Eliminate The Overhead In Your Data Integration With The Open Source dlt Library
Sep 4, 2023
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The podcast explores the dlt project, an open source Python library for data loading. It discusses the challenges in data integration, the benefits of dlt over other tools, and how to start building pipelines. Other topics include the journey of becoming a data engineer, performance considerations of using Python, collaboration in data integration, and integration with different runtimes. The hosts emphasize the need for better education in data management and practical solutions.
DLT is a Python library for data loading that simplifies the process of building data pipelines and offers a customizable approach to pipeline development and management.
DLT aims to bridge the gap in data management education by providing a user-friendly library-driven solution that empowers data professionals to build robust and scalable data pipelines.
Deep dives
Simplified Data Pipeline Building with DLT
DLT is a Python library for data loading, designed to simplify the process of building data pipelines. It was created to address the challenges faced by data engineers in managing large amounts of data and maintaining data pipelines. With DLT, users can easily load and curate data, automate tasks, and handle schema evolution. The library offers a declarative interface that allows for low-friction pipeline development and maintenance. It supports common use cases for data engineers, data users, and data analysts, making it a versatile tool for Python-first teams. DLT stands out from other extract and load tools by providing a library approach, allowing users to choose and customize the components they need. The goal is to provide a productivity boost and reduce development and maintenance time. DLT is built with Python in mind, leveraging the popularity and familiarity of the language among data professionals. While Python might not be the fastest language, DLT's focus on data loading, which is typically not transactional and requires scheduled jobs, makes it performant for its intended purpose. The DLT project primarily focuses on Python users, aiming to serve data professionals looking for a user-friendly and efficient solution for data loading and pipeline building. While DLT is not aimed at replacing all existing data integration tools, it excels at providing a flexible and customizable approach to pipeline development and management.
Challenges and Considerations in Designing DLT
Building DLT presented several challenges and considerations. One challenge was striking the right balance between code quality and accepting contributions. The project aims to encourage community involvement but also needs to ensure the quality of contributed code. Another challenge was ensuring scalability and performance, particularly for high-throughput data loads with stable schemas. While Python may not be the fastest language, DLT's target use cases and focus on scheduled jobs make it performant enough. DLT also had to address the complexities of supporting different runtime environments and orchestrators. The project aims to fit into existing ecosystems and workflows, rather than replacing them, by offering integrations with platforms like Airflow and accommodating user requirements for other orchestrators. Ultimately, the goal of DLT is to provide a library that offers a standardized and extensible solution for data loading, enabling users to customize their pipelines and easily maintain and scale their data workflows.
Future Plans: OpenAPI Integration and Community Building
DLT has exciting plans for future development and growth. One of the immediate goals is to enhance OpenAPI integration, allowing users to generate pipelines from OpenAPI specifications. This feature will simplify the process of building pipelines and make it easier to work with different APIs. DLT also plans to improve pipeline modularity to make it more extensible and maintainable. This will enable users to easily customize and add new sources and destinations. Additionally, DLT aims to foster community engagement through the development of DLT Hub. This platform will provide a space for the community to share pipelines, resources, and collaborate on projects. The long-term vision is to create a symbiotic relationship between the open-source DLT project and the DLT Hub platform, where the open-source project drives adoption and standardization, while the platform offers additional features and services to support the community and ensure the sustainability of the project.
Addressing Data Management Challenges Through Education
One of the biggest gaps in the tooling and technology landscape for data management is education. Many data professionals have had limited access to high-quality educational resources, often relying on vendor-based materials that prioritize selling products rather than providing practical and effective solutions. DLT recognizes the need for better education around data management and aims to fill this gap. By offering a user-friendly library and supporting resources, DLT helps data professionals understand and tackle their data integration challenges. The project aims to empower users with knowledge and provide practical tools that make data management more efficient and effective. By focusing on education and providing a library-driven solution, DLT aims to bridge the gap between theory and practice, enabling data professionals to build robust and scalable data pipelines.
Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today.
Announcements
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Your host is Tobias Macey and today I'm interviewing Adrian Brudaru about dlt, an open source python library for data loading
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what dlt is and the story behind it?
What is the problem you want to solve with dlt?
Who is the target audience?
The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. in the commercial space. What are the complexities or limitations of those tools that leave an opening for dlt?
Can you describe how dlt is implemented?
What are the benefits of building it in Python?
How have the design and goals of the project changed since you first started working on it?
How does that language choice influence the performance and scaling characteristics?
What problems do users solve with dlt?
What are the interfaces available for extending/customizing/integrating with dlt?
Can you talk through the process of adding a new source/destination?
What is the workflow for someone building a pipeline with dlt?
How does the experience scale when supporting multiple connections?
Given the limited scope of extract and load, and the composable design of dlt it seems like a purpose built companion to dbt (down to the naming). What are the benefits of using those tools in combination?
What are the most interesting, innovative, or unexpected ways that you have seen dlt used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on dlt?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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