Airbyte
Open-source data integration engine for ELT pipelines across data sources
Mara Pipelines
Lightweight Python ETL framework with PostgreSQL and web UI
A transparent data transformation framework that defines pipelines as Python code, uses PostgreSQL for processing, and provides an extensive web interface for debugging and execution.
Overview
Overview
Mara Pipelines is a lightweight data transformation framework designed for teams building ETL workflows who value transparency and simplicity over distributed complexity. It positions itself between plain scripts and heavyweight orchestrators like Apache Airflow.
Core Philosophy
Pipelines, tasks, and commands are defined declaratively in Python code. The framework uses PostgreSQL as its data processing engine and relies on command-line tools rather than in-app data processing. Execution follows GNU make semantics where nodes depend on upstream completion, not data flows. Single-machine execution via Python's multiprocessing eliminates the need for distributed task queues, making debugging straightforward.
Key Capabilities
The extensive web UI serves as the primary interface for inspecting, running, and debugging pipelines. Each pipeline displays dependency graphs, 30-day runtime charts, node priority tables, and execution logs. Tasks show upstream/downstream relationships, historical performance, and command output. Cost-based priority queues automatically run expensive nodes first based on recorded runtimes.
Deployment
Install via pip and integrate into Flask applications. A PostgreSQL database stores runtime information and incremental processing status. Note: heavy use of forking means native Windows execution requires Docker or WSL.
Highlights
Declarative Python pipeline definitions with task dependencies and bash command execution
PostgreSQL-backed execution tracking with automatic schema migration and runtime storage
Comprehensive web UI for visualizing dependencies, monitoring performance, and running tasks
Cost-based priority queues that schedule expensive nodes first using historical runtime data
Pros
- Simple single-machine execution eliminates distributed system complexity and debugging challenges
- Rich web interface provides immediate visibility into pipeline structure, performance, and logs
- Declarative Python code makes pipelines version-controllable and easy to review
- Cost-based scheduling optimizes execution order based on actual historical performance
Considerations
- Single-machine architecture limits scalability for very large or compute-intensive workloads
- PostgreSQL dependency required even for simple pipelines without complex state needs
- Does not run natively on Windows; requires Docker or WSL workarounds
- Documentation is work-in-progress; relies heavily on example projects for onboarding
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Fit guide
Great for
- Teams building medium-scale ETL workflows on single servers who prioritize simplicity over distribution
- Data engineers comfortable with PostgreSQL who want transparent, debuggable pipeline execution
- Organizations seeking a middle ground between custom scripts and complex orchestration platforms
- Flask-based applications needing integrated pipeline management with visual monitoring
Not ideal when
- Large-scale distributed data processing requiring horizontal scaling across multiple machines
- Windows-native environments without Docker or WSL infrastructure available
- Teams requiring real-time streaming or event-driven data flows rather than batch dependencies
- Projects needing extensive out-of-the-box integrations with cloud services and SaaS platforms
How teams use it
Daily data warehouse refresh
Schedule nightly ETL jobs that extract from sources, transform via SQL, and load to PostgreSQL with automatic retry and performance tracking
Multi-stage reporting pipeline
Chain data extraction, cleaning, aggregation, and export tasks with dependency management and visual progress monitoring
Database migration orchestration
Coordinate complex schema changes and data backfills across multiple tables with rollback-friendly task isolation
Incremental data synchronization
Track file dependencies and timestamps to process only changed data sources, reducing runtime and resource consumption
Tech snapshot
Python84%
JavaScript13%
PLpgSQL1%
CSS1%
Makefile1%
Frequently asked questions
Does Mara Pipelines require a distributed task queue like Celery?
No. Mara Pipelines uses Python's multiprocessing for single-machine execution, eliminating the need for distributed task queues and simplifying debugging.
Can I run Mara Pipelines on Windows?
Not natively due to heavy use of forking. You must use Docker or the Windows Subsystem for Linux (WSL) to run it on Windows.
Is PostgreSQL required for all pipelines?
PostgreSQL is recommended for storing runtime information, execution logs, and incremental processing state. The framework is designed with PostgreSQL as the data processing engine.
How does cost-based priority scheduling work?
Mara Pipelines records historical run times for each node and schedules nodes with higher costs (longer runtimes) first to optimize overall pipeline completion time.
Can I integrate Mara Pipelines into an existing Flask application?
Yes. The framework provides web UI components designed for Flask integration. Reference the mara example projects for implementation patterns.
Project at a glance
Dormant- Stars
- 2,086
- Watchers
- 2,086
- Forks
- 99
LicenseMIT
Repo age7 years old
Last commit2 years ago
Primary languagePython
Last synced 4 hours ago