How to Select the Right Database

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Read more about author Vivek Parate.

In today’s data-driven world, technologies are changing very rapidly, and databases are no exception to this. The current database market offers hundreds of databases, all of them varying in data models, usage, performance, concurrency, scalability, security, and the amount of supplier support provided. 

Choosing a database is a different class of challenge. Selecting the right database for your business is not an easy task. The ability to make a rigorous, informed choice of database technology requires detailed knowledge of the following:

  1. Understanding of business needs
  2. Technical evaluation
  3. Skill set mapping

Understand Your Business Needs

Regardless of the type of database you’re considering, the first critical step is defining your business needs. For a minor purchase, this step might involve a quick conversation with other staff, but for a large, mission-critical piece of software, it might take months of work.

Key driving factors for the database selection process include answers to the following questions:

  • What is the business application?
  • What is the nature of the data you looking to store?
  • What data growth are you expecting?
  • What are the implications if the database goes down?
  • What is the frequency of data access?
  • What ACID properties does your business demand?

Let’s consider an example: If your application demands flexibility in saving dynamic content of data, then you might not go for a relational database, but rather might prefer a document store or key-value database.

Business demands for unstructured data have different varieties of databases like S3 object stores, file-based systems, etc.

Technical Evaluation 

Any database will support writing data and reading it back out again. Some databases allow querying on arbitrary fields. Some provide indexing for rapid lookup. Some support ad hoc queries, while queries must be planned for others. The simple reason why there are so many different database systems is that it is not possible for any system to achieve all desirable properties at once.

Common universal database components that are important for any database selection process include:

  • Storage engine
  • Query processor
  • Query language
  • Metadata catalog
  • Optimization engine
  • Sharding or partition
  • Data availability
  • Scaling 

When selecting a database, technical evaluation is a critical part and any database performance depends on what it has built inside.

Storage Engine: The storage engine is the core component of the database management system (DBMS) that interacts with the file system at an OS level to store data. All SQL queries that interact with the underlying data go through the storage engine.

Query Processor: This is the intermediary between the user queries and the database. The query processor interprets the queries of users and makes them actionable commands that can be understood by the database to perform the appropriate functionality.

Query Language: A database access language is required for interacting with a database, from creating databases to simply inserting or retrieving data. In many query languages, the query language functionality can be further categorized according to specific tasks:

  • Data Definition Language (DDL): This consists of commands that can be used to define database schemas or modify the structure of database objects.
  • Data Manipulation Language (DML): Commands that directly deal with the data in the database. All CRUD operations come under DML.
  • Data Control Language (DCL): This deals with the permissions and other access controls of the database.
  • Transaction Control Language (TCL): Command that deals with internal database transactions.

Metadata catalog: This is the centralized catalog of all the objects within the database. When an object is created, the database keeps a record of that object with some metadata about it using the metadata catalog. 

Sharding: Sharding is a method for distributing a single dataset across multiple databases, which can then be stored on multiple machines. This allows for larger datasets to be split in smaller chunks and stored in multiple data nodes, increasing the total storage capacity of the system. Sharding can be:

  • Key-based sharding/hash sharding
  • Range-based sharding
  • Dictionary-based sharding

Partition: Partition divides the data into some logical form using partition key/keys. Database partitioning is normally done for manageability, performance, or availability reasons.

Data availability: Database high availability is a critical component of application high availability, but it’s not the complete story. Some situations (e.g., regional disasters or systemic corruption) require proper backup and restore mechanisms. And again, not all databases offer the same level of functionality here.

Scaling: Scalability describes a system’s elasticity. It refers to a system’s ability to grow. You can scale down, scale up, and scale out accordingly. Good scalability protects you from future downtime and ensures the quality of your service. Horizontal scaling means scaling by adding more machines to your pool of resources (also described as “scaling out”), whereas vertical scaling refers to scaling by adding more power (e.g., CPU, RAM) to an existing machine (also described as “scaling up”).

In vertical scaling, the data lives on a single node and scaling is done through multi-core, e.g., spreading the load between the CPU and RAM resources of the machine.

Skill Set Mapping

Dealing with unknown technologies without proper guidance generally adds more uncertainty. If you are dealing with complex databases without having proper technical support, it would be a nightmare. Generally, people prefer to have a stable and popular database, and the main reason is having proper support and resources in the market.

The most important part of database evaluation is evaluating available skill sets and finding out missing skills within an organization before choosing the right databases. The following are a few important criteria for nontechnical evaluation:

  • Popularity of technology
  • Features it supports
  • Cost of products
  • Knowledge base or technical support
  • Available resources and helps
  • Availability of engineers and their costs


DBaaS (database as a service) is a cloud computing managed service offering model that enables users to set up, operate, manage, and scale with some form of access to a database without the need for setting it up on physical hardware, installing software, or configuring it for performance.

Cloud service providers provide database services in three categories:

  • NoSQL 
  • DW

Popular DBMS offerings include:

database management systems

Benefits of Database-as-a-Service

Agility: Cloud DBaaS applications are agile in nature, so they adapt seamlessly to any upgrades according to business or technology advancements. DBaaS allows rapid provisioning of database resources to provide new computing resources and storage facilities in the minimum possible time. 

Safeguard Your Data: Security is one of the most critical challenges in the DBaaS domain. As more and more enterprises host their data in the cloud, it’s crucial for DBaaS providers to prevent unauthorized access to data resources, disallow misuse of data stored on third-party platforms, and ensure data confidentiality, integrity, and availability.

Scale According to Business Needs: The DBaaS model provides automated and dynamic scaling. DBaaS providers adapt to workload changes and can manage load variations by increasing resources during peak hours without any service disruption, or by allocating fewer resources during periods of non-peak usage to help reduce costs. Users can quickly add storage and computing capacity to meet high processing demands while also defining usage threshold policies for how the system should behave during demand fluctuations.

High Availability: In today’s fast-paced digital world, maintaining 24/7 operational uptime is a must for any modern business. Outages are directly proportional to the loss of revenue. As digital transformation becomes more and more essential, it’s increasingly important that your application service should remain up 24/7 without any downtime. 

Increase operational efficiency: Since DBaaS is a service, you can start small with one node at a time and scale bigger without disrupting the business. Organizations can scale as they grow, which is far more cost-efficient; by adding one or more nodes at a time and then spinning down resources that are no longer needed, IT teams can prevent costly overages. 


When selecting the right database for business needs, there are multiple evaluation processes – starting from the business needs to operational management, and from skill set mapping to technical examination. Having the right tools and techniques improves operational efficiency and results in fewer disturbances. Selecting one of the best options from thousands of available choices is not easy and needs skillful people and subject experts.