How can you balance flexibility and scalability in your microservice data modeling?

For years we believed on an infinitely scalable and ultimately flexible lake or database that can store all of our data and serve all of our use cases. Which creates tons of problems, some of which are increased risk of downtime, slow and complex upgrades, connecting hundreds of applications to the same platform which need to deliver the data via different and fine tuned APIs, inefficiences in data federation with external data sources and a bunch of others that we can list here. We have to accept the reality that there will be different data platforms (lakes or databases) that we will need to interact with. In the microservices case we need to invest on a "data mesh" where each data platform is tuned for a domain and publishes the data.

Balancing flexibility and scalability in microservice data modeling is crucial for building resilient and adaptable systems. Here are some strategies to achieve this balance: Domain-Driven Design (DDD): Focus on understanding the business domain and model your microservices around these bounded contexts. This allows for flexibility within each service while ensuring scalability by breaking down the system into manageable, cohesive parts. Use of Polyglot Persistence: Select different databases for each microservice based on the specific requirements of that service. Event-Driven Architecture: API Contracts and Versioning: Caching and Denormalization: Monitoring and Feedback Loops: Automated Testing and Deployment

Each type of database has its strengths and should be considered based on the specific requirements of your application. Relational databases are suitable for structured data with clear relationships, while NoSQL databases offer flexibility, scalability, and tailored solutions for different data models (document-oriented, graph-based, etc.). In-memory databases focus on high-speed data access, and time-series databases are specialized for handling time-based data efficiently. The choice depends on the nature of your data, scalability needs, and the performance characteristics crucial for your application's success.

We don't need to decide on a data store, we need to decide on the standard APIs and data schemas, which can represent the data or the metadata. Ideally these should be commonly accepted industrial standards. Once we build this web of data stores, we can freely access, combine data from any source that can use any technology. What we need to decide on is the data federation layer, which is actually a virtual lake, with a high performance caching layer and a graph of data nodes and data in each node that we can use to build a contextual representation of our corporate data estate. I deeply believe depending the application and use case, we should have the freedom to choose the right data store for each but make sure they publish data products

I would underline the importance of commonly agreed industrial data standards. The wire format or communications protocol or file standards won't cut it because we can send any type of data using these. We need to also agree on a common language on the domain level, our APIs should leveraged strongly typed domain specific standards. Great examples are OPC-UA, IEC, ISO, Energistics, etc... standards that define what data will be sent and what are the definitions of each field, hierachy of measurements, etc... Each standard uses a stack of underlying technology protocols.

Applying common standards in microservice architecture ensures uniformity and efficiency across the system, promoting consistency, interoperability, maintainability, and scalability. API Standards, Data Interchange Formats, Service Discovery, Containerization Standards can be good for common standards best on use case .