Data Pipelines

Create robust, scalable data pipelines that ingest, transform, and prepare data for machine learning models. Support for batch and streaming data processing.

Data Pipelines

Build automated data pipelines for ML training and analytics

Create robust, scalable data pipelines that ingest, transform, and prepare data for machine learning models. Support for batch and streaming data processing.

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Data Pipelines

Pipeline Capabilities

Comprehensive data pipeline solutions

ETL/ELT Pipelines
Extract, transform, and load data from multiple sources with Apache Airflow, Spark, or cloud-native tools.
Streaming Pipelines
Real-time data processing with Kafka, Kinesis, or Azure Event Hubs.
Feature Engineering
Automated feature extraction, transformation, and selection for ML models.
Data Quality
Data validation, cleaning, and quality checks to ensure reliable ML training data.
Data Versioning
Version control for datasets to ensure reproducibility in ML workflows.
Monitoring & Alerting
Monitor pipeline health, data quality, and performance with automated alerts.

Pipeline Architecture

End-to-end data pipeline solutions for ML workflows.

1
Data Ingestion

Collect data from databases, APIs, files, and streaming sources.

2
Data Transformation

Clean, transform, and enrich data for ML model training.

3
Feature Store

Store and serve features for consistent model inputs.

4
Model Training

Trigger ML model training with prepared datasets.

Pipeline Architecture

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