At Symphony, we provide secure and compliant collaboration as a service to financial services organizations. Founded in 2014 by a consortium of financial institutions, we inherited a monolithic single tenant architecture that has served us well so far. As our open platform solidifies its place in the fintech ecosystem, we continue to further push the boundaries of interoperability, connect with multiple partners, and expand our portfolio with acquisitions. In executing this mission, our globally distributed engineering team faces the challenges of modernizing our core architecture while maintaining best-in-class services to power mission critical applications and workflows.
In our first blog of this series, we will focus more precisely on how a microservices architecture can be choreographed to deliver overall product functions, allow data sharing, and power data analytics in an enterprise architecture. As software architects, we need to answer these questions:
Recognizing synchronous request/response patterns for communication between microservices can hamper scalability of a service-oriented architecture, we sought to find other patterns for scaling our platform.
To begin our architecting exercise, we needed to understand how to group data and functions into cohesive services to support our various product lines, anticipating the need for data sharing. We decided to follow the data mesh approach, a concept developed by ThoughtWorks’ Zhamak Dehghani, starting with Domain Driven Design (DDD).
With our first architecture designs, we quickly realized that we would likely have to deal with dozens of services to provide all the functionality of our product portfolio. As a result, the concept of bounded context proved to be critical. Bounded context allows for identification of models interrelated due to their common presence in related business needs. We can then identify touchpoints in between contexts, possibly surfacing through concepts with similar semantics in adjacent capabilities. By extrapolating data models to functions and services, bounded context helps group functions based on context and utility: those that manipulate the same data and have frequent interactions, functions that are bridging data flows in between contexts, and unrelated functions.
From this view (fully acknowledging Conway’s law), it becomes easier to attain organizational alignment even as our organization is constantly evolving. It becomes easier to identify team ownership on a set of services and make organizational decisions without performing major rewrites of our services. We also gain a better understanding of runtime coupling in between services, and can start applying governance on the interconnection points.
A simplified, partial decomposition of our DDD model with three identified bounded contexts could be defined as follows for our secure and compliant collaboration services:
Three main bounded contexts stand out: collaboration, identity, and compliance. Concepts like “participant” and “public profile” are linked across the contexts. This model also allows us to highlight data sets from other contexts that are required for services. For instance, profile discovery and conversations services depend on compliance control rules to authorize access to public profiles or addition of participants to conversations.
The more entities, services, and contexts are set up and running, the harder it becomes for development teams to identify and rely on existing assets.
In our architecture group, we initiated registries for all data entities, APIs, and service documentations to provide visibility and standardization throughout the organization. Here is some information we collect at Symphony from our engineering teams with example standards.
Registry type | Contracts | Addressing | |
|---|---|---|---|
Schema | Entity schema – JSON Schema, Protobuf, Avro | Link to APIs or topics | Duplication, latency to data update, cardinality, changes throughput |
API | API specifications – OpenAPI, gRPC | Paths Link to service | Response time, throughput, uptime |
Service | Not standardized | Domains Routes Clusters | Response time, throughput, uptime |
We enrich registries with automated or gated checks and documentation to apply governance across the deliverables from the different teams, provide best practices, ensure consistency, and enforce API or service scopes to maintain separation of concerns.
A data mesh provides an excellent framework for asynchronous communication, but there will still be a need for some synchronous interconnection between services. A service mesh pattern can address this need.
A particular slice of data mesh is about real-time data sharing, providing an excellent framework for an event-driven architecture. We refer to this subdivision as the event mesh.
A key aspect of implementing this pattern is the ability to incorporate data from external sources by building data caches, replicas, or materialized views within bounded contexts. We can achieve this without tight coupling, details of which will be presented in a future article in this series.
At Symphony, as in most IT or SaaS companies, one of our primary goals is to maintain the highest service levels possible for mission-critical applications. We use the following metrics to measure progress:
Here is a comparison between service mesh and event mesh, with their impacts on these metrics. Value for assessment is done in comparison with a do-nothing approach.
Metric | Service Mesh | Event Mesh |
|---|---|---|
Uptime SLA | Increased through standardizing exception-handling flows
| Greatly increased through contained blast radius |
MTTR | Reduced through standardizing exception-handling flows | Greatly reduced through contained blast radius |
Incident severity | Reduced through standardizing exception-handling flows | Greatly reduced through contained blast radius |
Latency | Significant variance during incidents, low end-to-end latency | Lesser variance, longer end-to-end latency |
Throughput | Capped by throughput of slowest service in end-to-end flow | Driven by front APIs throughput only |
Key benefit | Minimize end-to-end latency | Full isolation |
Through an example and simplified model, we have introduced the usage of DDD and its extrapolation to structuring microservices and governing their interactions. We have highlighted the impacts of two service communication patterns on key operational metrics: service mesh and event mesh. The pattern with the most significant positive impact is event mesh. However, not all communications can be asynchronous. It makes sense to favor event mesh whenever possible, and otherwise rely on service mesh.
In our next blog, we will explore the positioning of event modeling in software development lifecycle, as well as detail our practical approach to build a collaborative and governed event catalog.
The current digital environment provides organizations with a range of messaging solutions, each characterized by varying levels of security and compliance capabilities. This has become particularly prominent as people add messaging apps to their personal and professional workflows as a complement to e-mail.
Symphony’s Cloud9 has been awarded ‘Best Sell-Side Trading Communication System’ at WatersTechnology’s Sell-Side Technology Awards 2025.
In this interview we hear how Cloud9 differentiates itself with a modern, cloud-native approach to trader voice. It requires no on-premise infrastructure and functions primarily as a software application, aligning with broader cloud-based communication trends.
Cloud9’s flexible architecture empowers technology teams with robust deployment, while seamlessly supporting remote and mobile work. Michael Lynch also explains the plans for the mobile app and how it will extend voice beyond the desk to users on the go, offering 24/7 trading control.
Watch the interview to learn more.
Symphony’s leadership team have shared their expertise regarding the impact to the industry and offer valuable insights into how firms can collectively respond to information security threats. Here are 7 key insights from these conversations:
