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Databricks Unity Catalog: Your Path to Scalable Data Governance
Learn how Databricks Unity Catalog provides a unified approach to data governance across your organization, enabling better data discovery, lineage tracking, and access control.
Automate Data Ingestion With Configuration, Not Code
CARBON turns weeks of custom ETL development into hours of YAML configuration. Define your sources, transformations, and targets in simple config files, and let PySpark pipelines handle the rest.
Your data engineering teams write custom ETL code for every new source system, every migration project, and every pipeline change. That manual effort creates bottlenecks, introduces inconsistencies, and delays analytics delivery by weeks or months. CARBON eliminates repetitive pipeline development with configuration-driven automation.
KEY CAPABILITIES
Key Capabilities
CARBON combines low-code YAML configuration, PySpark transformation libraries, and automated orchestration to deliver reliable data pipelines at scale.
Reliable Data Ingestion Pipelines
We build dependable data pipelines using a low-code ingestion framework that replaces fragile, hand-coded scripts with configuration-driven processes for consistent and accurate data flow.
ETL Pipeline Automation
We automate the full ETL development cycle through YAML configuration, generating extraction, transformation, and load pipelines without repetitive custom coding.
Standardized Transformations
We provide pre-built PySpark libraries for common data transformations, so your engineering teams focus on business logic rather than repetitive data plumbing.
Scalable Pipeline Architecture
We run CARBON on PySpark libraries and Spark runtime, giving your data pipelines the ability to scale horizontally with growing data volumes without re-architecture.
Accelerated Data Migration
We streamline migrations from legacy data warehouses to modern cloud platforms, reducing transition timelines by more than half through pre-built connectors and configuration templates.
Pipeline Orchestration and Monitoring
We integrate with Apache Airflow to automatically generate DAGs for pipeline scheduling, monitoring, and alerting.
KEY FEATURES
From Manual ETL Code to Automated Data Pipelines
YAML-Driven Configuration
Define connectors, schema metadata, transformations, and column mappings in declarative YAML files.
- ✓ Eliminate custom ETL code for standard ingestion patterns
- ✓ Version-control your pipeline definitions alongside application code
- ✓ Onboard new data sources in hours rather than weeks by reusing configuration templates
PySpark Transformation Libraries
Access pre-built libraries that standardize common data transformations across all pipelines.
- ✓ Reduce engineering effort by applying tested transformation patterns
- ✓ Extend the library with your own transformations when business requirements demand custom processing
- ✓ Run all transformations on Spark runtime for distributed processing at enterprise data volumes
Apache Airflow Orchestration
Generate DAGs automatically from your YAML configuration, eliminating manual orchestration setup.
- ✓ Schedule, monitor, and manage pipeline dependencies through a single orchestration layer
- ✓ Receive alerts when pipeline stages fail or data quality thresholds are breached
- ✓ Track pipeline execution history and performance metrics for capacity planning
End-to-End ETL Lifecycle Management
Automate all ETL stages from extraction through transformation to loading in target platforms.
- ✓ Handle schema evolution and metadata changes without rebuilding pipelines
- ✓ Validate data quality at each pipeline stage through BEAT integration
- ✓ Maintain full data lineage from source systems through to analytics-ready datasets
BUSINESS IMPACT
Business Benefits
Faster Time to Insight
- Reduce data migration timelines by more than 50% compared to traditional hand-coded ETL
- Onboard new data sources in hours using pre-built connectors and configuration templates
- Accelerate your path from raw data to analytics-ready datasets
Lower Engineering Costs
- Reduce manual coding effort through standardized transformation libraries
- Free your data engineers from repetitive pipeline maintenance to focus on high-value data modeling
- Scale your data operations without proportionally increasing headcount
Improved Data Reliability
- Replace fragile hand-coded scripts with tested, configuration-driven pipelines
- Validate data quality at every stage through automated checks and BEAT integration
- Reduce pipeline failures with built-in error handling and recovery mechanisms
GSPANN ACCELERATORS
Accelerators Integration
Cadmium™
AI-powered CDP accelerator that consumes CARBON data feeds to build unified customer profiles from multiple enterprise sources.
Learn more →BEAT™
Automated ETL testing tool that validates data quality across CARBON pipelines, ensuring accuracy at every stage from source to target.
Learn more →Nitrate™
Testing and data quality accelerator that provides the observability and validation framework underlying CARBON pipeline monitoring.
Learn more →Ready to Eliminate Manual ETL Development?
Schedule a walkthrough to see how CARBON automates pipeline development and accelerates your migration to modern data platforms.
Insights
CARBON Resources
