NoSQL Playbook
Everything about NoSQL from Playbook , cheatsheets, interview questions to roadmap!
Learning SQL is no longer just enough. Infact, with the advancements in technology, we have different types of databases as well. One of the most important ones is NoSQL. It is very widely used. But how is it different from relational database, what are the important concepts to learn , cheatsheet to help you learn this faster along with a roadmap! Sharing all of this below
Difference between RDBMS and NoSQL :
NoSQL stands for "Not Only SQL", and it refers to a category of databases designed to handle large volumes of unstructured, semi-structured, or rapidly changing data. Unlike traditional Relational Database Management Systems (RDBMS), NoSQL databases are schema-less, offering flexibility, scalability, and high performance for certain types of applications.
When is NoSQL Used?
NoSQL is commonly used when:
Data is unstructured or semi-structured (e.g., JSON, XML).
Applications require horizontal scalability (e.g., cloud-native apps).
Data volume and velocity are too high for RDBMS (e.g., real-time analytics).
Schema changes need to happen frequently or dynamically.
There's a need for high availability and fault tolerance.
EXAMPLE OF NoSQL Databases :
MongoDB
Apache Cassandra
Redis
Apache HBase
Neo4j
Difference between RDMBS and NoSQL :
SCHEMA STRUCTURE :
SQL : Relational databases require a predefined schema, enforcing a structured format for data storage. This ensures data consistency and integrity but can limit flexibility.
NoSQL : NoSQ databases embrace a schema-less or flexible schema, allowing for the storage of unstructured or semi-structured data. This adaptability is beneficial for rapidly evolving data needs.
TRANSACTION PROPERTIES :
SQL : Adherence to ACID (Atomicity, Consistency, Isolation, Durability) properties is a sign of relational databases, ensuring reliable and consistent transactions.
NoSQL : Many NoSQL databases follow an eventual consistency model, prioritizing performance and availability over immediate consistency, suitable for applications where slight delays in data consistency are acceptable.
QUERY LANGUAGE :
SQL : Utilizes Structured Query Language (SQL), offering a standardized and powerful tool for complex data retrieval and manipulation.
NoSQL : Often employs non-standardized querying languages or simple API calls, providing specialized queries but lacking a universal language.
SCALING :
SQL : Typically relies on vertical scaling, expanding by enhancing the power of existing servers, which can become costly and limited in scalability.
NoSQL : Designed for horizontal scaling, allowing the addition of more servers to handle larger volumes of data, ideal for big data applications.
PERFORMANCE :
SQL : Offers reliable performance for structured data and complex queries but can face challenges with large-scale horizontal scaling.
NoSQL : Generally provides high performance for read-heavy applications and large-scale data, benefiting from its scalability and flexible schema.
DATA CONSISTENCY :
SQL : Ensures strict data consistency due to ACID compliance, making it reliable for scenarios where data accuracy is paramount.
NoSQL : Data consistency can vary, with some systems offering strong consistency while others prioritize performance and scalability.
When should you choose NoSQL over RDBMS ?
Data is Unstructured or Semi-Structured
You have JSON, XML, or varied data fields (e.g., user profiles with optional fields).
You want schema flexibility without migrations.
Scalability is a Priority
You expect horizontal scaling across many servers (cloud-native, global apps).
You’re handling big data or real-time data streams.
High Write/Read Throughput Needed
Applications need low latency and high availability (e.g., chat apps, gaming, IoT).
Rapid Development or Frequent Schema Changes
You’re iterating fast and don’t want rigid schema constraints.
You Use Data in a Key-Value, Document, Graph, or Columnar Model
E.g., shopping cart (key-value), product catalog (document), social network (graph), analytics (columnar).
REAL WORLD USE-CASE :
Product Example: Online Shopping Platforms like Amazon or Etsy
Challenges:
An e-commerce platform must maintain millions of mobile devices listings per its needs, where each product can have multiple:
Differing characteristics (Like size in case of garments or specifications of electronics)
Reviews, seller details, shipping details, and seller information
Frequently changing information (Like pricing and availability)
A single database may be read or written to in real time at large scales simultaneously.
In a relational database, there is always the need of join operations along with defining structured rigid requirements which is always expensive in terms of performance and maintainability.
HOW NoSQL Helps :
NoSQL Solution: MongoDB (Document-based NoSQL DB)
Flexible Schema: Products are kept as a JSON-like document. Products can have different fields (e.g., a book has ISBN; a phone has battery life).
Nested Data: Reviews, seller data, and variants can be nested into product documents—no joins required.
Horizontal Scalability: MongoDB shards data across servers, allowing scale-out architecture for millions of users.
Fast Reads/Writes: In order to retrieve a product with all its associated data, only one query is needed—faster page loads, better user experience.
Benefits :
Agility: Developers can add new product features (e.g., flash sales, badges) without schema rewrite.
Performance: Users experience quicker load times since there are fewer joins and document retrieval is optimized.
Scalability: Manages high shopping periods like Black Friday effortlessly by scaling horizontally.
Resilience: Data is replicated for high availability and disaster recovery.
NoSQL Cheatsheets Compilation :
NoSQL Top Interview Questions :
1. What is NoSQL, and how does it differ from traditional SQL databases? Explain the different types of NoSQL databases and provide examples for each.
