AWS Big Data Blog

Covestro Deutschland AG, headquartered in Leverkusen, Germany, is a global leader in high-performance polymer materials and components. Since its spin-off from Bayer AG in 2015, Covestro has established itself as a key player in the chemical industry, with 48 production sites worldwide, €14.4 billion 2023 revenue, and 17,500 employees. Covestro’s core business focuses on developing innovative, sustainable solutions for products used in various aspects of daily life. The company offers materials for mobility, building and living, electrical and electronics sectors, in addition to sports and leisure, cosmetics, health, and the chemical industry. The company’s products, such as polycarbonates, polyurethanes, coatings, adhesives, and specialty elastomers, are important components in automotive, construction, electronics, and medical device industries.

To support this global operation and diverse product portfolio, Covestro adopted a robust data management solution. In this post, we show you how Covestro transformed its data architecture by implementing Amazon DataZone and AWS Serverless Data Lake Framework (SDLF), transitioning from a centralized data lake to a data mesh architecture. Through this strategic shift, teams can share and consume data while maintaining high quality standards through a consolidated data marketplace and business metadata glossary. The result: streamlined data access, better data quality, and stronger governance at scale that various producer and consumer teams can use to run data and analytics workloads at scale, enabling over 1,000 data pipelines and achieving a 70% reduction in time-to-market.

Business and data challenges

Prior to their transformation, Covestro operated with a centralized data lake managed by a single data platform team that handled the data engineering tasks. This centralized approach created several challenges: bottlenecks in project delivery because of limited engineering resources, complicated prioritization of use cases, and inefficient data sharing processes. The setup often resulted in unnecessary data duplication, which in turn slowed down time-to-market for new analytics initiatives, increased costs, and limited the ability of business units to act quickly on insights.The lack of visibility into data assets created significant operational challenges:

• Teams could not find existing datasets, often recreating data already stored elsewhere
• No clear understanding of data lineage or quality metrics
• Difficulty in determining who owned specific data assets or who to contact for access
• Absence of metadata and documentation about available datasets
• Departments shared little knowledge about how they were using data

These visibility issues, combined with the lack of unified access controls, led to:

• Siloed data initiatives across departments
• Reduced trust in data quality
• Inefficient use of resources
• Delayed project timelines
• Missed opportunities for cross-functional collaboration and insights

A strategic solution: Why Amazon DataZone and SDLF?

The challenges Covestro faced reflect deeper structural limitations of centralized data architectures. As Covestro scaled, central data teams often became bottlenecks, and lack of domain context led to fragmented quality, inconsistent standards, and poor collaboration. Instead of centralizing control, a data mesh gives ownership to the teams who generate and understand the data, while keeping the governance and interoperability consistent across the organization. This makes it well-suited for Covestro’s environment, which requires agility, scalability, and cross-team collaboration.

AWS Serverless Data Lake Framework (SDLF) is a solution to these challenges, providing a robust foundation for data mesh architectures. Traditional data lake implementations often centralize data ownership and governance, but with the flexible design of SDLF, organizations can build decentralized data domains that align with modern data mesh principles. The framework provides domain-oriented teams with the infrastructure, security controls, and operational patterns needed to own and manage their data products independently, while maintaining consistent governance across the organization. Through its modular architecture and infrastructure as code templates, SDLF accelerates the creation of domain-specific data products, so that Covestro’s teams can deploy standardized yet customizable data pipelines. This approach supports the key pillars of data mesh: domain-oriented decentralization, data as a product, self-serve infrastructure, and federated governance, providing Covestro with a practical path to overcome the limitations of traditional centralized architectures.

Amazon DataZone enhances the data mesh implementation through a unified experience for discovering and accessing data across decentralized domains. As a data management service, Amazon DataZone helps organizations catalog, discover, share, and govern data across organizational boundaries. It provides a central governance layer where organizations can establish data sharing agreements, manage access controls, and enable self-service data access while supporting security and compliance. While teams can use the SDLF framework to build and operate domain-specific data products, Amazon DataZone complements it with a searchable catalog enriched with metadata, business context, and usage policies, making data products easier to find, trust, and reuse.

Through the sharing capabilities of Amazon DataZone, domain teams can share their data products with other domains while maintaining granular access controls and governance policies, enabling cross-domain collaboration and data reuse. This integration means that domain teams can publish their SDLF-managed datasets to an Amazon DataZone catalog, so authorized consumers across the organization can discover and access them. Through the built-in governance capabilities built into Amazon DataZone, organizations can implement standardized data sharing workflows, check data quality, and enforce consistent access controls across their distributed data system, strengthening their data mesh architecture with robust data governance and democratization capabilities.Together, SDLF and Amazon DataZone provide Covestro with a comprehensive solution for implementing a modern data mesh architecture, enabling autonomous data domains to operate with consistent governance, seamless data sharing, and enterprise-wide data discovery.

