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Summary – Facing a surge in DynamoDB requests, latency climbs and consistency can suffer, while DAX provides a multi-AZ managed in-memory cache but limits certain operations, incurs recurring costs and deepens AWS lock-in. This article outlines how DAX works (multi-AZ clusters, read/write-through and write-back), its gains on simple reads, its limitations (unsupported operations, architectural complexity) and compares alternatives (Redis, KeyDB, ElastiCache, CQRS/event-sourcing patterns, cloud-native databases). Recommended: use DAX for highly read-intensive workloads if TCO and consistency are acceptable; otherwise, choose a modular architecture with open-source caching or distributed patterns, backed by a contextual assessment and expert support.
In digital environments where performance and latency make all the difference, AWS DynamoDB remains a preferred choice for Swiss companies. However, when read request volumes rise, even DynamoDB can exhibit latencies that fall short of near-real-time expectations.
It is in this context that DynamoDB Accelerator (DAX) comes into play—a managed, distributed, in-memory cache by AWS capable of reducing the latency of simple operations. This article details the key mechanisms of DAX, its benefits and constraints, before comparing it with open-source and cloud-native alternatives. It also offers criteria to balance latency, consistency, technological openness, and total cost of ownership.
When to Use AWS DAX
DAX significantly speeds up simple read operations on DynamoDB by leveraging a multi-Availability Zone distributed in-memory cache. These performance gains are optimal for read-heavy workloads such as e-commerce or real-time personalization.
Understanding the three caching strategies built into DAX enables you to quickly determine if this managed service meets your project’s latency and consistency requirements.
How DAX Works and Its Multi-AZ Architecture
The DAX cluster is deployed across multiple Availability Zones to ensure high availability and fault tolerance. Each node keeps data in RAM, enabling millisecond response times. This architecture eliminates disk storage for reads, offering speed unmatched by direct DynamoDB queries.
Communications between the application and the DAX cluster occur through the standard DynamoDB API, with no major code changes required. The client extension integrates easily into Java, .NET, or Python environments, while maintaining compatibility with GetItem, Query, and Scan requests. This approach simplifies adding a cache without overhauling the existing architecture.
In case of a node failure, DAX automatically reroutes requests to the remaining instances, ensuring continued service. The cluster can be scaled up or down on the fly to adapt to traffic changes, while AWS manages maintenance and security updates, relieving the operations team from cache administration tasks.
Built-in Caching Strategies
The read-through strategy queries the DAX cache first for every read operation. If the data is missing, DAX fetches it from DynamoDB, stores it in memory, and returns it to the application. This drastically reduces the number of direct database calls, lightening the load on DynamoDB.
The write-through strategy ensures consistency between the cache and the database. On each write, DAX simultaneously propagates the update to DynamoDB and updates its local cache. This real-time synchronization prevents divergence, at the cost of a slight increase in write latency.
The write-back strategy, on the other hand, allows a delay before data is persisted in DynamoDB. Writes are held in the cache for a configurable period, then batch-replicated to the database. This mode reduces write pressure on DynamoDB but must be used cautiously to avoid data loss in case of disaster.
Typical Read-Intensive Use Cases
E-commerce sites with large product catalogs benefit from an in-memory cache to speed up product page loading, even during traffic spikes. Similarly, real-time personalization platforms leverage DAX to display recommendations or promotions without introducing visible latency for the user.
Example: A mid-sized e-commerce company integrated DAX for its product recommendation flows. Before DAX, response times for dynamic queries exceeded 25 milliseconds, affecting the customer journey. After enabling the cache, average latency dropped to 4 milliseconds, while cutting DynamoDB read capacity unit costs by 60%. This example shows that a managed service can quickly boost performance without a complete infrastructure overhaul.
In practice, DAX is especially relevant when serving a high volume of GetItem or Query requests on partitioned tables. In these scenarios, the cache acts as a memory-powered turbocharger, freeing the direct query pool to DynamoDB and optimizing overall infrastructure cost.
