Functional and Non-functional Requirements for Data Store Selection

In today's digital age, where data drives decisions and operations, selecting the right data store is pivotal. The right choice can propel a business forward, while the wrong one can spell disaster. But how do you make this choice? Enter: functional and non-functional requirements.

Functional Requirements: The 'What'

Functional requirements address what the system is supposed to achieve. They outline the specific functionalities and capabilities the data store must support. Let's take a simple e-commerce website as an example:

1. Product Searches: The site needs to let users search for products. Hence, the data store must support efficient search operations.
2. Ordering System: Users should be able to place orders, implying the need for transactional support in the data store. Consistency is must here.
3. User Profiles: The site will have user profiles, suggesting the need for a database that can store structured user data. especially key-value type

Non-Functional Requirements: The 'How'

Non-functional requirements, on the other hand, delve into how the system operates. They depict the quality, performance, and other operational characteristics of the system.

Back to our e-commerce website example:

1. Performance: The site should load quickly, and search results should appear within seconds. This calls for a data store optimized for performance.
2. Data Security: User payment and personal information must be securely stored, emphasizing the need for strong encryption mechanisms.
3. Scalability: As the user base grows, the data store should scale without hitches, ensuring that the increasing user demands are met without performance degradation.

Lets dive deep into these requirements one by one :

1. Functional Requirements

Functional requirements are related to the specific functionalities that the data store should support.

1. Data Model: The structure of your data plays a significant role. Do you need a relational model (like SQL databases), a document-based model (like MongoDB), a key-value store (like Redis), a columnar store (like Cassandra), or a graph model (like Neo4j)?
2. Query Capability: Depending on the kind of queries you'll be running, some databases might be more appropriate than others. For instance, if you need complex joins and transactions, a relational database might be best. For flexible schema-less data retrieval, a document store might suffice.
3. Consistency and Transaction Support: Some applications require strong data consistency and ACID transactions. Relational databases, such as PostgreSQL or MySQL, are designed with this in mind.
4. Schema Flexibility: If your data schema is likely to evolve over time, then a schema-less or flexible-schema database like MongoDB or CosmosDB might be more suitable. As mentioned before, evolving schemas require flexibility.

1. Fixed Schema: Traditional relational databases like Oracle, MySQL, or MS SQL.
2. Flexible Schema: Document stores like MongoDB, Cosmos DB, or columnar databases like HBase.
5. Data Size: The volume of data you expect to handle can influence your choice.
1. Small to Medium: Relational databases like MySQL, PostgreSQL, or SQLite often suffice.
2. Large: Columnar stores like Cassandra, or distributed systems like Hadoop or distributed versions of SQL databases can be more appropriate.
3. Very Large (Big Data): Solutions like Hadoop HDFS, Google Bigtable, or Amazon S3 with Big Data processing tools like Spark might be needed.
6. Data Relationship: The nature and complexity of the relationships between data entities can guide the choice.
1. Simple Relations: Relational databases can handle this efficiently with JOIN operations.
2. Complex Relations: Graph databases like CosmosDB graph model are designed to manage intricate relationships efficiently.
7. Data Movement: If your system requires synchronization, migration, or streaming of data, this becomes crucial.
1. Streaming: Kafka or RabbitMQ for event streaming, Spark Streaming for stream processing.
2. Migration/Synchronization: Tools like Apache NiFi, Talend, or database-specific tools like Oracle GoldenGate.
8. Data Lifecycle: How your data evolves and ages over time can dictate storage strategies and archival methods.
1. Short-Lived Data: In-memory databases like Redis or Memcached are perfect for temporary data.
2. Long-Term Storage with Occasional Access: Systems like Amazon Glacier or Hadoop HDFS can be more cost-effective.
3. Data Archival and Retrieval: Databases with in-built lifecycle management like Amazon S3's object lifecycle policies or Azure Storage account Blob storage..

2. Non-Functional Requirements

Non-functional requirements are related to how the system operates, rather than what specific operations it supports.

1. Scalability: If you anticipate a significant increase in data or query volume, consider databases that scale out easily. NoSQL databases like Cassandra or DynamoDB are known for their horizontal scalability.
2. Availability and Fault Tolerance: If you need high availability, especially across multiple regions, it's essential to look into databases that offer replication, failover mechanisms, and distributed data storage.
3. Latency: For applications that require low-latency data access (like real-time analytics), in-memory databases like Redis or in-memory options of relational databases can be beneficial.
4. Durability: How critical is it that once data is written, it is never lost? Many databases provide durable storage mechanisms to ensure data safety even in case of system failures.
5. Operational Ease: Consider the effort required to set up, maintain, monitor, and backup the database. Cloud offerings like Amazon RDS or Azure Cosmos DB provide managed database services that alleviate some operational concerns.
6. Security: Features related to authentication, authorization, encryption (at rest and in transit), and auditing can be vital based on the sensitivity of the data.
7. Cost: Total cost of ownership includes not only the cost of the database software (or service) but also hardware, operational, and maintenance costs.
8. Integration and Ecosystem: How well does the data store integrate with your existing tools, systems, and processes? The available drivers, plugins, and community support can be deciding factors.
9. Backup and Recovery: The ease with which you can backup data and recover from failures or data loss can be crucial, especially for critical applications.
10. Compliance: If you're in a regulated industry, you might need databases that support specific compliance requirements, such as GDPR, HIPAA, or PCI DSS.

Functional and non-functional requirements act as the guiding stars in the journey of selecting the right data store. The 'what' and 'how' of system needs, represented by these requirements, ensure that the chosen data store not only aligns with the present needs but also scales for the future, offering the optimal mix of functionality and operational excellence. With platforms like Azure offering a plethora of database services, understanding these requirements becomes even more crucial to harness the full potential of the digital realm.

Please share your experience and feedback in the comments and help everyone to learn and grow along with you. Happy Learning 💪