This post is written in collaboration with Claudia Chitu and Spyridon Dosis from ACAST.
Founded in 2014, Acast is the world’s leading independent podcast company, elevating podcast creators and podcast advertisers for the ultimate listening experience. By championing an independent and open ecosystem for podcasting, Acast aims to fuel podcasting with the tools and monetization needed to thrive.
The company uses AWS Cloud services to build data-driven products and scale engineering best practices. To ensure a sustainable data platform amid growth and profitability phases, their tech teams adopted a decentralized data mesh architecture.
In this post, we discuss how Acast overcame the challenge of coupled dependencies between teams working with data at scale by employing the concept of a data mesh.
The problem
With an accelerated growth and expansion, Acast encountered a challenge that resonates globally. Acast found itself with diverse business units and a vast amount of data generated across the organization. The existing monolith and centralized architecture was struggling to meet the growing demands of data consumers. Data engineers were finding it increasingly challenging to maintain and scale the data infrastructure, resulting in data access, data silos, and inefficiencies in data management. A key objective was to enhance the end-to-end user experience, starting from the business needs.
Acast needed to address these challenges in order to get to an operational scale, meaning a global maximum of the number of people that can independently operate and deliver value. In this case, Acast tried to tackle the challenge of this monolith structure and the high time to value for product teams, tech teams, end consumers. It’s worth mentioning that they also have other product and tech teams, including operational or business teams, without AWS accounts.
Acast has a variable number of product teams, continuously evolving by merging existing ones, splitting them, adding new people, or simply creating new teams. In the last 2 years, they have had between 10–20 teams, consisting of 4–10 people each. Each team owns at least two AWS accounts, up to 10 accounts, depending on the ownership. The majority of data produced by these accounts is used downstream for business intelligence (BI) purposes and in Amazon Athena, by hundreds of business users every day.
The solution Acast implemented is a data mesh, architected on AWS. The solution mirrors the organizational structure rather than an explicit architectural decision. As per the Inverse Conway Maneuver, Acast’s technology architecture displays isomorphism with the business architecture. In this case, the business users are enabled through the data mesh architecture to get faster time to insights and know directly who the domain specific owners are, speeding up collaboration. This will be further detailed when we discuss the AWS Identity and Access Management (IAM) roles used, because one of the roles is dedicated to the business group.
Parameters of success
Acast succeeded in bootstrapping and scaling a new team- and domain-oriented data product and its corresponding infrastructure and setup, resulting in less friction in gathering insights and happier users and consumers.
The success of the implementation meant assessing various aspects of the data infrastructure, data management, and business outcomes. They classified the metrics and indicators in the following categories:
- Data usage – A clear understanding of who is consuming what data source, materialized with a mapping of consumers and producers. Discussions with users showed they were happier to have faster access to data in a simpler way, a more structured data organization, and a clear mapping of who the producer is. A lot of progress has been made to advance their data-driven culture (data literacy, data sharing, and collaboration across business units).
- Data governance – With their service-level object stating when the data sources are available (among other details), teams know whom to notify and can do so in a shorter time when there is late data coming in or other issues with the data. With a data steward role in place, the ownership has been strengthened.
- Data team productivity – Through engineering retrospectives, Acast found that their teams appreciate autonomy to make decisions regarding their data domains.
- Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling.
Data mesh overview
A data mesh is a sociotechnical approach to build a decentralized data architecture by using a domain-oriented, self-serve design (in a software development perspective), and borrows Eric Evans’ theory of domain-driven design and Manuel Pais’ and Matthew Skelton’s theory of team topologies. It’s important to establish the context to understand what data mesh is because it sets the stage for the technical details that follow and can help you understand how the concepts discussed in this post fit into the broader framework of a data mesh.
To recap before diving deeper into Acast’s implementation, the data mesh concept is based on the following principles:
- It’s domain driven, as opposed to pipelines as a first-class concern
- It serves data as a product
- It’s a good product that delights users (data is trustworthy, documentation is available, and it’s easily consumable)
- It offers federated computational governance and decentralized ownership—a self-serve data platform
Domain-driven architecture
In Acast’s approach of owning the operational and analytical datasets, teams are structured with ownership based on domain, reading directly from the producer of the data, via an API or programmatically from Amazon S3 storage or using Athena as a SQL query engine. Some examples of Acast’s domains are presented in the following figure.