Solution architecture and implementation

The following architecture illustrates the high-level design of the data mesh solution. The implementation used a comprehensive AWS solution built on AWS services to create a robust, scalable, and governed data mesh that serves multiple business domains across the Covestro organization.

Data domain foundation: Serverless Data Lake Framework

A key pillar of the implementation is the Serverless Data Lake Framework (SDLF), which provides the foundational infrastructure and security needed to support data mesh strategies. SDLF delivers the core building blocks for data domains such as Amazon S3 storage layers, built-in encryption with AWS KMS, IAM-based access control, and infrastructure as code (IaC) automation. By using these components, Covestro can deploy decentralized, domain-owned data products rapidly while maintaining consistent governance across the enterprise.

The framework uses Amazon Simple Storage Service (Amazon S3) as the primary data storage layer, delivering virtually unlimited scalability and eleven nines of durability for diverse data assets. The proposed S3 bucket architecture follows AWS Well-Architected principles, implementing a multi-tiered structure with distinct raw, staging, and analytics data zones. This layered approach helps different business domains to maintain data sovereignty (each domain owns and controls its data, while keeping accessibility patterns organization-wide).

Security is a fundamental aspect in Covestro’s data mesh implementation. SDLF automatically implements encryption at rest and in transit across data storage and processing components. AWS Key Management Service (AWS KMS) provides centralized key management, while carefully crafted AWS Identity and Access Management (IAM) roles enable resource isolation.

Data processing with AWS Glue

AWS Glue serves as the cornerstone of the data processing and transformation capabilities, offering serverless extract, transform, and load ETL services that automatically scale based on workload demands.

Covestro’s pre-existent centralized data lake was fed by more than 1,000 ingestion data pipelines interacting with a variety of source systems. To support the migration of existing ingestion and processing pipelines, Covestro developed reusable blueprints that included the development and security standards defined for the data mesh.Covestro released standardized patterns that teams can deploy across multiple domains while providing the flexibility needed for domain-specific requirements. These blueprints support diverse source systems, from traditional databases like Oracle, SQL Server, and MySQL to modern software as a service (SaaS) applications such as SAP C4C.

They also developed specialized blueprints for processing, standardizing, and cleaning ingested raw data. These blueprints store processed data in Apache Iceberg format, automatically saving metadata in the AWS Glue Data Catalog and providing built-in capabilities to handle schema evolution seamlessly.

Covestro relies on SDLF to quickly configure and deploy the blueprints as AWS Glue jobs inside the domain. With SDLF, teams deploy a data pipeline through a YAML configuration file, and the orchestration and management mechanisms of SDLF handle the rest. The solution includes comprehensive monitoring capabilities built on Amazon DynamoDB, providing real-time visibility into data pipeline health and performance metrics (when teams deploy a pipeline through SDLF, the system automatically integrates it with the monitoring setup).

Data quality with AWS Glue Data Quality

To achieve data reliability across domains, Covestro extended the capabilities of SDLF to incorporate AWS Glue Data Quality into data processing pipelines. This integration enables automated data quality checks as part of the standard data processing workflow. Thanks to the configuration-driven design of SDLF, data producers can implement quality controls either using recommended rules, which are automatically generated through data profiling, or applying their own domain-specific rules.

The integration provides data teams with the flexibility to define quality expectations while maintaining consistency in how quality checks are implemented at the pipeline level. The solution logs quality evaluation results, providing visibility into the data quality metrics for each data product. These elements are illustrated in the following figure.

Enterprise-ready access control with AWS Lake Formation

AWS Lake Formation integration with the Data Catalog supports the security and access control layer that makes the data mesh implementation enterprise-ready. Through Lake Formation, Covestro implemented fine-grained access controls that respect domain boundaries while enabling controlled cross-domain data sharing.

The service’s integration with IAM means that Covestro can implement role-based access patterns that align with their organizational structure, so users can access the data they need while keeping appropriate security boundaries.

Data democratization with Amazon DataZone

Amazon DataZone functions as the heart of the data mesh implementation. Deployed in a dedicated AWS account, it provides the data governance, discovery, and sharing capabilities that were missing in the previous centralized approach. DataZone offers a unified, searchable catalog enriched with business context, automated access controls, and standardized sharing workflows that enable true data democratization across the organization.

Through Amazon DataZone, Covestro established a comprehensive data catalog that helps business users across different domains to discover, understand, and request access to data assets without requiring deep technical expertise. The business glossary functionality supports consistent data definitions across domains, eliminating the confusion that often arises when different teams use different terminology for the same concepts.

Data product owners can use the integration of Amazon DataZone integration with AWS Lake Formation to grant or revoke cross-domain access to data, streamlining the data sharing process while supporting security and compliance requirements.

Managing cross-domain data pipeline dependencies

When implementing Covestro’s data mesh architecture on AWS, one of the most significant challenges was orchestrating data pipelines across multiple domains. The core question to address was “How can Data Domain A determine when a required dataset from Data Domain B has been refreshed and is ready for consumption?”.

