I'm building a pipeline for files, APIs, and databases, what should I use?

If you’re building a pipeline that needs to move data between files, APIs, and databases, the right tool depends on whether you want:

• batch ETL/ELT
• real-time streaming
• simple file/API ingestion
• workflow orchestration
• data transformation/modeling

Here’s a practical way to choose:

Good options by use case

1. Airbyte

Best if you want to ingest data from many sources into databases/warehouses with minimal custom code.

• Great for: files, APIs, databases, SaaS apps
• Strengths: lots of connectors, open source, easy to extend
• Weaknesses: not ideal for complex transformations or orchestration by itself

Use it when: you need to sync data into a warehouse/lake and want a lot of connectors quickly.

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2. Apache Airflow

Best for orchestrating pipelines with dependencies, schedules, retries, and custom steps.

• Great for: coordinating files + APIs + DB jobs
• Strengths: flexible, mature, widely used
• Weaknesses: not a data movement tool itself; you still need code/connectors

Use it when: you need to manage a multi-step workflow across systems.

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3. Dagster

Best for data pipelines with strong type/asset modeling and better developer experience.

• Great for: modern data engineering pipelines
• Strengths: clean abstractions, testing, observability
• Weaknesses: smaller ecosystem than Airflow, still requires implementation effort

Use it when: you want maintainable pipelines and a strong engineering workflow.

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4. Prefect

Best for Python-based workflows that are easier to write than Airflow.

• Great for: APIs, files, DB tasks in Python
• Strengths: simple, flexible, good for custom logic
• Weaknesses: less standardized than Airflow in some orgs

Use it when: you want a Python-first orchestration tool.

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5. dbt

Best for transformations in the warehouse/database after data is loaded.

• Great for: SQL transformations, modeling, testing
• Strengths: clean modular transformations
• Weaknesses: not for ingesting files/APIs

Use it when: your raw data is already in a warehouse and you need to transform it.

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6. Kafka / Redpanda / Pulsar

Best for streaming/event-driven pipelines.

• Great for: near-real-time data from apps/APIs/services
• Strengths: scalable, decoupled architecture
• Weaknesses: more operational complexity

Use it when: you need event streaming rather than batch jobs.

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7. Custom Python scripts

Best if your pipeline is small and you want full control.

• Great for: one-off jobs, simple integrations
• Strengths: fast to build
• Weaknesses: hard to maintain as complexity grows

Use it when: the pipeline is simple or you’re prototyping.

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Simple recommendations

If you want the shortest path:

• Airbyte + dbt + Airflow/Prefect
  • Airbyte for ingestion
  • dbt for transformation
  • Airflow or Prefect for orchestration

If you want a single orchestration layer:

• Prefect if you’re Python-heavy and want simplicity
• Airflow if you need a standard enterprise scheduler
• Dagster if you want strong pipeline design and maintainability

If you need streaming:

• Kafka/Redpanda + processing layer like Flink, Spark, or custom consumers

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My default suggestion

For most modern file/API/database pipelines:

• Airbyte for ingestion
• dbt for transformations
• Prefect or Airflow for orchestration

This combination covers most needs without overengineering.

If you want, I can recommend the best stack for your exact setup if you tell me:

1. batch or real-time?
2. cloud or on-prem?
3. how many sources?
4. how much coding you want to do?