USE CASE
Real-Time Features for ML Models
Stale training data produces stale predictions. Stream database changes to your warehouse or lake in real time—keep feature stores fresh, retrain models on current data, and power real-time inference pipelines.
AI/ML Use Cases
Stream fresh data to power your machine learning workflows
Training Data Freshness
Keep ML training datasets current with continuous CDC to your data lake. Retrain models on the latest data.
Real-time Analytics for ML
Power fraud detection, recommendations, and risk scoring with sub-second data in your warehouse.
Data Lake Pipelines
Stream to Iceberg, Delta Lake, or S3 Parquet for batch ML training and analytics workloads.
Event-Driven ML
Consume predictions and model outputs from Kafka to update operational databases and trigger actions.
Why Streamkap for ML Pipelines
Fresher Training Data
Continuous CDC keeps your training datasets hours or days more current than batch ETL.
Lower Latency Inference
Real-time data in warehouses enables faster feature computation and model serving.
Kafka Integration
Produce CDC events for ML pipelines and consume model outputs to update systems.
Stream to Your ML Stack
Snowflake
ML training and serving
Databricks
MLflow and Delta Lake
BigQuery
BigQuery ML
S3/Iceberg
Data lake ML
Kafka
ML event pipelines
ML Data Pipeline Architecture
Fresh Training Data
Event-Driven ML
Stale data produces stale predictions
Stream to your ML stack in real time. Fresh features, fresh models.