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In today’s data-centric world, organizations recognize that data is essential for fostering innovation and delivering value to customers and business units. They are rapidly transforming traditional data platforms with cloud-native solutions that are scalable, feature-rich, and cost-efficient. Adopting a mindset that prioritizes data products from specialized teams, rather than relying on a centralized data management system, enables businesses to make data-driven decisions swiftly and effectively.
In this article, we outline a strategy to develop a data mesh utilizing AWS native services, including AWS Lake Formation and AWS Glue. This method empowers lines of business (LOBs) and organizational units to operate independently by taking full ownership of their data products. Simultaneously, it ensures central data discovery, governance, and auditing for the organization, thus safeguarding data privacy and compliance.
Advantages of a Data Mesh Model
A centralized model aims to streamline staffing and training by consolidating data and technical expertise in one place, thereby minimizing technical debt associated with managing a single data platform. Typically, data platform teams within central IT are divided based on the technical functions they support. For instance, one team might focus on ingestion technologies for collecting data, while another manages data pipelines, including writing and debugging extract, transform, and load (ETL) code, validating data quality, and ensuring compliance with business SLAs. However, a central data platform can lead to challenges in scaling, ownership, and accountability, as central teams may lack insight into the specific needs of a given data domain.
Delegating ownership and autonomy to the teams responsible for the data can alleviate these challenges. This allows them to create data products tailored to their domain. For example, product teams ensure that the product inventory is consistently updated and that they, as domain experts, can resolve any discrepancies. They control the entire process from ingestion to data production, selecting the technology stack and managing security and auditing. This accountability streamlines information flow, with producers held responsible for the datasets they provide, aligning with the agreed-upon SLAs.
The concept of treating data as a product mirrors Amazon’s operational model of service creation. Service teams own their services, expose APIs with defined SLAs, and manage the end-to-end customer experience. This contrasts with traditional models where one team develops software and another operates it. This ownership model enhances efficiency and scalability, enabling rapid responses to customer needs, unhindered by centralized teams. In the data realm, this means data producers are responsible for the entire dataset lifecycle, employing the most suitable technologies for their requirements.
Solution Overview
This article demonstrates how the Lake House Architecture is ideally positioned to assist teams in establishing data domains while facilitating data sharing and federation across business units. By employing a data mesh approach, organizations can enhance autonomy and accelerate innovation while adhering to high standards of data security and governance.
Key Considerations for Data Mesh Design:
- Data Mesh Framework: A data mesh provides a structure for organizations to organize around data domains, delivering data as a product. However, it may not be suitable for every organization.
- Lake House Architecture: This architecture offers technical guidance and solutions for constructing a modern data platform on AWS.
- Repeatable Blueprint: The Lake House approach, anchored by a foundational data lake, serves as a scalable blueprint for implementing data domains and products.
- Adaptability: The methods employed with AWS analytics services in a data mesh may evolve but will remain aligned with the best practices for each service.
Goals for Data Mesh Design:
- Data as a Product: Each domain takes full ownership of their data, responsible for building, operating, serving, and resolving any issues. Data accuracy and accountability rest with the domain owner.
- Federated Data Governance: Effective governance ensures data security and accuracy. Each domain can manage technical implementations like lineage tracking and access controls, while centralized data discovery and auditing make it easy for users to find data.
- Common Access: Data should be easily accessible to data analysts and scientists, as well as analytics and machine learning services like Amazon Athena and Amazon SageMaker. Domains must provide interfaces that facilitate access while maintaining security and audit trails.
User Experience Considerations:
- Data teams manage the entire information lifecycle, from data creation to analytics systems that generate insights. They determine which datasets are suitable for consumer access.
- Data producers register datasets with a central catalog, choosing what to share and how consumers can engage with the data while ensuring its accuracy.
- Consumers should access data via supported interfaces, such as data APIs, to ensure performance, tracking, and security.
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