Data Access:
Data access becomes much more complex when you move to a microservices architecture. Because in microservices we encapsulate the data to ensures that the microservices are loosely coupled and can evolve independently of one another. The data owned by each microservice is private to that microservice and can only be accessed via its microservice API.
Challenges are :
If multiple services were accessing the same data, schema updates would require coordinated updates to all the services. This would break the microservice lifecycle autonomy
You can't make a single ACID transaction across microservices because it means that we must use eventual consistency when a business process spans multiple microservices. This is much harder to implement than simple SQL joins, because you can't create integrity constraints or use distributed transactions between separate databases.
Different microservices often use different kinds of databases. How do we deal with that?
Boundaries:
How to define the boundaries of each microservice?
We know that we have to define multiple fine grained services but how do we define responsibilities of each service? How do we know their logical boundaries?
For Solution refer to: Identifying Boundaries
Retrieve data between multiple microservices:
How to create queries that retrieve data from several microservices?
Challenging part is we want to avoid chatty communication to the microservices from remote client apps. And not only that, we want to improve the efficiency in the communications of your system.
Solutions are:
API Gateway: This is a service that provides a single-entry point for certain groups of microservices. It's similar to the Facade pattern from object-oriented design, but in this case, it's part of a distributed system.
CQRS with query/reads tables: aggregating data from multiple microservices is the Materialized View pattern. In this approach, you generate, in advance (prepare denormalized data before the actual queries happen), a read-only table with the data that's owned by multiple microservices. The table has a format suited to the client app's needs.
"Cold data" in central databases
Consistency across multiple microservices
How to achieve consistency across multiple microservices?
Challenge is the data owned by each microservice is private to that microservice and can only be accessed using its microservice API. Therefore, a challenge presented is how to implement end-to-end business processes while keeping consistency across multiple microservices?
A good solution for this problem is to use eventual consistency between microservices articulated through event-driven communication and a publish-and-subscribe system.
Communication across microservice
How to design communication across microservice boundaries?
In a distributed system like a microservices-based application, with so many artifacts moving around and with distributed services across many servers or hosts, components will eventually fail. Partial failure and even larger outages will occur, so you need to design your microservices and the communication across them considering the common risks in this type of distributed system.
Solution can be found at: Communication in Microservices
Adopting microservices is a trade-off: you buy agility and scalability, but you pay with complexity. As the architecture has matured, the nature of these challenges has evolved. Below are the foundational hurdles every architect faces, followed by the emerging challenges of the AI/Cloud-Native era.
These remain the primary hurdles for any team moving from Monolith to Microservices.
In a monolith, a JOIN query solves everything. In microservices, each service owns its private database to ensure loose coupling.
The Challenge: You cannot perform a single ACID transaction across services. Implementing business processes that span multiple services (e.g., "Order Placed" $\rightarrow$ "Inventory Reserved" $\rightarrow$ "Payment Charged") requires Eventual Consistency, which is significantly harder to debug than a SQL transaction.
The Solution: Use Event-Driven Architecture and the Saga Pattern to manage distributed transactions via a publish-subscribe model.
The Challenge: Where do you draw the line? If a service is too large, it becomes a "distributed monolith." If it's too small, the overhead of network calls destroys performance.
The Solution: Adhere to Domain-Driven Design (DDD). Align services with "Bounded Contexts" (business capabilities) rather than technical layers (e.g., "User Service" vs. "Database Service").
The Challenge: A simple user screen might need data from the User, Orders, Shipping, and Recommendations services. Calling each one individually from the client app creates "chatty" communication and high latency.
The Solution: Implement a Supergraph (Federated GraphQL) or an API Gateway to aggregate data on the server side before sending a single response to the client.
Now that microservices are the standard, we face a new set of operational hurdles involved in running them at scale with AI.
The Challenge: In the Latest cloud environments, bills are no longer a single line item. With thousands of ephemeral "nano-services" and serverless functions scaling automatically, it is easy to accidentally burn 50% of your budget on a misconfigured retry loop in a non-critical service.
The Reality: Developers often provision high-performance resources for services that rarely run.
The Latest Solution: Implement Cost-Aware Architecture. Use automated policy-as-code tools (like OpenPolicyAgent) that prevent deployment if the estimated resource cost exceeds the service's "Business Value Score."
The Challenge: Microservices are deterministic and fast (milliseconds). AI Agents are probabilistic and slow (seconds). When you embed a Generative AI step into a synchronous microservice chain, you block the entire workflow, causing timeouts across the system.
The Reality: "Timeouts" are the new "NullReferenceException."
The Latest Solution: Move AI processing to Async Agentic Workflows. Never let a user-facing API wait for an LLM. instead, return a 202 Accepted and let the AI agent callback via a webhook or WebSocket when it's done reasoning.
The Challenge: We used to worry about not having logs. Now, with Distributed Tracing (OpenTelemetry) enabled on thousands of pods, we are generating petabytes of telemetry data. Finding the root cause of an error is like finding a needle in a stack of needles.
The Reality: Engineers ignore alerts because there are too many of them.
The Latest Solution: AIOps (AI for IT Operations). We no longer look at raw dashboards. We use AI models to consume the telemetry stream, correlate the noise, and present the engineer with a single "Root Cause Summary" rather than 10,000 individual error logs.
The Challenge: A single microservice might depend on 50 open-source libraries. Multiply that by 100 services, and you have 5,000 potential entry points for a security vulnerability.
The Reality: Keeping 100 different package.json or .csproj files updated is a full-time job.
The Latest Solution: Automated SBOM (Software Bill of Materials) Management. Pipelines now automatically block builds if a dependency in any microservice is outdated or flagged in the CVE database, forcing an "Update or Fail" policy before code ever reaches production.