!!! tip inline end "Visibility and Control"
    Workbench provides AWS ML Pipeline visibility [Workbench Dashboard](../index.md) and control over the creation, modification, and deletion of artifacts through the Python API [Workbench REPL](../repl/index.md).
    
# Workbench vs Databricks
Databricks is a mature and comprehensive platform offering an extensive suite of functionality for data management, collaborative notebooks, and the full machine learning lifecycle. It’s widely regarded as the “Rolls-Royce” of ML pipeline systems—robust, feature-rich, and designed for scalable, enterprise-grade workloads. 

In contrast, Workbench is a more specialized tool focused on the AWS ecosystem, aimed at simplifying the creation and management of ML pipelines. It’s like a scrappy go-kart—lightweight, fast, and agile—offering a streamlined experience without the bells and whistles of larger platforms.


| Feature / Aspect                | Databricks                                                                                       | SCP Workbench                                                                  |
|-------------------------------|------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| **Purpose**                   | Unified cloud-based platform for big data analytics, data engineering, and machine learning.  | Python API and web interface framework to simplify creation and deployment of AWS SageMaker ML models and pipelines. |
| **Primary Focus**              | Data processing at scale using Apache Spark, collaborative notebooks, ML lifecycle, and data lakes. | Simplifies building, managing, and monitoring AWS ML pipelines, especially SageMaker models and AWS Glue jobs. |
| **Core Technology**            | Built on Apache Spark, native cloud platform integrations (Azure, AWS, GCP).                   | Built as an AWS-focused tool leveraging AWS SageMaker, Glue, Athena, Feature Store, with Python API and dashboards. |
| **Deployment Model**           | Cloud-native SaaS platform (AWS, Azure, GCP).                                                  | Private SaaS (BYOC - Bring Your Own Cloud), deployed within customer's AWS account for full control and compliance. |
| **User Interface**             | Collaborative notebooks, dashboard, job scheduler, MLflow integration, SQL analytics UI.       | Web dashboard with multiple interfaces providing visibility into AWS Glue jobs, SageMaker models, endpoints, data sources, feature sets. |
| **API**                       | Supports multiple languages (Python, Scala, SQL, R). Comprehensive SDKs and MLflow APIs.       | A Python API that abstracts and manages AWS ML pipeline components, making AWS ML service usage easier via Python objects. |
| **Machine Learning Support**   | End-to-end ML lifecycle management including experiment tracking, model training, deployment, monitoring. | Focused on AWS SageMaker model creation, deployment, and pipeline management with monitoring via dashboard. |
| **Data Processing**            | Optimized for big data processing using Spark and Delta Lake. Supports streaming and batch.     | Relies on AWS Glue and Athena for data ETL and querying; no native big data engine like Spark. |
| **Ecosystem Integration**      | Supports integration with many data sources and cloud services. Integrates with BI and data science tools. | Deeply integrated with AWS services, minimal or no direct support for other cloud platforms or third-party tools. |
| **Security & Compliance**      | Cloud provider’s security features, supports enterprise compliance.                            | Private SaaS allows keeping data and services within own AWS environment, facilitating strict control and compliance. |
| **Target User**                | Enterprises needing an end-to-end unified analytics and ML platform with collaboration.        | Organizations heavily invested in AWS who want easier programmatic control and monitoring of SageMaker and AWS ML pipelines. |
| **Community and Maturity**     | Established commercial product with large user base and extensive documentation/support.       | Newer project, actively developed, smaller community, focused on AWS ML pipeline usability. |
| **Use Case Example**           | Building scalable data lakes, collaborative data science projects, streaming analytics, enterprise ML workflows. | Simplifying deployment and monitoring of AWS SageMaker ML models and Glue pipelines with dashboards. |

## Additional Resources

- Using Workbench for ML Pipelines: [Workbench API Classes](../api_classes/overview.md)

<img align="right" src="../../images/scp.png" width="180">

- Workbench Core Classes: [Core Classes](../core_classes/overview.md)
- Consulting Available: [SuperCowPowers LLC](https://www.supercowpowers.com)