Batch Processing
A data processing approach that collects events over a defined time period and processes them together as a group, typically on hourly or daily schedules, optimized for throughput and complex computations.
Also known as: batch job, scheduled processing
Why It Matters
Batch processing handles the heavy analytical lifting that real-time streaming cannot efficiently perform. Calculating customer lifetime values, rebuilding recommendation models, generating daily executive reports, and running complex aggregations across millions of records are all tasks better suited to batch processing.
Batch jobs are also more cost-efficient for large-scale data processing. Instead of maintaining always-on streaming infrastructure, batch jobs spin up resources when needed, process the data, and shut down. For many analytics use cases where hourly or daily freshness is sufficient, batch processing delivers the same insights at a fraction of the infrastructure cost.
Reliable batch processing is the foundation of your reporting cadence. When stakeholders expect updated dashboards every morning, it is the overnight batch jobs that aggregate, transform, and load the data that powers those dashboards.
Industry Applications
A fashion retailer runs nightly batch jobs that calculate product affinity scores, update customer segments, and regenerate personalized recommendation lists. These feed into the next day's email campaigns and on-site personalization.
A B2B analytics platform runs hourly batch jobs that aggregate usage data across thousands of customer accounts, calculate health scores, and update the customer success dashboard. Daily batch jobs rebuild predictive churn models with the latest behavioral data.
How to Track in KISSmetrics
KISSmetrics uses a combination of real-time and batch processing internally. For your own batch processing needs, schedule regular exports of KISSmetrics data to your data warehouse where batch transformation jobs can calculate derived metrics, build cohort tables, and prepare data for machine learning models.
Common Mistakes
- -Not implementing retry logic for failed batch jobs, causing missing data that requires manual intervention
- -Scheduling batch jobs without considering dependencies, so downstream jobs run before upstream data is ready
- -Letting batch job execution times grow unchecked until they exceed the available processing window
- -Not alerting on batch job failures, so stale data goes undetected until someone manually checks
Pro Tips
- +Use orchestration tools (Airflow, Dagster, Prefect) to manage batch job dependencies and scheduling
- +Implement data freshness checks that verify batch job outputs before downstream systems consume them
- +Build batch jobs to be idempotent - rerunning them should produce the same result without duplicating data
- +Monitor batch job duration trends and optimize before jobs exceed their processing windows
Related Terms
Real-Time Streaming
A data processing approach that ingests, processes, and delivers data continuously as events occur, rather than collecting data in batches for periodic processing.
ETL Pipeline
A data integration process that Extracts data from source systems, Transforms it into a consistent format, and Loads it into a destination system like a data warehouse for analysis.
Data Warehouse
A centralized repository that stores large volumes of structured and semi-structured data from multiple sources, optimized for analytical queries and reporting rather than transactional processing.
Data Lakehouse
A data architecture that combines the low-cost storage and flexibility of a data lake with the structured querying and performance of a data warehouse, supporting both raw and curated data in one system.
Historical Analytics
The analysis of past data over extended time periods to identify trends, measure long-term performance, compare cohorts, and inform strategic decisions based on accumulated evidence.
See Batch Processing in action
KISSmetrics tracks every user across sessions and devices so you can measure what matters. Start free - no credit card required.