The transition from highly structured SQL to a NoSQL database is usually triggered by requirements for scalability, flexibility, and the inability to work with huge volumes of unstructured or semistructured data. The difference between SQL databases and NoSQL databases is that NoSQL databases do not have a concept of schema, or it is flexible and can be changed when needed, referring to today’s dynamic applications.

To make the right choice of the database, compare SQL and NoSQL and learn the basic differences between two types of systems before the migration. This tutorial is a guide to transitioning from an SQL to an NoSQL database. The first is to identify the key areas of difference; the second is to rate your data and needs correspondingly; and the third is to comprehend the work for making the transition.

How to Migrate from SQL to NoSQL: A Basic Tutorial

1. Identifying the current SQL database in your systems (array)

• Check your present schema, such as tables, relations, and constraints.
• If there is any carryover from previous systems or old data that may have to be improved upon, point it out.
• Rate transactional requirements as well as ACID properties of particular document transactions.

2. As you review the survey data results and determine your application requirements,

Determine the type of NoSQL database best suited to your application:

• Relational (tabular) databases (e.g., MySQL) for rows and columns data type such as tables or No SQL such as document-based databases–Mongo DB for the hierarchical data such as JSON.
• Caching or session management systems like Redis as a key-value store.
• Most commonly practical databases, for example, graphs like Neo4j, for dealing with highly relational data.
• Consider whether base consistency (eventual consistency) matches your application.

3. Assess the benefits (and overheads) of the Use Case for Migration.

• The recommended usage of NoSQL is when you have a constantly changing set of data or contain a large number of unstructured data.
• When it comes to applicational needs such as real-time analytics, horizontal scalability, and analytics at a high velocity, NoSQL is efficient.

4. The next thing to do in the SCoRE process is to design the target SQL schema.

Transition from a relational model to a more flexible structure:

• Always use embedded documents instead of normalized models if you are using MongoDB so that you can get rid of joins.
• Relate one to many relationships in to structures where those relationships can be naturally organized in a hierarchical manner.
• Consider how to devise plans for indexing so that query relevancy could be kept optimal in your NoSQL database.

5. Types of databases available and NoSQL Server Installation

• Select your NoSQL database based on your scaleability and consistency requirements.
• Sharding and replication configuration for distributed systems
• Initial configurations deployment, such as security configuration—an authentication configuration or encryption.

6. Development of a Data Migration Plan

• Export SQL data using ETL types of tools.
• Transform data so that it can be meaningful in NoSQL format; for example, rows are transformed into a JSON format to be read by MongoDB.
• Validate data integrity by running checks on a copy of the migrated data.

7. Testing the Migrated Process

• Simulation of the migration process with a test migration in a staging environment.
• Compare SQL and NoSQL based on query performance as well as data accuracy.
• Compare the applications in terms of behavior and latency when under load.

8. Roll out incrementally.

• Data migration should be incrementally done, not the "big bang" migration.
• Application should be able to run in hybrid mode—that is, should support both SQL and NoSQL duration transition phases.
• Regularly monitor performance metrics for bottlenecks and resolve them.

9. Optimization of NoSQL Database after Migration

• Use caching where possible to aid read operations.
• The aggregation pipelines for complex queries.
• Monitor database usage patterns and performance constantly to optimize further.

10. Train Your Team

• Educate developers and DBAs on specifics about the NoSQL database selected.
• Provide additional resources in terms of query languages, for instance, MongoDB's MQL or Cassandra Query Language (CQL).
• Support cross-functional teams in standardizing new flows and best practices.

Key Concerns

Consistency

SQL is ACID-based, whereas most NoSQL follow the BASE set of rules. You need to think carefully about which consistency level you need before migration.

Costs and Resources

You may incur additional costs, such as hardware to implement horizontal scaling or train the people on your team on a NoSQL database.

Security

SQL and NoSQL are two types of different database-related technologies with respective safety considerations.

Conclusion:

Hence, using NoSQL from SQL is a strategic move that provides your application with a massive spike in flexibility, scalability, and performance. Therefore, with the right understanding of requirements, careful planning, and the steps mentioned above, you should easily achieve it.