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The BMW Group and Amazon Web Services () have announced a comprehensive strategic partnership in 2020. The goal of the collaboration is to further accelerate BMW Group innovation by placing data and analytics at the heart of decision-making. A key element of the collaboration is the further development of the BMW Group Cloud Data Center (CDH). It is a central location for managing the company’s data and information solutions in the cloud (see case).
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The Re:Invent 2019 session showcased the BMW Group and its new CDH platform, which demonstrated the various archetypes of data platforms and then followed the journey of the BMW Group to build a cloud data center.
In this blog, we will discuss how the BMW Group overcame the data lake challenge by rethinking its organizational processes by transforming the data lake with cloud technologies.
To enable this innovation, in 2015 the BMW Group created a centralized internal data lake that collects and integrates anonymized data from sensors in vehicles, operating systems and data warehouses to provide historical, real-time and predictive insights.
Building and operating an enterprise-grade data lake consumed so many development resources that the team was unable to deliver new features to its customers. In addition, as the adoption of data lakes increases, the scale of computing resources and resources of the central ingest group, shown in Figure 1, becomes much more difficult to ingest data sources.
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We are inspired by the data mesh and data fabric concepts that are gaining popularity at the same time as the cloud data center. A data-focused, product-driven organization, open data products, responsive ad teams, and strong ownership within domains are values we know and love in software development.
In contrast, the data fabric concept is a centralized approach and technology that focuses on providing a central layer of metadata, automating services to manage data, and creating enterprise storage. We use these high-level concepts to derive our own set of principles for our architecture and design. We have tried to provide a data platform that covers technical complexity. It revolves around automation, self-service loading and shared global standards. Data is considered as an asset that producers provide to the data platform. Data assets are defined, optimized and ready for widespread use.
A highly centralized architecture and distributed account structure can be difficult to monitor, manage and manage. From a user perspective, it is a set of open APIs for management and, most importantly, a data portal as a single entry point for all data-related user journeys. This includes all operations on the meta model, such as creating, editing, or deleting providers, use cases, or databases. Most importantly, users can directly ingest new data sources, query use cases or databases, explore data through SQL, or perform analysis through code workbooks through the data portal. The goal is to abstract the complex details of the platform and simplify self-service capabilities for all user groups.
The central object of our platform is the database. As shown in Figure 4, this data product and the container are the resources associated with this object, the container. It acts as a repository of resources depending on the staging environment and a specific region for storing data in Amazon Simple Storage Service (Amazon S3) – an object storage service that offers scalability, data availability, security and performance. Definitions in the Clay data catalog that contain references to data used as sources and targets for extract, transform, and load (ETL) jobs in Clay.
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Data providers and data consumers are the two main users of a database. Databases are created by designated providers who can create any database on designated domains. As the database owner, they are responsible for the actual content and appropriate metadata. They can use their own tools or rely on provided plans to extract data from source systems. Once a dataset is released for use, users can use datasets created by different providers for analytics or machine learning (ML) workloads.
To embrace the idea of a completely decentralized architecture, we use separate accounts as boundaries for data producers and consumers. This approach places responsibility where the data is generated or used. For data producers, it centralizes responsibility for data acquisition and data quality—accuracy, completeness, reliability, relevance, cleanliness—and places responsibility on data producers because they are the experts in their data domain. They are responsible for working closely with data source owners.
Also, for data users, having a separate account gives them the flexibility to manage and maintain data flow from the data platform. They are free to choose the appropriate services for their use. They are also responsible for developing business interests, analyzing and generating new insights.
Using a multiple account strategy has helped us give data producers and consumers the freedom to be faster and more innovative. Since accounting is the lowest level at which costs are shared, having individual accounts means that existing accounting and reimbursement mechanisms can be used more easily. This was important because we wanted to provide a variety of development, staging, and production environments that users of the platform would use to build and test new use cases. To learn more about the benefits of using multiple accounts, see Configuring an environment using multiple accounts.
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When deciding between decentralized and decentralized approaches, we had to consider a number of factors that led to a model of centralized computing and centralized storage. The data network is focused on centralized open data, based on a product-oriented approach with FLAIR principles:
However, none of the above principles require centralized storage. The motivation for centralized storage was to make it easier for data producers to comply in terms of compliance. A centralized repository helped us avoid duplicate development efforts for platform services and established platform principles. The diagram below shows how central storage accounts are organized. The gray boxes in Figure 5 below define the calculation limits for stage (dev, int & prod) and center (global, market1, market2). This is a one-to-many mapping between hub/stage and account. This ensures that the platform can disaggregate data based on market, regulatory and compliance factors.
While individual teams are responsible for their accounts, reporting teams have the authority to control their domains and the data they produce. This gave teams the freedom to choose the tools and services that best fit their needs. Teams used analytics services to capture, transform, and store data on the platform. It includes services such as Amazon EMR, a big data cloud platform, and Amazon Kinesis, which facilitate the collection, processing and analysis of streaming data in real time. Other services include Lambda, a serverless, event-driven computing service, and Amazon Athena, an interactive query service.
Created a set of reusable blueprints for teams that inform users to follow best practices. These plans aim to make teams faster by reducing manual processes and process delays. The reusable, modular approach made it easier for data teams to focus on data acquisition so that engineers could spend less time on repetitive tasks and more time on high-value tasks with clear business outcomes. Plans have completely standardized settings that reduce the possibility of errors or deviations. This reduced the likelihood of incompatibility issues and allowed for seamless integration of new features.
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Using a server-first service approach, Cloud Data Hub helped development teams focus on business challenges and avoid technical plumbing. Another advantage of using managed services is that the platform enjoys better service. A prime example of this is that Clay has undergone several improvements and updates since 2019, and the platform and its tenants have automatically benefited from it.
Use a multi-account strategy to scale and better manage cloud data across multiple business entities
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