Disadvantages of NoSQL DBs

1. The Death of the "True" Join

In NoSQL, native cross-collection relationships do not exist. In an RDBMS, you normalize data across tables and link them using foreign keys.

MongoDB: While you can use the $lookup aggregation stage to perform a left-outer join, it is highly resource-intensive. If your query filters heavily on non-indexed joined fields, performance degrades rapidly.
Cosmos DB: It does not support joining data across separate containers natively. To join data, you must self-join within the same item, query collections separately in application code, or completely change your architecture to de-normalize your data.

2. Duplicate Data and Memory Bloat

NoSQL is to duplicates data across multiple documents.

Data Duplication: If a user updates their username, your application code must hunt down and update that string across every single order, comment, and document where it was copied. This increases the risk of data inconsistency.
Storage Overhead: MongoDB stores field names (e.g., "first_name": "John") inside every single document. Across billions of records, repeating these structural strings consumes massive amounts of RAM and disk space compared to a rigid SQL table structure.

3. The "Distributed System" Consistency Tax

By defaulting to horizontal scaling, these databases prioritize availability over immediate consistency (the CAP theorem).

Eventual Consistency: When data is written to a node, it takes time to replicate across a global cluster. A user might post a comment, refresh the page, and not see it immediately because their read request hit a replica node that hasn't synchronized yet.
Cosmos DB mitigates this by offering five tunable consistency levels, but opting for "Strong Consistency" dramatically increases latency and reduces throughput.

4. Predictable Cost Explosion (Cosmos DB Specific)

Cosmos DB relies on a proprietary pricing model called Request Units (RUs).

You pay for provisioned throughput per second. If a developer accidentally writes an unindexed, cross-partition query (a "scatter-gather" query), it will consume massive amounts of RUs instantly, causing Azure to throttle application traffic or trigger massive bill spikes.
Cosmos DB enforces a strict 2 MB payload limit per document. If a single document grows past this, the write fails, requiring complex architectural workarounds.

5. Proprietary Lock-In and Emulation Gaps

Cosmos DB Lock-in: Moving a system natively built on Cosmos DB's SQL API off of Microsoft Azure is highly complex, effectively binding your architecture to one cloud provider.
The Emulation Trap: Cosmos DB features a "Cosmos DB API for MongoDB" designed to run Mongo code seamlessly. However, it operates as an emulation layer and frequently lags behind the latest native MongoDB versions, occasionally failing to support complex aggregation operators, server-side sessions, or specialized indexing commands.

6. No Database Transactions

When Not to Use NoSQL:

Financial Ledgers & ERPs: Where absolute, immediate ACID transactional integrity across hundreds of disparate entities is mandatory.
Highly Interconnected Data: Applications like HR systems or supply chains, where everything links back to everything else, are best left to relational or dedicated graph databases.
Unpredictable Ad-Hoc Analytics: Business analysts who need to run highly complex, randomized SQL queries on data fields that developers didn't index ahead of time.

Ones and Zeros