As illustrated in the preceding figure, some domains are loosely coupled to other domains’ operational or analytical endpoints, with a different ownership. Others might have stronger dependency, which is expected, for business (some podcasters can be also advertisers, creating sponsorship creatives and running campaigns for their own shows, or transacting ads using Acast’s software as a service).
Data as a product
Treating data as a product entails three key components: the data itself, the metadata, and the associated code and infrastructure. In this approach, teams responsible for generating data are referred to as producers. These producer teams possess in-depth knowledge about their consumers, understanding how their data product is utilized. Any changes planned by the data producers are communicated in advance to all consumers. This proactive notification ensures that downstream processes are not disrupted. By providing consumers with advance notice, they have sufficient time to prepare for and adapt to the upcoming changes, maintaining a smooth and uninterrupted workflow. The producers run a new version of the initial dataset in parallel, notify the consumers individually, and discuss with them their necessary timeframe to start consuming the new version. When all consumers are using the new version, the producers make the initial version unavailable.
Data schemas are inferred from the common agreed-upon format to share files between teams, which is Parquet in the case of Acast. Data can be shared in files, batched or stream events, and more. Each team has its own AWS account acting as an independent and autonomous entity with its own infrastructure. For orchestration, they use the AWS Cloud Development Kit (AWS CDK) for infrastructure as code (IaC) and AWS Glue Data Catalogs for metadata management. Users can also raise requests to producers to improve the way the data is presented or to enrich the data with new data points for generating a higher business value.
With each team owning an AWS account and a data catalog ID from Athena, it’s straightforward to see this through the lenses of a distributed data lake on top of Amazon S3, with a common catalog mapping all the catalogs from all the accounts.
At the same time, each team can also map other catalogs to their own account and use their own data, which they produce along with the data from other accounts. Unless it is sensitive data, the data can be accessed programmatically or from the AWS Management Console in a self-service manner without being dependent on the data infrastructure engineers. This is a domain-agnostic, shared way to self-serve data. The product discovery happens through the catalog registration. Using only a few standards commonly agreed upon and adopted across the company, for the purpose of interoperability, Acast addressed the fragmented silos and friction to exchange data or consume domain-agnostic data.
With this principle, teams get assurance that the data is secure, trustworthy, and accurate, and appropriate access controls are managed at each domain level. Moreover, on the central account, roles are defined for different types of permissions and access, using AWS IAM Identity Center permissions. All datasets are discoverable from a single central account. The following figure illustrates how it’s instrumented, where two IAM roles are assumed by two types of user (consumer) groups: one that has access to a limited dataset, which is restricted data, and one that has access to non-restricted data. There is also a way to assume any of these roles, for service accounts, such as those used by data processing jobs in Amazon Managed Workflows for Apache Airflow (Amazon MWAA), for example.
How Acast solved for high alignment and a loosely coupled architecture
The following diagram shows a conceptual architecture of how Acast’s teams are organizing data and collaborating with each other.
Acast used the Well-Architected Framework for the central account to improve its practice running analytical workloads in the cloud. Through the lenses of the tool, Acast was able to address better monitoring, cost optimization, performance, and security. It helped them understand the areas where they could improve their workloads and how to address common issues, with automated solutions, as well as how to measure the success, defining KPIs. It saved them time to get the learnings that otherwise would have been taking longer to find. Spyridon Dosis, Acast’s Information Security Officer, shares, “We are happy AWS is always ahead with releasing tools that enable the configuration, assessment, and review of multi-account setup. This is a big plus for us, working in a decentralized organization.” Spyridon also adds, “A very important concept we value is the AWS security defaults (e.g. default encryption for S3 buckets).”