Constraints and Limitations of DAX to Consider
Despite its efficiency for simple reads, DAX has functional limitations and technical incompatibilities that restrict its universal adoption. Some advanced operations and secondary indexes are not supported, leading to complex workarounds.
Moreover, using DAX can introduce consistency risks and increased operational complexity, while adding recurring costs for an additional managed service.
Unsupported Operations and Incompatibilities
DAX does not support UpdateItem, BatchWriteItem, BatchGetItem, or scans with complex filters. Developers often have to implement additional application logic to work around these restrictions, which increases code maintenance overhead.
Similarly, certain local or global secondary indexes do not work with DAX, forcing table design revisions or multiple direct queries to DynamoDB. This can result in a hybrid pattern where some queries bypass the cache, complicating the read-write management scheme.
Example: A Swiss public organization had counted on DAX for its event logs with TTL on items. Since DAX does not support automatic in-memory TTL deletions, the team had to deploy an external purge process. This implementation highlighted that the native DAX ecosystem does not cover all needs and sometimes requires additional components to ensure data compliance and freshness.
Consistency Risks and Architectural Complexity
Although attractive for reducing write load, the write-back strategy can introduce a temporary delta between the cache and the source of truth. In the event of a cluster reboot or extended failover, some data may be lost if it has not been synchronized. This fragility necessitates monitoring and recovery mechanisms.
Adding a third-party managed service also requires revisiting network topology, managing IAM authentication or security groups, and setting up specific metrics to monitor cache health. The infrastructure becomes heavier and demands advanced DevOps skills to operate continuously without service disruption.
Overall, DAX remains a specialized component that must be integrated carefully into already complex architectures. Teams spend time documenting where the cache is used, orchestrating autoscaling, and controlling consistency during simultaneous data updates.
Additional Costs and Vendor Lock-In
Using DAX incurs additional costs proportional to the number of nodes and instance types chosen. For a 4-node, multi-AZ cluster, monthly fees can add up quickly, not to mention the impact on network bills in a private VPC. To estimate total cost of ownership, see our article on Capex vs Opex in Digital Projects: What It Means for Swiss Companies.
Relying on DAX strengthens a company’s dependency on a specific AWS service that is less flexible than an open-source cache deployed on EC2 or Kubernetes. Migrating later to an alternative solution involves complex changes at both code and infrastructure levels, representing a non-negligible transition cost.
Therefore, financial trade-offs must include Total Cost of Ownership, taking into account managed service fees, associated operational costs, and vendor lock-in risks. In some scenarios, a self-hosted solution or a hybrid approach may be more attractive in the medium to long term.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
Scalable, Less Locked-In Alternatives to Consider
To maintain technological flexibility and avoid severe vendor lock-in, other open-source and cloud-native solutions offer comparable or superior performance depending on the context. Redis or KeyDB, ElastiCache, and more scalable databases allow architecture adaptation to business requirements.
Architectural patterns like CQRS with event sourcing or distributed application caches also help separate read and write concerns, optimizing both scalability and maintainability.
Redis, KeyDB, and ElastiCache for a Flexible In-Memory Cache
Redis and its fork KeyDB provide a versatile in-memory solution capable of storing complex data structures and handling high concurrency. Their active communities ensure frequent updates, enhanced security, and compatibility with various languages and frameworks. For an overview of database systems, see our Guide to the Best Database Systems for Swiss Companies.
ElastiCache, AWS’s managed version of Redis, strikes a balance between reduced maintenance and optimization freedom. Snapshots, read scaling, cluster modes, and Redis Streams support are all features that allow fine-tuning based on business needs.
Unlike DAX, Redis natively supports disk persistence, TTL management, transactions, and Lua scripting, offering either strong or eventual consistency depending on configuration. This flexibility lets you tailor the cache to varied use patterns and minimize application workarounds.
