Avo vs Dagster: What are the differences?

### Introduction
Avo and Dagster are popular tools used for orchestrating data pipelines and workflows. Here are some key differences between Avo and Dagster:

1. **Data Orchestration vs. Data Orchestration and Data Lineage**:
   Avo focuses primarily on data orchestration, helping users manage and execute data workflows efficiently. On the other hand, Dagster not only provides data orchestration capabilities but also offers robust data lineage tracking, allowing users to trace data transformations back to the original sources.

2. **Language/Framework Agnostic vs. Python-Centric**:
   Avo is language and framework agnostic, making it versatile and compatible with various tech stacks. In contrast, Dagster is Python-centric, leveraging the capabilities of Python programming language for defining pipelines and data transformations.

3. **Community Support**:
   Dagster boasts a strong and active community that actively contributes to the development and improvement of the tool. While Avo also has community support, it may not be as extensive as Dagster's community.

4. **Built-in Monitoring and Alerting vs. External Tools Required**:
   Avo comes with built-in monitoring and alerting features, allowing users to track the performance of their data pipelines seamlessly. Conversely, in Dagster, users may need to integrate external monitoring and alerting tools for the same functionality.

5. **Ease of Use for Beginners vs. Learning Curve**:
   Avo is known for its user-friendly interface and intuitive design, making it easier for beginners to start orchestrating data pipelines quickly. On the other hand, Dagster may have a steeper learning curve due to its Python-centric approach and more extensive feature set.

6. **Focus on Workflow Automation vs. Comprehensive Data Ops Platform**:
   Avo primarily focuses on workflow automation, streamlining the process of managing and scheduling data workflows. In contrast, Dagster aims to be a comprehensive Data Ops platform, catering to a wider range of data engineering needs beyond just workflow automation.

In Summary, Avo and Dagster differ in their approach to data orchestration and lineage tracking, language compatibility, community support, monitoring capabilities, user-friendliness, and overall scope within the realm of data engineering tools.