Question 1

How does Amphi ETL compare to Apache Airflow?

Accepted Answer

Amphi ETL focuses on visual, low-code data transformation with Python code generation, while Apache Airflow is a code-first orchestrator for complex workflows. Amphi is better for rapid pipeline development with a visual interface, whereas Airflow excels at scheduling and monitoring large-scale ETL.

Question 2

Can Amphi handle large datasets efficiently?

Accepted Answer

Amphi uses libraries like pandas and DuckDB, which are optimized for moderate-sized data. For very large datasets, performance may depend on hardware and custom optimizations, as it's designed for lightweight ETL rather than big data processing.

Question 3

How to create a custom component in Amphi?

Accepted Answer

You can create a TypeScript/JavaScript file following the example in the README, then add it via the interface. This requires coding knowledge to define the component's logic, imports, and code generation, making it flexible but technical.

Question 4

Does Amphi support incremental data loading?

Accepted Answer

The README doesn't explicitly mention incremental loads, but since it generates Python code, you can implement this logic in custom components or embedded code. Out-of-the-box features may be limited to basic transformations.

Question 5

What are the deployment options for Amphi?

Accepted Answer

Amphi can be self-hosted on local machines or servers using commands from the README, with parameters for workspace, IP, and port. It also offers a JupyterLab extension, but cloud deployment requires manual setup and maintenance.

Question 6

Is Amphi good for machine learning data prep?

Accepted Answer

Yes, Amphi's visual interface and Python code generation make it suitable for preprocessing data for ML, especially with AI assistance. However, for highly specialized ML pipelines, direct coding might offer more control.

Question 7

How to export and run Amphi pipelines outside the tool?

Accepted Answer

Amphi generates native Python code that can be exported and executed independently, using dependencies like pandas. This portability allows integration into existing scripts or systems, as highlighted in the features.

amphi-etl

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