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Batch Processing in ML Systems

Published: November 2025 | 15 min read

Understanding Batch Processing

Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected over time and processed together. In ML systems, batch processing is crucial for handling large datasets that don't require real-time processing.

Key Components

  1. Data Collection
  2. Scheduled data pulls from various sources
  3. Database dumps and exports

  4. File-based data ingestion (CSV, JSON, Parquet, etc.)

  5. Processing Frameworks

  6. Apache Spark
  7. Apache Flink
  8. Apache Beam

  9. Hadoop MapReduce

  10. Storage Solutions

  11. Data lakes (S3, Azure Data Lake, GCS)
  12. Data warehouses (BigQuery, Redshift, Snowflake)
  13. Distributed file systems (HDFS)

Best Practices

  • Idempotency: Ensure operations can be retried without side effects
  • Monitoring: Track job status, resource usage, and data quality
  • Error Handling: Implement robust error handling and retry mechanisms
  • Scalability: Design for horizontal scaling to handle growing data volumes
  • Cost Optimization: Optimize for cost by right-sizing resources and scheduling during off-peak hours

Common Use Cases

  • Training machine learning models on historical data
  • Generating daily/weekly reports
  • Data preprocessing and feature engineering
  • Backfilling historical data
  • Batch scoring of ML models

Example Workflow

  1. Data is collected from various sources
  2. Data is validated and cleaned
  3. Features are extracted and transformed
  4. Models are trained or updated
  5. Results are stored for downstream consumption

Tools and Technologies

  • Orchestration: Apache Airflow, Luigi, Prefect
  • Processing: Spark, Flink, Beam, Dask
  • Storage: S3, GCS, Azure Blob Storage, HDFS
  • Monitoring: Prometheus, Grafana, Datadog

Performance Considerations

  • Partitioning: Partition data to enable parallel processing
  • Caching: Cache frequently accessed data in memory
  • Resource Allocation: Allocate appropriate resources (CPU, memory) based on workload
  • Data Locality: Process data close to where it's stored to minimize network transfer

Security and Compliance

  • Data Encryption: Encrypt data at rest and in transit
  • Access Control: Implement fine-grained access controls
  • Audit Logging: Maintain logs of all data access and processing activities
  • Compliance: Ensure compliance with relevant regulations (GDPR, HIPAA, etc.)

Next Steps