NoSQL stands for ‘Not Only SQL.’ NoSQL is designed for larger data sets. These databases are flexible, as they can store and process unstructured and semi-structured data not easily handled by RDBMS, designed primarily for structured data.NoSQL helps in processing big data in real-time web applications. Large-scale organizations, such as Google, Amazon, Facebook, etc., use NoSQL databases to handle massive datasets.
If you are looking for a job related to NoSQL, you must prepare for the 2026 NoSQL Interview Questions. Though every interview is different and the job scope is also different, we can help you out with the top NoSQL Interview Questions and Answers, which will help you take the leap and get your success in your interview.
Below is the top list of 2026 NoSQL Interview Questions and Answers. These questions are divided into two parts as follows:
Part 1 – NoSQL Interview Questions (Basic)
This first part covers basic NoSQL interview questions and answers.
Q1. What do you mean by NoSQL?
Answer:
NoSQL stands for “Not Only SQL.” Developers created NoSQL databases to overcome some restrictions and challenges of traditional databases. NoSQL databases manage large distributed datasets effectively. They also offer high flexibility, allowing developers to store and process unstructured and semi-structured data that relational database management systems (RDBMSs) do not handle easily. These databases are used for processing big data in real-time web applications.
Q2. What are the features of NoSQL?
Answer:
Below are some of the features of NoSQL:
- Flexibility: NoSQL offers flexibility to store structured, semi-structured, or unstructured data, unlike a relational database which allows only structured data.
- Dynamic Schemas: NoSQL does not require schema definition, which solves the problem of modifying the schema for a table that already contains large datasets and needs new columns added.
- Sharding: Sharding means partitioning data into smaller databases to have faster access to data. This feature is present in NoSQL databases, allowing us to quickly fetch data from a server.
- Generic: The user can customize the NoSQL database as per the need.
- Scaling: NoSQL databases scale horizontally; thus, they are cheaper to manage.
Q3. What is the CAP theorem? How is it applicable to NoSQL systems?
Answer:
This is the frequently asked NoSQL Interview Questions in an interview. CAP theorem states three basic requirements for an application with distributed architecture.
- Consistency: Data in the database must be consistent before and after the execution of any operation. For example, every user should see the same data after an update operation.
- Availability: The system should always be up and running; there should be no downtime.
- Partition Tolerance: The system should work despite unreliable server communication. Theoretically, it is not possible to achieve all three requirements. CAP theorem allows distributed systems to follow any two of these requirements. Since partition tolerance is mandatory for distributed databases. Thus, we are left only with CP (Consistency, Partition tolerance) and AP (availability, Partition tolerance).
Some examples of AP systems are Dynamo, Cassandra, Simple DB, and CouchDB.
Some examples of CP systems are Big Table, Hyper Table, Mongo DB, and HBase.
Q4. Explain the difference between RDBMS vs NoSQL.
Answer:
RDBMS over NoSQL:
- Relational databases are better suited for structured and organized, relational data.
- Organize data through normalization.
- Use Structured query language(SQL), which is easy to learn.
- Maintains Data Integrity
- The data and its relationships are stored in separate tables.
- ACID compliance ensures that all transactions are committed or none are committed.
- Scale-up/ Vertical Scaling.
NoSQL over RDBMS:
- Better for Unstructured and Unpredictable Data.
- Handles Big Data
- No predefined schema.
- Cheaper to manage.
- Scale-out/Horizontal Scaling.
- BASE Transaction.
- High performance, availability, and scalability.
Part 2 – NoSQL Interview Questions (Advanced)
Let us now have a look at the advanced NoSQL Interview Questions.
Q5. What are the major challenges with traditional RDBMS?
Answer:
Following are some of the significant challenges with RDBMS systems:
- Not optimized to scale out: RDBMS systems do not optimize horizontal scaling well.
- Not able to handle unstructured data: RDBMS systems are not able to handle schema-less data (semi-structured or unstructured)
- Costly: There is a high licensing cost for data analysis with RDBMS systems.
- Not able to handle the high velocity of data ingestion: RDBMS systems steadily retain data.
The NoSQL systems evolved to overcome all the above challenges.
Q6. What are the different types of NoSQL databases?
Answer:
There are five different categories of NoSQL databases:
- Document Database: The document databases contain many other key-value pairs. A collection contains sets of documents, and each document can contain diverse or heterogeneous fields. A document database also supports nested documents. A user can add custom fields (Dynamic data) to the document.
