Building Scalable Data Mesh Solutions: Lessons from Disney Streaming

Introduction

In the world of data management, organizations are increasingly shifting toward scalable and efficient solutions. Himateja Madala, the Senior Data Engineering Manager and Head of the Data Mesh Data Platform at Disney Streaming, has been instrumental in redefining Disney’s data systems to follow a data mesh philosophy. This article delves into the insights and key takeaways from Madala’s extensive experience, providing guidance on how organizations can build their own scalable and automated data mesh framework.

Insights for Building a Scalable Platform

Choose Between an Existing Data Platform or Your Own

When implementing a data mesh strategy, organizations must decide whether to leverage an existing data platform or build their own from scratch. This choice hinges on a careful evaluation of their specific needs, resources, and long-term goals. While an existing data platform can offer immediate access to established tools and technologies, potentially reducing time-to-market and implementation costs, it might also impose limitations that impede the adaptability and scalability crucial for a successful data mesh. On the other hand, building a custom data platform allows organizations to tailor solutions to precise requirements and maintain full control of the architecture. However, this approach can be resource-intensive, demanding substantial investments in terms of time, money, and talent. Ultimately, the decision should align with the organization’s strategic vision, considering factors such as data governance, scalability, and the ability to foster a culture of decentralized data ownership and collaboration within the data mesh framework.

Empower Non-Centralized Users

Empowering non-centralized users lies at the heart of a successful data mesh implementation. By distributing data ownership and access to various domains and teams, organizations can foster a culture of data democratization and collaboration. Non-centralized users, including data producers and consumers across the organization, gain the ability to manage their own data assets and make data-driven decisions independently. This empowerment is facilitated through clear data ownership boundaries, standardized data access and discovery mechanisms, and a shared understanding of data quality and lineage. By providing these tools and guidelines, data mesh not only reduces the bottleneck of centralized data teams but also ensures that data is effectively utilized by those closest to the business challenges, resulting in more agile and efficient decision-making processes. Essentially, data mesh transforms data into a valuable, decentralized asset that drives innovation and advancement throughout the organization.

Embrace a Diversity of Data Products

The diversity of data products is a core principle of a data mesh strategy. In a data mesh, data products are the encapsulated, self-serve data assets that cater to specific domain needs. Embracing diversity in these data products means that they can take various forms, such as data APIs, databases, real-time streams, or even machine learning models, tailored to address the needs of individual business units. This approach recognizes that there is no one-size-fits-all solution for data, and encourages innovation and specialization within distinct domains. Data products can be developed to serve various purposes, from analytical insights to real-time decision support, promoting flexibility and autonomy for domain teams. This diversity fosters a dynamic ecosystem where each data product is designed to maximize its value, enabling faster, more efficient, and domain-focused data utilization throughout the organization.

Leverage Data Contracts for Quality

Leveraging data contracts is critical for ensuring data quality in the implementation of a data mesh. These contracts serve as explicit agreements that define the expectations and specifications for data shared across different domains within an organization. They outline the data’s structure, semantics, and quality standards, ensuring that both data producers and consumers know what to expect. Establishing and adhering to these contracts improves data consistency and reliability while mitigating issues related to data inaccuracies and misinterpretations. Data contracts also facilitate effective data governance, allowing for continuous monitoring and validation of data quality, and providing a structured framework for addressing data quality issues when they arise. These contracts play a crucial role in maintaining high-quality, trustworthy data within the data mesh ecosystem, which is essential for making informed and reliable business decisions.

Automate What You Can

Through automation, organizations can efficiently ingest, process, and deliver data products to consumers while reducing the risk of errors and minimizing manual intervention. Automation also supports data discovery and cataloging, enabling users to find and access the right data products effortlessly. By automating data quality checks and validation, issues can be identified and resolved proactively, improving data reliability. Automation within the data mesh framework can also enhance scalability, making it easier to adapt to evolving business needs without extensive manual configuration. Automation is a key enabler for achieving the agility, efficiency, and consistency necessary to successfully implement a data mesh, allowing organizations to harness the full potential of their data assets.

Enable Trust at Scale

Enabling trust at scale within a data mesh is essential for its success, yet it can present challenges. To accomplish this, organizations must implement several key strategies:

1. Establish clear and well-defined data contracts and standards, ensuring data consistency, reliability, and adherence to quality benchmarks;
2. Employ centralized governance to set these standards and ensure data compliance and security, improving trust in the entire data ecosystem;
3. Promote effective metadata management and lineage tracking to provide transparency and traceability, allowing data consumers to have confidence in the data’s origins and transformations;
4. Implement data quality monitoring and validation mechanisms to help identify and rectify issues promptly, reinforcing trust in data accuracy; and
5. Foster a culture of data ownership and accountability, where domain teams are responsible for their data products, fostering a sense of ownership that further builds trust within the organization.

With these strategies, trust at scale in a data mesh becomes more than just a technical endeavor. It’s also a cultural and governance transformation that ensures data serves as a reliable and valuable asset driving decision-making and innovation.

Consider Owning the Infrastructure

There are many factors to consider when deciding whether to own the infrastructure in a data mesh implementation. Owning the infrastructure grants organizations complete control over their data architecture, enabling them to tailor the infrastructure to meet specific performance, security, and compliance requirements. However, this level of control also means taking on the responsibility of managing and maintaining the infrastructure, which can be resource-intensive in terms of time and expertise. Ownership requires a thorough understanding of technology stacks, scalability needs, and data pipeline orchestration, potentially requiring investments in skilled personnel and tools. In addition, owning the infrastructure involves the need to adapt and scale as the organization grows, leading to ongoing capital and operational expenditures. Organizations must carefully weigh the benefits of infrastructure ownership, including customizability and control, against the costs and resource commitments required to ensure a sustainable and efficient data mesh ecosystem.

Establish Centralized Governance and Federated Decision-Making

Centralized governance provides a framework for setting overarching data policies, standards, and security measures, ensuring data compliance and integrity throughout the organization. It helps maintain consistency and coherence in data management while preventing the emergence of data silos and chaos. On the other hand, federated decision-making empowers domain teams to make decisions about their own data, fostering agility and innovation. It ensures that those closest to the specific business context have the freedom to derive insights and create value from data, without being impeded by central bottlenecks. This dual approach strikes a balance between control and flexibility, allowing an organization to maximize the benefits of data mesh by combining centralized governance for data reliability and compliance with domain-specific decision-making for agility and responsiveness to evolving business needs.

Keep Data Within the Mesh

Keeping data within the mesh not only reduces data transfer latencies but also enhances data security, privacy, and overall network reliability. Decentralizing data storage and processing improves resilience against hardware failures and cyber threats. It also supports the scalability required for use cases like Internet of Things (IoT) and edge computing, ensuring that as more devices come online, the network stays efficient and responsive. The data mesh architecture also aligns with the increasing focus on data sovereignty, enabling compliance with local regulations while lowering data transmission costs.

Learn More about Data Mesh

This article covers key insights from Himateja Madala‘s experience building scalable automated access for data mesh at Disney Streaming. To learn more about her approaches for building effective data sharing at scale, check out this episode of Data Mesh Radio. For more information about how organizations are leveraging data mesh, check out this list of user journey stories.