NoSQL B

Which of the following types of NoSQL database might be best for the following scenarios:

Scenario 1: A retailer’s webapp has 3 pages that customers can view. Each page displays different parts of a customer’s information: 1) Their account details + billing information 2) Their previous purchased items 3) Their bookmarked/favourite items. Which type of nosql database might be most suitable for this?

Scenario 2: This same webapp also needs to store images, videos, and gifs for each of the products. Which type of database might be most suitable for this?
1: Scenario 1
2: Scenario 2

Question

NoSQL B

Which of the following types of NoSQL database might be best for the following scenarios:

Scenario 1:  A retailer’s webapp has 3 pages that customers can view. Each page displays different parts of a customer’s information: 1) Their account details + billing information 2) Their previous purchased items 3) Their bookmarked/favourite items. Which type of nosql database might be most suitable for this?

Scenario 2: This same webapp also needs to store images, videos, and gifs for each of the products. Which type of database might be most suitable for this?
1: Scenario 1
2: Scenario 2

BlackTom AI · Accepted Answer

Here’s how to evaluate the NoSQL options against the two scenarios, with attention to what each data model excels at.
Option 1: Graph Database
This model specializes in traversing relationships between entities. Scenario 1 involves page-level views of a customer’s account details, billing info, past purchases, and bookmarks. A graph database isn’t primarily about organizing data into pages or multi-field entities for quick lookup across disparate sections; it’s better when you need to navigate complex interconnections. Since the scenario emphasizes retrieving different views of a single customer's data rather than querying intricate relationships, a graph database would be less natural here.
Option 2: Column Family
Column-family stores (like HBase) organize data into rows with a flexible set of columns, grouped into column families. For Scenario 1, this model is well-suited because you can store a single row per customer and structure the data into column families such as account/billing, purchases, and bookmarks. This supports efficient, wide schemas where you can fetch only the needed column families for a given view, reducing unnecessary data transfer. In practice, this aligns with the need to assemble multiple, loosely related sections of a customer profile in a scalable way.
Option 3: Document Store
Document databases store entire documents (e.g., JSON) as units. For Scenario 1, a document per customer could also work well, consolidating account, billing, purchases, and bookmarks into one document. That can simplify reads of holistic customer profiles but might complicate updates if different views require separate access patterns. It’s a viable alternative, but it may be less flexible than a column-family approach if you want very fine-grained control over which fields are loaded per view.
Option 4: Key-value Store
Key-value stores are excellent for simple lookups by a key, offering fast retrieval of a value when you know the key. Scenario 2 involves storing images, videos, and GIFs for each product. If you store those assets as binary blobs and manage metadata separately, a key-value store can be effective for straightforward, scalable retrieval by a product-based key. However, for large binary assets, many deployments pair a key-value store with a dedicated object storage system, using the key to reference the asset stored elsewhere. In that sense, a key-value model is a good fit for asset addressing, but it’s often part of a larger storage pattern.
Scenario 2: applying the same reasoning to the options, the simplest fit for media files is to store the media in an object store (or blob storage) and keep lightweight metadata or references in a NoSQL store. If constrained strictly to the provided NoSQL types, a key-value store remains the most natural choice for rapid retrieval by a product key, especially when images/videos/gifs are treated as opaque binary values.
In summary, for Scenario 1, Column Family aligns well with a structured, partitioned representation of customer data across multiple views, while Graph Database and Document Store offer alternative but less targeted advantages. For Scenario 2, Key-value store is a sensible fit for direct asset references or binary blobs, particularly when paired with external object storage for the actual media files.

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