In the architecture diagram, we can see that each team can be a data producer, except the team owning the central account, which serves as the central data platform, modeling the logic from multiple domains to paint the full business picture. All other teams can be data producers or data consumers. They can connect to the central account and discover datasets via the cross-account AWS Glue Data Catalog, analyze them in the Athena query editor or with Athena notebooks, or map the catalog to their own AWS account. Access to the central Athena catalog is implemented with IAM Identity Center, with roles for open data and restricted data access.
For non-sensitive data (open data), Acast uses a template where the datasets are by default open to the entire organization to read from, using a condition to provide the organization-assigned ID parameter, as shown in the following code snippet:
When handling sensitive data like financials, the teams use a collaborative data steward model. The data steward works with the requester to evaluate access justification for the intended use case. Together, they determine appropriate access methods to meet the need while maintaining security. This could include IAM roles, service accounts, or specific AWS services. This approach enables business users outside the tech organization (which means they don’t have an AWS account) to independently access and analyze the information they need. By granting access through IAM policies on AWS Glue resources and S3 buckets, Acast provides self-serve capabilities while still governing delicate data through human review. The data steward role has been valuable for understanding use cases, assessing security risks, and ultimately facilitating access that accelerates the business through analytical insights.
For Acast’s use case, granular row- or column-level access controls weren’t needed, so the approach sufficed. However, other organizations may require more fine-grained governance over sensitive data fields. In those cases, solutions like AWS Lake Formation could implement permissions needed, while still providing a self-serve data access model. For more information, refer to Design a data mesh architecture using AWS Lake Formation and AWS Glue.
At the same time, teams can read from other producers directly, from Amazon S3 or via an API, keeping the dependency at minimum, which enhances the velocity of development and delivery. Therefore, an account can be a producer and a consumer in parallel. Each team is autonomous, and is accountable for their own tech stack.
Additional learnings
What did Acast learn? So far, we’ve discussed that the architectural design is an effect of the organizational structure. Because the tech organization consists of multiple cross-functional teams, and it’s straightforward to bootstrap a new team, following the common principles of data mesh, Acast learned this doesn’t go seamlessly every time. To set up a fully new account in AWS, teams go through the same journey, but slightly different, considering their own set of particularities.
This can create certain frictions, and it’s difficult to get all data producing teams to reach a high maturity of being data producers. This can be explained by the different data competencies in those cross-functional teams and not being dedicated data teams.
By implementing the decentralized solution, Acast effectively tackled the scalability challenge by adapting their teams to align with evolving business needs. This approach ensures high decoupling and alignment. Furthermore, they strengthened ownership, significantly reducing the time needed to identify and resolve issues because the upstream source is readily known and easily accessible with specified SLAs. The volume of data support inquiries has seen a reduction of over 50%, because business users are empowered to gain faster insights. Notably, they successfully eliminated tens of terabytes of redundant storage that were previously copied solely to fulfill downstream requests. This achievement was made possible through the implementation of cross-account reading, leading to the removal of associated development and maintenance costs for these pipelines.
Conclusion
Acast used the Inverse Conway Maneuver law and employed AWS services where each cross-functional product team has its own AWS account to build a data mesh architecture that allows scalability, high ownership, and self-service data consumption. This has been working well for the company, regarding how data ownership and operations were approached, to meet their engineering principles, resulting in having the data mesh as an effect rather than a deliberate intent. For other organizations, the desired data mesh might look different and the approach might have other learnings.
To conclude, a modern data architecture on AWS allows you to efficiently construct data products and data mesh infrastructure at a low cost without compromising on performance.
The following are some examples of AWS services you can use to design your desired data mesh on AWS:
About the Authors
Claudia Chitu is a Data strategist and an influential leader in the Analytics space. Focused on aligning data initiatives with the overall strategic goals of the organization, she employs data as a guiding force for long-term planning and sustainable growth.
Spyridon Dosis is an Information Security Professional in Acast. Spyridon supports the organization in designing, implementing and operating its services in a secure manner protecting the company and users’ data.
Srikant Das is an Acceleration Lab Solutions Architect at Amazon Web Services. He has over 13 years of experience in Big Data analytics and Data Engineering, where he enjoys building reliable, scalable, and efficient solutions. Outside of work, he enjoys traveling and blogging his experiences in social media.
