Just Consulting

Database Design and Considerations

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In e-commerce, databases are essential for storing product information, managing inventory, tracking customer orders, and much more. They play a crucial role in ensuring smooth operations and providing a seamless user experience across all aspects of an online store.

Database design is dependent upon a wide range of factors, and legacy integrations can play a large part in that. Many Ecommerce websites have Shopify, Salesforce, or other integrations which your company may want to keep, as they currently serve as the source of truth for your larger business, so workarounds or data sync pipelines or jobs may have to be implemented. These workarounds could involve periodic data synchronization jobs, API integrations, or even custom middleware to ensure that your e-commerce platform remains consistent with the existing systems. It's crucial to carefully plan these integrations to maintain data integrity and minimize disruptions to ongoing operations.

There are a large number of different storage solutions available to chose from, and as with any technology, you will want to evaluate the pros and cons of each before choosing to adopt it. The following is a short list of a few of the options I considered for this website.

  1. PostgreSQL: PostgreSQL is a tried and tested relational database and supports advanced features.
  2. MongoDB: A NoSQL database that is scalable and works well with JSON data
  3. Sanity PostgreSQL: Sanity comes with a psql database by default for all CMS data - this can be used as the core database if desired, but is not advisable.

Relational Databases

  1. PostgreSQL
    • Advanced features like JSONB support and full-text search.
    • Excellent for complex queries and data integrity.
    • Strong support for concurrent transactions.
  2. MySQL
    • Widely used and well-supported.
    • Strong community and extensive documentation.
    • Good for structured data and complex queries.
  3. Microsoft SQL Server
    • Good integration with other Microsoft products.
    • Strong transactional support and security features.

NoSQL Databases

  1. MongoDB
    • Flexible schema design, suitable for varying product attributes.
    • Great for unstructured data and rapid development.
    • Good performance for large volumes of data.
  2. Cassandra
    • Highly scalable and designed for high availability.
    • Suitable for large datasets across multiple servers.
    • Offers excellent write performance.
  3. DynamoDB
    • Fully managed NoSQL database service by AWS.
    • Scalable and offers high availability.
    • Good for applications requiring low-latency responses.

Graph Databases

  1. Neo4j
    • Ideal for managing complex relationships, such as product recommendations.
    • Supports advanced querying capabilities for graph data.

Document Stores

  1. Couchbase
    • Combines the capabilities of both a document database and a key-value store.
    • Good for applications needing high performance and flexibility.

Time-Series Databases

  1. InfluxDB
    • Best for time-based data, such as tracking user behavior over time.
    • Efficient for logging and metrics.

Hybrid Databases

  1. Firebase Realtime Database / Firestore
    • Great for real-time applications, allowing for instant updates.
    • Managed by Google, which simplifies scaling and maintenance.