Addressing The Challenges Of Component Integration In Data Platform Architectures
Nov 27, 2023
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In this podcast, the host discusses the challenges of integrating components in data platform architectures, including user experience, data sharing and delivery, and shadow IT. They explore event-driven pipelines, access control, data flow ownership, and metadata propagation. The importance of reliable integrations and extensible systems is emphasized, along with tools like Open Lineage and DBT. Python and open metadata platforms are highlighted for simplifying integration and managing permissions and roles across data tools.
Addressing the challenges of component integration is crucial for building a cohesive data platform architecture.
Providing efficient data delivery options and preventing unauthorized data exfiltration are key considerations in data platform management.
Deep dives
Challenges of Integrating Disparate Tools in Building a Data Platform
The podcast episode discusses the challenges of integrating different tools to build a comprehensive data platform. It explores the complexities of maintaining a single source of truth and a unified interface for defining platform concerns. The host shares their experience of building a data platform from scratch, focusing on the difficulties faced in integrating the chosen technologies and managing the friction that arises. The episode acknowledges that small teams building data platforms often opt for managed platforms or select from popular vendor combinations like 5-tran, Snowflake, and DBT. The host emphasizes the need to onboard more users, provide a seamless user experience, and address data sharing among users outside the team or department.
Delivering Data and Managing Access in a Data Platform
The podcast episode addresses the challenges of data delivery and access management within a data platform. With data stored in a lake house architecture and using the Iceberg table format on S3, the episode explores different methods of data delivery based on user sophistication and requirements. The host discusses options like generating CSV and emailing it, providing access to an S3 bucket, or offering a simple dashboard format. The episode further considers the importance of preventing unauthorized data exfiltration and the role of governance in data access. The host emphasizes the need to provide the best user experience and remove friction to discourage users from exporting data into other systems.
Interoperability, Integration, and Building a Holistic Data Platform
The podcast episode delves into the complexities of interoperability, integration, and building a holistic data platform. The host explores the importance of adopting open standards, highlighting SQL as a long-standing standard that enables easy data exploration and self-service across different tools. The episode focuses on DBT as a de facto interface for managing transformations and discusses the challenges faced by potential competitors in breaking into the market. It also touches on the benefits of integrating with widely adopted tools like Airflow and the significance of considering community integrations and the weight of existing integrations when selecting tools. The host stresses the importance of extensibility and maintenance while striving for a comprehensive platform experience.
Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. One of the core complexities that needs to be addressed is the fractal set of integrations that need to be managed across the individual components. In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'll be sharing an update on my own journey of building a data platform, with a particular focus on the challenges of tool integration and maintaining a single source of truth
Interview
Introduction
How did you get involved in the area of data management?
data sharing
weight of history
existing integrations with dbt
switching cost for e.g. SQLMesh
de facto standard of Airflow
Single source of truth
permissions management across application layers
Database engine
Storage layer in a lakehouse
Presentation/access layer (BI)
Data flows
dbt -> table level lineage
orchestration engine -> pipeline flows
task based vs. asset based
Metadata platform as the logical place for horizontal view
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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