In a data mesh architecture, domains maintain ownership of their data products while enabling consumption by other domains. This distributed model creates complex dependency chains where downstream pipelines must wait for upstream data products to complete processing before execution can begin.

To address this cross-domain dependency coordination, Covestro extended the SDLF with a custom dependency checker component that operates through both shared and domain-specific elements.

The shared components consist of two centralized Amazon DynamoDB tables located in a hub AWS account: one collecting successful pipeline execution logs from the domains, and another aggregating pipeline dependencies across the entire data mesh.

These domains deploy local components such as a dependency-tracking Amazon DynamoDB table and an AWS Step Functions state machine. The state machine checks prerequisites using centralized execution logs and integrates seamlessly as the first step in every SDLF-deployed pipeline, without additional configuration. The following diagram shows the process described.

To prevent circular dependencies that could create locks in the distributed orchestration system, Covestro implemented a sophisticated detection mechanism using Amazon Neptune. DynamoDB Streams automatically replicate dependency changes from domain tables to the central registry, triggering an AWS Lambda function that uses the Gremlin graph traversal language (using pygremlin) to construct, update, and analyze a directed acyclic graph (DAG) of the pipeline relationships, with native Gremlin functions detecting circular dependencies and sending automated notifications, as illustrated in the following diagram. This process continuously updates the graph to reflect any new pipeline dependencies or changes across the data mesh.

Operational excellence through infrastructure as code

Infrastructure as code (IaC) practices using AWS CloudFormation and the AWS Cloud Development Kit (AWS CDK) significantly improve the operational efficiency of the data mesh implementation. The infrastructure code is version-controlled in GitHub repositories, providing complete traceability and collaboration capabilities for data engineering teams. This approach uses a dedicated deployment account that uses AWS CodePipeline to orchestrate consistent deployments across multiple data mesh domains.

The centralized deployment model supports that infrastructure changes follow a standardized continuous integration and deployment (CI/CD) process, where code commits trigger automated pipelines that validate, test, and deploy infrastructure components to the appropriate domain accounts. Each data domain resides in its own separate set of AWS accounts (dev, qa, prod), and the centralized deployment pipeline respects these boundaries while enabling controlled infrastructure provisioning.

IaC enables the data mesh to scale horizontally when onboarding new domains, supporting the maintenance of consistent security, governance, and operational standards across the entire environment. Covestro provisions new domains quickly using proven templates, accelerating time-to-value for business teams.

Business impact and technical outcomes

The implementation of the data mesh architecture using Amazon DataZone and SDLF has delivered significant measurable benefits across Covestro’s organization:

Accelerated data pipeline development

• 70% reduction in time-to-market for new data products through standardized blueprints
• Successful migration of more than 1,000 data pipelines to the new architecture
• Automated pipeline creation without manual coding requirements
• Standardized approach and sharing across domains

Enhanced data governance and quality

• Comprehensive business glossary implementation that supports consistent terminology
• Automated data quality checks integrated into pipelines
• End-to-end data lineage visibility across domains
• Standardized metadata management through Apache Iceberg integration

Improved data discovery and access

• Self-service data discovery portal through Amazon DataZone
• Streamlined cross-domain data sharing with appropriate security controls
• Reduced data duplication through improved visibility of existing assets
• Efficient management of cross-domain pipeline dependencies

Operational efficiency

• Decreased central data team bottlenecks through domain-oriented ownership
• Reduced operational overhead through automated deployment processes
• Improved resource utilization through elimination of redundant data processing
• Enhanced monitoring and troubleshooting capabilities

The new infrastructure has fundamentally transformed how Covestro’s teams interact with data, enabling business domains to operate autonomously while upholding enterprise-wide standards for quality and governance. This has created a more agile, efficient, and collaborative data ecosystem that supports both current needs and future growth.

What’s next

As Covestro’s data platform continues to evolve, the focus is now to support domain teams to effectively built data products for cross domain analytics. In parallel, Covestro is actively working to improve data transparency with data lineage in Amazon DataZone through OpenLineage to support more comprehensive data traceability across a diverse set of processing tools and formats.

Conclusion

In this post, we showed you how Covestro transformed its data architecture transitioning from a centralized data lake to a data mesh architecture, and how this foundation will prove invaluable in supporting their journey toward becoming a more data-driven organization. Their experience demonstrates how modern data architectures, when properly implemented with the right tools and frameworks, can transform business operations and unlock new opportunities for innovation.

This implementation serves as a blueprint for other enterprises looking to modernize their data infrastructure while maintaining security, governance, and scalability. It shows that with careful planning and the right technology choices, organizations can successfully transition from centralized to distributed data architectures without compromising on control or quality.

For more on Amazon DataZone, see the Getting Started guide. To learn about the SDLF, see Deploy and manage a serverless data lake on the AWS Cloud by using infrastructure as code.

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