DynamoDB pain points: How to address them and exploring possible alternatives

DynamoDB is a robust NoSQL database that has become increasingly popular due to its performance, scalability, relative simplicity, and low operational overhead. However, it has its drawbacks. Some of the main disadvantages of DynamoDB include limited querying options, limited storage capacity (400kb per document), no multi-region ACID support (eventual consistency), and difficulties in replicating data in multi-region setups. … ## Limited querying capabilities If you have worked with DynamoDB to some extent, then you know that relational data modeling is tedious with DynamoDB. There are no foreign key joins in DynamoDB like most people are used to in relational databases. You can, however, handle complex data access patterns similar to relational databases. ... While DynamoDB global tables provide a way to replicate data across multiple regions, it can be a complex and costly process that requires additional setup, management, and code — and it may only support some features or data replication across all indexes. You have to create the same table in multiple regions with the same schema, indexes, and throughput settings. Additionally, data replication between different regions needs to be configured and managed. There is no zero-configuration solution to replicate data across multiple regions with DynamoDB. Furthermore, certain features are not available in global tables. **DynamoDB Time to Live**(TTL), which is the ability to expire items after a specified period of time, is not available with global tables. Another unsupported feature is **DynamoDB Streams**. Additionally, **local secondary indexes (LSIs)**and **global secondary indexes (GSIs)**are not replicated across all replicas in global tables. You must manually create those LSIs and GSIs in different regions; otherwise, you cannot query data efficiently. … ### Storage limitations Another pain point with DynamoDB is the item size limit. An individual item has a maximum of 400kb in size. The item size limit impacts the way data is modeled. The application may need to denormalize the data or split it across multiple items to work within the item size limit. The storage limitation is not a big deal for most applications out there, but if you plan to grow and scale your application to millions of users, you should start thinking about the additional complexity you need to handle. DynamoDB competitors such as MongoDB, Fauna, or Cassandra don't have this issue. ... However, as you build and scale your application with DynamoDB, you must be aware of some common pitfalls. As discussed in the previous section, an individual item in DynamoDB can be at most 400kb. While building with DynamoDB, it is crucial to understand the data model that best fits your application's use case and design the table schema accordingly. Failing to do this at the start will cost you as you scale your application. … ## Conclusion To conclude, DynamoDB is well-suited for use cases that require high scalability, high performance, and low latency. It seamlessly integrates with other AWS services and supports document and key-value data models. However, it also comes with its limitations. Limited query capabilities, little support for the transaction, eventual consistency, and limited support for analytics are some of the main pain points with DynamoDB. With enough engineering resources and time, you can overcome these pain points.