- Consider the below example:
Collection
{
personName:”Mr. X”
person phone:”1234”
personAddress:”Andheri”
}Some NoSQL Document databases are Mongo DB, Couch DB, etc.
- Column Database: In a columnar database, data is stored in cells grouped by columns instead of rows. This gives us fast retrieval and access to data. Column families can contain unlimited columns, which can be dynamically created at runtime.
Some of the NoSQL Column databases are Google’s Big Table, Apache Cassandra, and HBase. - Key-Value stores: It stores the Hashtable of key-value pairs. Hashtable contains a bucket, which is a logical group of keys. In a Hashtable, a key can be auto-generated or synthetic, while the value can be a String, JSON, or BLOB. Consider the below example where a key is a city and value is an address in that city.
| Key | Value |
| “Mumbai” | {“Andheri, Mumbai, Maharashtra, India”} |
| “Bhopal” | {“New Market, Bhopal, Madhya Pradesh, India”} |
| “Pune” | {“Hadapsar, Pune, Maharashtra, India”} |
Some of the NoSQL Key-Value stores are:
Redis, CouchBase Server, Amazon’s Dynamo.
- Cache systems: These are similar to key-value pairs. They store frequently accessed data in memory for fast retrieval and access.
Example: Redis and Memcache - Graph database: Graph-based databases are designed to store information about a data network and are best suited for handling connected data. These databases represent data with edges and nodes. The edges connect nodes having some relationships, and the nodes store data.
One of the NoSQL Graph databases is Neo4J.
Q7. How does NoSQL relate to big data?
Answer:
NoSQL databases are designed to handle large volumes of data, often called Big Data. They do not require a fixed schema, allowing organizations to store and manage diverse and rapidly changing data. This flexibility makes NoSQL databases well-suited for modern business environments that generate large amounts of structured, semi-structured, and unstructured data.
Q8. Can you explain the transaction support by using a BASE in NoSQL?
Answer:
The CAP theorem states that distributed systems cannot provide consistency, availability, and partition tolerance at the same time. The BASE system sacrifices strict consistency to maintain availability and partition tolerance. It continues to operate during network partitions and always allows clients to read and write data.
The BASE stands for:
Basically
Available
Soft state
Eventual consistency
Q9. What is Partition Tolerance?
Answer:
A distributed database is a type of database where data is stored on multiple computers or servers located in different places, but it works as one system. The data is shared across different locations, allowing the database to handle more users and larger amounts of information efficiently. It also improves reliability because if one server fails, other servers can continue providing access to the data. Users can retrieve and manage information without needing to know where the data is physically stored.
Example:
If one data center loses network connectivity, the system still operates.
Q10. Why are joins avoided in NoSQL databases?
Answer:
Joins are generally avoided in NoSQL databases because they can be slow and resource-intensive in distributed environments where data is stored across multiple servers. Performing joins requires retrieving and combining data from different locations, which increases latency. To improve performance, NoSQL databases use embedded documents and denormalized data structures, storing related data together so it can be accessed quickly without complex join operations.
Instead, NoSQL databases use:
- Embedded documents
- Denormalized data structures
Q11. What is a distributed database?
Answer:
A distributed database is a type of database where data is stored on multiple computers or servers located in different places, but it works as one system. The data is shared across different locations, allowing the database to handle more users and larger amounts of information efficiently. It also improves reliability because if one server fails, other servers can continue providing access to the data. Users can retrieve and manage information without needing to know where the data is physically stored.
Example: A Cassandra cluster stores data across multiple regions while functioning as one database.
Q12. What is data partitioning?
Answer:
Data partitioning is a technique used to split a large database into smaller sections called partitions. These partitions are stored across different servers or storage systems. Each partition holds a portion of the data, making it easier to manage and access. By spreading data across multiple locations, the database can process queries more quickly, improve performance, and handle larger amounts of data without overloading a single server.
This approach improves scalability, enhances query performance, optimizes resource utilization, and helps systems efficiently handle growing volumes of data.
Q13. What is denormalization in NoSQL?
Answer:
Denormalization in NoSQL means keeping related data together in one document instead of storing it in separate collections or tables. This approach reduces the need to fetch data from multiple places, making queries faster and improving read performance. It is especially useful in applications that handle large amounts of data.
Example: Customer information may be stored directly within an order document rather than referencing a separate customer record.
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