#261 Just What the Heck is a Data Product Container? - Zhamak's Corner 29
Oct 20, 2023
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This podcast explores the concept of data product containers and their importance in simplifying the sharing and delivery of data products. It discusses the decentralized architecture in data mesh and features a guest from Nix data, a data mesh technology company. The concept of data containerization and its potential impact on the data industry is also explored, highlighting the need for data product containers as a new atomic unit for data. The speaker discusses the concept of a data product quantum and Next Data's approach, emphasizing the shift away from a centralized approach.
Data product containers simplify the process of managing and accessing data products by encapsulating all necessary components, including code, metadata, APIs, and policies.
Decentralized architecture in data mesh empowers teams to independently manage their own data products, minimizing dependencies and enabling reliable and efficient data sharing.
Deep dives
The Importance of Data Product Containers
In this podcast episode, the concept of data product containers is discussed. Drawing inspiration from software containers, data product containers aim to encapsulate all the necessary structural components for sharing data in a responsible and easy way. This includes the code, metadata, APIs, and policies related to the data. By bundling these components together, data product containers simplify the process of managing and accessing data products. The centralized approach of relying on catalogs or dictionaries is considered less effective compared to a decentralized approach that allows data producers to package and manage their own data products. By adopting data product containers, the challenges and dependencies associated with managing and sharing data are minimized, enabling smooth collaborations and interactions with data.
Decentralized Architecture in Data Mesh
The podcast explores the importance of decentralized architecture in the context of data mesh. While many organizations tend to adopt centralized data products, managed via centralized architecture, this approach can lead to various problems similar to those encountered in centrally managed software solutions. In contrast, a decentralized architecture in data mesh empowers teams to manage their own data products independently, minimizing dependencies and reducing potential cascading issues. This approach promotes a more agile and flexible environment for data sharing, where teams have more control over their data products and can ensure reliable and efficient data sharing across the organization.
Data Mesh and the Evolution of Containerization
The podcast dives into the parallels between containerization in software and the emerging concept of data product containers in data mesh. The transformative impact of containerization on software development, which allowed for easy deployment and abstraction of infrastructure, is seen as a key lesson for the data side. Data product containers aim to bundle together the various structural components required for data sharing, such as code, metadata, data itself, APIs, and policies. By doing so, data product containers simplify the process of packaging, running, discovering, and using data products. While there may not be a one-to-one analogy between software containers and data product containers, the shared goal of streamlining the delivery and management of valuable assets remains a crucial aspect of both domains.
Containers in software abstracted away a number of very cumbersome tasks and encapsulated a lot of the dependencies software had to its environment. Combined, that meant developers could focus on delivering value instead of focusing on the infra. We need to do the same in data.
It's all about sharing data in a responsible and easy way. That means putting all the components together so you don't have to manage many versions. Just like microservices.
How do you manage to make this easy for the data product developer - bundle everything together. But centralized data products are creating a lot of potential issues/risk to scalability and flexibility. We just keep trying to centralize in data.
Sponsored by NextData, Zhamak's company that is helping ease data product creation.