How to Build a Data Orchestration Pipeline Using Luigi in Python?

To increase the profitability of your business and ensure rational use of resources, developing high-performance data pipelines can be beneficial. Data orchestration can help you with this as it simplifies the management of various pipeline tasks, including scheduling and error handling.

Luigi, a Python library built to streamline complex workflows, can help you effectively build and orchestrate data pipelines.

Here, you will learn how to build Luigi Python data orchestration pipelines and their real-world use cases. You can utilize this information to create robust data pipelines and improve the operational performance of your organization.

What is Luigi?

Luigi is an open-source Python package that provides a framework to develop complex batch data pipelines and orchestrate various tasks within these pipelines. It simplifies the development and management of data workflows by offering features such as workflow management, failure handling, and command-line integration.

Developed by Spotify, a digital music service company, Luigi was initially developed to coordinate tasks involving data extraction and processing. The developers at Spotify enhanced Luigi's functionalities, including features like resolving task dependencies and visualizing workflows. Spotify now uses Luigi for batch-processing jobs, generating lists of top songs, and providing personalized music recommendations to users.

Why Use Luigi For Data Pipelines?

Luigi is one of the most preferred solutions for developing data orchestration pipelines. Some of the reasons for this are as follows:

Automation of Complex Workflows

Luigi enables you to chain multiple tasks in long-running batch processes. These processes include running Hadoop jobs, transferring data between databases, or running machine learning algorithms. After linking the tasks, Luigi allows you to define dependencies between tasks and ensure execution in the correct order.

Built-in Templates for Common Tasks

Luigi offers pre-built templates for common tasks, such as running Python MapReduce jobs in Hadoop, Hive, or Pig. These templates can save significant time and effort when performing big data operations, making it easier to implement complex workflows.

Atomic File Operations

Atomic file operations are tasks that you must execute completely without interruptions or partial completion. Luigi supports such operations by providing file system abstractions for HDFS (Hadoop Distributed File System) and local files. These abstractions, implemented as Python classes with various methods, ensure that data pipelines remain robust. If an error occurs at any stage, Luigi helps prevent crashes and maintains the integrity of the process.

Understanding Luigi Fundamentals

To effectively use Luigi for building data orchestration pipelines, it is important to understand its core components and how they work together. It has a simple architecture based on tasks written in Python.

The architecture includes the option of a centralized scheduler, which helps ensure that two instances of the same task aren’t running simultaneously. It also provides visualizations of task progress.

Let’s look at the prominent components of Luigi’s architecture:

Tasks

Tasks in Luigi are Python classes where you define the input, output, and dependencies for executing data pipeline jobs. The tasks depend on each other and the output targets. Some key methods within the task class include:

• The require() method helps you specify task dependencies. It is important to execute all the necessary tasks before running the main task. This ensures that all tasks are executed in the correct order.
• The run() method contains the code to execute the task.
• The output() method returns one or more target objects, representing the task’s output.

Targets

Targets represent the resources produced by tasks. They are like outputs produced after the execution of code for desired jobs. A single task can create one or more targets as output and is considered to be complete only after the production of all of its targets. To check if the task has created a target or not, you can use the exists() method.

Dependencies

In Luigi, dependencies represent the relationships between tasks of a data pipeline workflow. You can utilize dependencies to execute all the tasks in the correct order, ensuring that each task is executed after completing the previous one. This enables you to streamline your workflows and prevent errors caused by out-of-order task execution.

Dependency Graphs

Dependency graphs in Luigi are illustrations that provide a visual overview of your data workflows. In these graphs, each node represents a task. Typically, the completed tasks are represented in green, while pending tasks are indicated in yellow. These graphs help you manage and monitor the progress of your pipelines effectively.

How to Create a Data Orchestration Pipeline Using Luigi in Python?

With Python Luigi, you can build ETL data pipelines to transfer data across different data systems. In this tutorial, let’s develop a simple data pipeline for transferring data between MongoDB and PostgreSQL with the Luigi Python package.

Here is a step-by-step guide for it:

Step 1: Install Luigi

First, install Luigi in your Python environment using the following command:

pip install luigi

Step 2: Import Important Libraries

Then, you can import all the necessary libraries along with Luigi that will be required to build your data pipeline.

Step 3: Create a Luigi Task to Extract Data from MongoDB

In this step, you can define a Luigi task named ExtractTask to extract data from MongoDB Atlas. Then, store the extracted data as a JSON file in your local system.

Here:

• mongo_uri is the URI of the MongoDB Atlas database.‍
• db_name is the name of the database within MongoDB Atlas.‍
• collection_name is the name of the collection in the MongoDB database.

