ingestion | Combines batch and streaming depending on the source |
Common Data Sources for ML
- Relational Databases: Structured transactional data.
- APIs: Third-party or internal service data.
- Log Files: Application and server event logs.
- Message Queues: Kafka, RabbitMQ for real-time event streams.
- File Storage: CSV, JSON, Parquet files in cloud storage.
Key Ingestion Tools
- Apache Kafka: High-throughput distributed streaming platform.
- Apache NiFi: Automates data flow between systems.
- Airbyte/Fivetran: Managed connectors for common data sources.
- Cloud-native tools: AWS Glue, Google Dataflow, Azure Data Factory.
Best Practices
- Validate data schema at the point of ingestion.
- Log ingestion failures for monitoring and debugging.
- Design for idempotency so re-running ingestion doesn't create duplicates.
- Separate raw ("bronze") data from cleaned, model-ready ("gold") data.
Key Takeaway: Reliable data ingestion is the foundation of any ML pipeline — if data doesn't arrive consistently and correctly, nothing downstream can be trusted.
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