Factors Influencing Database Choice

The choice of the right database ensures a significant impact on the performance, scalability, and overall success of an application. Let’s explore the essential factors that influence database choice.

• Data model and query pattern
• CAP Theorem
• Scale, performance, and interoperability
• Security and Compliance
• Development, Operational Overhead, and Cost

1. Data model and Query Pattern

Data modeling is the foundation of the database design process. Designing a logical structure that meticulously represents real-world entities, attributes, and their relationships ensures data integrity and optimal storage efficiency. The data modeling is heavily influenced by the nature of the data. It can be structured or unstructured, or it represents a domain entity and relationships among the domain entities. The data storage approach can vary from being a simple key-value to storing a document against a key.

Additionally, you should consider the query pattern because application read and write patterns are a key factor in choosing the right database. For instance, if the application needs to fetch data by key, then all you need is a key-value database, or if it needs to query by many different fields, you can choose a relational database, and so on.

2. CAP Theorem

The CAP theorem refers to the three crucial distributed system characteristics: consistency, availability, and partition tolerance. It states that any data store can guarantee only two of the three characteristics. Both the ACID and BASE models are governed by the CAP theorem.

Suppose the use case demands consistency and partition tolerance, then the master needs to block the transaction for a particular key until the slaves get updated. Here, any updates against the key will be blocked during the synchronization process, thereby trading off its availability. Examples include the banking system (highly consistent) and Youtube (highly available).

The ACID model is mostly used in RDBMS because it needs to be highly consistent, and the BASE model is mostly used in NoSQL because it demands availability.

3. Scalibility, Performance, and interoperability

Based on the volume of data and requests to the database, the scale requirements can be identified. A system can be scaled vertically (increasing the power of a single server) or horizontally (adding additional nodes to share the load).

Before implementing scaling techniques, it is important to understand the application’s performance. By understanding read-heavy and write-heavy queries, developers can opt for appropriate optimization techniques and determine the database latency requirements.

Interoperability is an important aspect if there is a need to access and process data from multiple sources. In such cases, the database should integrate well with adjacent technologies in your architecture. For example, consider the healthcare industry. It depends on multiple sources of information from multiple systems.

4. Security and Compliance

Basic security mechanisms like access control, authentication, authorization, encryption, backup, and recovery can minimize security risks. The system should protect sensitive data from unauthorized access.

In today’s digital age, compliance protocols are no longer just recommendations and can vary based on industry. It includes handling sensitive data, privileged users, the type and frequency of audit reports, and ensuring the database complies with regulations such as GDPR or CCPA.

5. Development, Operational Overhead, and Cost

Every database requires maintenance to ensure optimal performance. For instance, moving data from a transactional database to a data lake will reduce storage stress on the transactional database and optimize computation-heavy analytics queries. Some databases are serverless, and for others, we need to create and manage our own cluster.

Developers should take care of optimization techniques such as purging deleted rows, resequencing, compressing, managing index paths, defragmentation, etc. Also, evaluate how steep the learning curve will be for a newbie.

Related to cost, It’s not just about the upfront cost but also the long-term cost associated with scaling, maintenance, and support.

In the fast-paced digital era, transformation in technology happens as never before, and the data generated by applications is growing by leaps and bounds, nearly always. Businesses and developers are seeking a formidable choice: a robust and efficient database solution to harness the true potential of data.

Let’s unlock the power of data and fuel the critical decision-making strategy by picking the right databases and providing effective database management and optimization techniques.