Step 4: Transform the Data

You can convert the data in a JSON file into a Pandas DataFrame to perform essential transformations and then save the standardized data as a CSV file.

Step 5: Load Data to PostgreSQL

Define a Luigi task, LoadTask, to load the transformed data into PostgreSQL tables.

If the table doesn’t exist yet, you can create it first and then transfer data from the CSV file to Postgres using the following code:

Step 6: Run the ETL Pipeline

To run the ETL pipeline, you can trigger the LoadTask with MongoDB URI, database name, and PostgreSQL credentials. Luigi facilitates the orchestration of tasks in an accurate order, ensuring data integrity and consistency.

After successful execution, you will get the following message confirming the pipeline’s completion:

The above steps demonstrate how to build a data pipeline using Luigi.

How Airbyte Supports Data Orchestration?

You can use Luigi to develop simple data pipelines efficiently. However, building complex data pipelines with Luigi requires extensive coding expertise and strong Python programming skills. This can make the process complicated and time-consuming.

To overcome these limitations associated with Luigi for data pipeline development, you can use Airbyte, an effective data movement platform. It offers an extensive library of 550+ pre-built connectors, enabling you to extract data from varied source data systems.

If the connector you want to use is not available in the existing set of connectors, Airbyte allows you to build custom connectors with:

• Connector Builder
• Low Code Connector Development Kit (CDK)
• Python CDK
• Java CDK

After data extraction and loading, you can utilize Airbyte’s dbt Cloud integration. This allows you to create and run dbt transformations right after your syncs in Airbyte Cloud. With this, you can transform the raw data into a consistent format for analysis or reporting. Further, you may also integrate Airbyte with data orchestration tools like Apache Airflow, Dagster, Prefect, and Kestra to orchestrate your data pipelines.

Some additional features of Airbyte are as follows:

• AI-Powered Connector Builder: Airbyte allows you to use an AI assistant while developing custom connectors using the Connector Builder. The AI assistant automatically pre-fills the necessary fields in the Connector Builder and also provides intelligent solutions to fine-tune the configuration process.‍
• Building Developer-Friendly Data Pipelines: PyAirbyte is a Python library that offers a set of utilities for using Airbyte connectors in a development environment. With PyAirbyte, you can extract data from multiple sources and load it to SQL caches like Postgres or BigQuery. You can then use Python libraries like Pandas to clean and transform the data.‍
• Change Data Capture (CDC): Airbyte’s CDC feature allows you to incrementally capture changes made at the source data system and replicate them in the destination to ensure data consistency.‍
• Streamline GenAI Workflows: If your extracted data is in semi-structured or unstructured form, Airbyte enables you to load it directly into a vector store destination. With automated transformation methods, you can modify raw data into vector embeddings. Storing these embeddings in vector databases like Pinecone, Milvus, and Qdrant simplifies GenAI workflows.‍
• RAG Techniques: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG techniques like chunking or indexing. Using these techniques, you can improve the accuracy of outcomes generated by LLMs.

Use Cases For Luigi Python Data Pipeline

Let’s take a look at some of the most common use cases:

Machine Learning Model Training Workflow

Building a machine learning pipeline involves tasks such as data cleaning, feature engineering, model training, hyperparameter tuning, and model evaluation. Each task is dependent on the successful completion of the previous task. With Luigi, you can manage these dependencies efficiently, as it enables you to track the progress of all the tasks. If the model training fails, Luigi allows its independent re-execution without restarting the entire pipeline, saving time and resources. Such an approach enables you to develop complex ML workflows.

Business Intelligence

You can build a data pipeline by extracting customer data from CRM, social media, e-commerce websites, and other sources. Transforming this incoming data makes it compatible with a central data warehouse. During this data pipeline building process, Luigi can help you manage task dependencies and handle errors. You can utilize the data pipeline for customer data analytics, segmentation, or sentiment analysis.

Ad Performance Analytics

Luigi simplifies the creation and orchestration of daily pipelines for ad performance analysis. It involves extracting ad metrics such as clicks, impressions, or conversions from APIs of platforms like Google, Facebook, or LinkedIn. You can then normalize and aggregate this data to get a unified view for performance analysis. This helps you generate daily reports and adjust campaign strategy and budget.

Conclusion

Building a data orchestration pipeline using Luigi offers several benefits, including workflow visualization and better error-handling capabilities. It enables you to develop reliable data pipelines and ensure timely data delivery for low-latency business operations.

This blog provides a step-by-step guide for building Luigi Python data pipelines by illustrating the development of an ETL pipeline for transferring data from MongoDB to PostgreSQL. Using this stepwise approach, you can build any data pipeline for various purposes, such as business intelligence, machine learning workflows, or ad performance analytics.