Implementing CQRS and Event Sourcing Patterns
The CQRS (Command Query Responsibility Segregation) pattern separates read and write paths, allowing each to be optimized independently. Leveraging an event-driven architecture, commands feed a persistent event stream that can be replicated to a read-optimized datastore, such as Redis, ScyllaDB, or a relational database with read replicas.
Combining CQRS with event sourcing, state changes are stored as events. This approach facilitates auditing, replaying, and reconstructing historical states. The read system can then supply ultra-fast materialized views without directly impacting the transactional database.
Companies can handle millions of events per second while maintaining excellent read responsiveness. The clear separation of responsibilities simplifies schema evolution and horizontal scalability, avoiding overloading transactional tables with analytical queries or wide scans.
Cloud-Native Databases for Global Scalability
PostgreSQL with read replicas, offered by RDS or Aurora, provides a robust relational foundation while offloading part of the read workload. Combined with sharding and partitioning, it can handle large data volumes without resorting to a separate cache for every simple operation.
For massively distributed workloads, NoSQL databases like ScyllaDB or Cassandra ensure uniform latency and fast writes thanks to their decentralized architecture. These open-source solutions can be deployed on Kubernetes or in managed cloud mode, minimizing lock-in risks.
Adopting these complementary databases requires adjusting application logic and data workflows but offers a broader innovation path for companies seeking cost control and autonomy over their tech stack.
Criteria for Balancing Latency, Consistency, and Technological Openness
Every project must define its priorities in terms of response time, consistency guarantees, and degree of technological freedom. This trade-off phase determines the architecture’s longevity and total cost of ownership.
Partnering with a strategic advisor capable of proposing a contextual approach and integrating open-source components, managed services, and custom development makes all the difference.
Defining Key Indicators for the Trade-Off
The analysis should focus on the target latency in milliseconds, the volume of concurrent requests to support, and the required consistency level (strong, eventual, or configurable). These parameters drive the choice between an in-memory cache, a distributed database, or a mix of both.
Total Cost of Ownership should include the direct cost of managed services or licenses, operational maintenance costs, and long-term migration expenses. Additionally, indirect costs related to architectural complexity and vendor dependency risk must be considered.
Finally, technological flexibility—the ability to switch solutions without a major overhaul—is an essential factor for organizations looking to control their roadmaps and anticipate future market evolution.
Hybrid Architecture and Modularity
A modular approach combines an in-memory cache for critical reads and a distributed database for persistence. Microservices or serverless functions can query the most appropriate component based on the transactional context and performance objectives.
Clearly defined responsibilities promote reuse of open-source modules, integration of managed services, and custom development of specific modules. This hybrid architecture limits change propagation and simplifies scaling by adding targeted nodes.
With this modularity, teams can test various technology combinations, compare results, and adjust cache or database configurations without impacting the entire system.
Contextual Approach and Strategic Support
Defining an optimal solution relies on a precise assessment of business context, data volume, traffic peaks, and security requirements. This audit phase enables recommending a mix of DAX, Redis, CQRS patterns, or distributed databases according to identified priorities.
Example: A Swiss company in financial services sought ultra-fast response for near-real-time dashboards. After evaluation, the team favored a managed Redis cluster paired with a CQRS pattern over a DAX cluster. This choice ensured strong consistency while guaranteeing scalability and controlled total cost of ownership. This example demonstrates the importance of thorough contextual analysis and strategic partnership in guiding the decision.
Choosing the Right Caching Strategy for DynamoDB
AWS DAX is a high-performance accelerator for read-intensive use cases, but its limited feature coverage and additional cost reserve it for specific scenarios. Open-source alternatives like Redis or KeyDB, more open managed services, and CQRS patterns offer greater flexibility and better control over Total Cost of Ownership. The trade-off between latency, consistency, and technological openness should be based on precise indicators and contextual analysis.