2. What are the advantages and disadvantages of using NoSQL databases?
Advantages of NoSQL Databases :
Scalability: Designed for horizontal scaling in distributed environments
Flexibility: Schema-less data model allows rapid development and evolving data models
High Performance: Optimized for low-latency read/write loads
Big Data Friendly: Capable of storing numerous large amounts of unstructured or semi-structured data
Built for the Cloud: Natively designed to work with cloud infrastructure and microservices
Disadvantages of NoSQL Databases
Limited ACID Transactions: Some NoSQL systems prioritize eventual consistency over strong consistency, which can be a drawback.
Weaker Querying Capabilities: In particular, key-value or column stores may struggle with complex queries, making them less versatile.
Less Standardization: There's a wide variation in syntax, structure, and features across NoSQL databases, which can make the learning curve a bit steeper.
Data Duplication: The denormalization process can lead to redundant data, increasing storage requirements and the risk of inconsistencies.
Maturity and Tooling: Many NoSQL systems are relatively new and might not have the same level of robust tools or community support that SQL databases enjoy.
3. Describe the CAP theorem and its significance in NoSQL databases.
CAP Theorem states that in a distributed system, it's impossible to guarantee all three of the following simultaneously:
Consistency (every node has the same data at the same time)
Availability (every request gets a response)
Partition tolerance (system continues despite network failures)
NoSQL databases often choose Availability + Partition Tolerance over strict Consistency to remain scalable and fault-tolerant. This trade-off enables systems like Cassandra and DynamoDB to function efficiently in distributed, cloud-based environments.
How do you model data in a NoSQL database compared to a relational database?
In a NoSQL database, data modeling focuses on denormalization and embedding related data within a single document or entity. This approach optimizes performance by reducing the need for complex joins, unlike relational databases that rely on normalized schemas and relationships.
Explain the concept of eventual consistency in NoSQL databases.
Eventual consistency is a consistency model where updates to a database will propagate to all nodes over time, ensuring that all nodes will eventually hold the same data. This model prioritizes availability and partition tolerance, making it suitable for distributed systems.
What is sharding in NoSQL databases, and why is it important?
Sharding is the process of splitting large datasets across multiple servers (shards). Each shard holds a portion of the total data.
Why it matters:
Supports horizontal scaling
Improves read/write throughput
Reduces latency
For example, MongoDB automatically shards collections when data grows beyond certain thresholds.
How do you handle relationships between data in a NoSQL database?
Since NoSQL databases don't support joins natively, relationships are handled by:
Embedding: Putting related data within documents (for one-to-few relationships)
Referencing: Pointing to other documents through document IDs or keys (for one-to-many or many-to-many)
Example in MongoDB: Put comments inside a post document (embedding), or put them in a separate document with a post_id (referencing).
Explain the concept of indexing in NoSQL databases and its importance.
Indexing allows NoSQL databases to locate data quickly without scanning the entire data set.
Benefits :
Dramatically improves query performance
Essential for sorting, filtering, or searching on non-key fields
For example, in MongoDB, you can index on fields like username or email to speed up user lookups.
What are the differences between strong consistency and eventual consistency?
ROADMAP :
The best resource to follow for roadmap is this. I’ve used this resource to break it down further into a week by week basis for you to learn MongoDB in 30 days.
Week 1: NoSQL and MongoDB Introduction
Day 1-2: NoSQL Introduction
Learn about NoSQL and why it differs from SQL databases.
Read about the types of NoSQL databases: Key-Value, Document, Column-Family, and Graph databases.
Learn when to use NoSQL over SQL.
Day 3-4: MongoDB Introduction
Learn MongoDB's structure and features (e.g., JSON-like documents, schema-less).
Install MongoDB on your system and familiarize yourself with the MongoDB shell (mongosh).
Day 5-7: CRUD Operations
Practice CRUD operations in MongoDB using basic commands.
Insert documents (insertOne, insertMany).
Retrieve documents (find, findOne).
Update documents (updateOne, updateMany).
Delete documents (deleteOne, deleteMany).
Week 2: Intermediate Concepts
Day 8-9: Querying Data
Learn filtering data with query operators like $gt, $lt, $eq, $in, etc.
Practice sorting and limiting results.
Day 10-11: Indexing
Learn the use of indexes for performance tuning.
Create single-field and compound indexes.
Day 12: Aggregation Framework
Explore the aggregation pipeline for complex data transformations (e.g., $match, $group, $sort).
Day 13-14: Data Modeling
Learn how to design schemas for document-based databases.
Learn about embedding vs. referencing data.
Week 3: Advanced Topics
Day 15-16: Replication and Sharding
Learn about replication for high availability and sharding for scalability.
Day 17: Transactions
Learn how to use transactions in MongoDB for multi-document operations.
Day 18-19: Backup and Restore
Practice backing up and restoring databases using MongoDB tools.
Day 20: Security Best Practices
Learn MongoDB authentication, authorization, and encryption.
Week 4: Practical Implementation
Day 21-23: Project Implementation
Develop a simple application with MongoDB (e.g., blog website with comments and posts).
Set up the data model.
Develop CRUD operations.
Use aggregation for analytics.
Day 24-26: Integration with Programming Languages
Integrate MongoDB with a backend programming language (e.g., Python or Node.js).
Practice working with libraries like PyMongo or Mongoose.
Day 27: Performance Monitoring
Learn about methods of monitoring and optimizing MongoDB performance.
Day 28-30 : Revise and Study real world concepts