Pachyderm vs StreamSets: What are the differences?

# Key Differences Between Pachyderm and StreamSets

Pachyderm and StreamSets are both popular tools used for data processing and management, but they differ in several key aspects.
1. **Architecture**: Pachyderm is built on a container-based architecture, where each job runs in its own container. On the other hand, StreamSets follows a pipeline-based architecture, allowing for data flow through various stages in a workflow.
2. **Version Control**: Pachyderm offers version control for data and code, allowing users to track changes and revert to previous versions easily. In contrast, StreamSets lacks this native version control feature, making it more challenging to manage changes over time.
3. **Scalability**: Pachyderm is designed for scalability, enabling the processing of large datasets efficiently through its distributed computing capabilities. StreamSets, while capable of handling data processing tasks, may face scalability challenges when dealing with massive datasets or complex workflows.
4. **Real-time Data Processing**: StreamSets specializes in real-time data processing, providing features like stream processing and data drift detection. Pachyderm, on the other hand, focuses more on batch processing and iterative data workflows.
5. **Deployment Flexibility**: Pachyderm offers flexibility in deployment options, supporting on-premises, cloud, and hybrid environments. In comparison, StreamSets has limitations in deployment choices, primarily focusing on cloud-based solutions.

In Summary, Pachyderm and StreamSets differ in terms of architecture, version control capabilities, scalability, real-time data processing support, and deployment options.