Navigating the NoSQL Landscape: MongoDB vs. Cassandra for nested or complex JSON data handling

Senthil Nayagan
Senthil Nayagan           

The choice between MongoDB and Cassandra becomes crucial when dealing with nested or complex JSON objects. MongoDB and Cassandra offer different approaches due to their underlying data models and architectures.
Navigating the NoSQL Landscape: MongoDB vs. Cassandra for nested or complex JSON data handling

Image Credits: Image generated by DALL-E.
A tree with multiple nests and a lively community of monkeys, visually represents the complex hierarchy of a nested JSON structure.


In the world of NoSQL databases, MongoDB and Cassandra stand out for their robustness, scalability, and flexibility, making them prime choices for managing the data demands of modern applications. When it comes to managing complex, nested JSON data structures, the choice between MongoDB and Cassandra becomes crucial, as each offers unique capabilities and advantages tailored to specific requirements.

This post aims to provide a comprehensive overview of how MongoDB and Cassandra handle nested or complex JSON data, comparing their data modeling capabilities, query flexibility, performance implications, and scalability to help us decide which NoSQL database best suits our specific use case. Understanding the strengths and limitations of each database in handling complex JSON data is key to making an informed decision.


Handling nested JSON objects in Cassandra presents challenges due to the lack of support for JOIN operations, requiring a different approach for efficient data read/write operations. The recommended strategy involves flattening the nested data structure, utilizing Cassandra’s collection types (lists, sets, and maps) or User-Defined Types (UDTs) to model the data. This approach allows us to store complex, nested information in a single table, reducing the need for joins.

Now that we’ve captured the essence with our TL;DR, let’s dive deeper into the nuances and explore the subject in greater detail.

A brief introduction to NoSQL database

The term “NoSQL” originally meant “non-SQL” but has evolved to mean “not only SQL,” reflecting the diverse approaches modern NoSQL databases use for data management, retrieval, and storing. A database is typically classified as a NoSQL database based on several key characteristics that differentiate it from traditional relational database systems (shortly referred to as RDBMS). These characteristics cater to the needs of modern applications requiring scalability, flexibility, and the ability to handle large volumes of structured, semi-structured, and unstructured data.

Here are the core qualities that qualify a database as a NoSQL database:

  1. Non-relational or less structured data model: NoSQL databases are designed to store and manage data in formats other than the traditional table-based structure (with rows and columns) used in relational databases. They can handle structured, semi-structured, and unstructured data, making them versatile for various data types, including JSON documents, key-value pairs, wide-column stores1, and graphs.

  2. Scalability: NoSQL databases are built to provide horizontal scalability, meaning they can manage increased loads by adding more servers to the database infrastructure. This contrasts with the vertical scaling approach (upgrading a single server’s capacity) typical of relational databases, allowing NoSQL systems to handle vast amounts of data and high throughput.

  3. Flexible Schema: Unlike relational databases that require a predefined schema (structure of the data) that can be costly to modify, NoSQL databases allow a schema-less or dynamic schema approach. This flexibility enables applications to store and combine data of different structures without needing to define and adhere to a rigid schema upfront.

  4. High performance for specific use cases: Many NoSQL databases are optimized for specific types of data and access patterns, providing high performance for tasks like full-text search, real-time analytics, and handling large volumes of transactions or data streams.

  5. Distributed nature: NoSQL databases often employ a distributed architecture, distributing data across multiple servers or nodes. This approach enhances the database’s availability, fault tolerance, and resilience, as there is no single point of failure.

  6. API access and query language: NoSQL databases typically provide API access for queries and data manipulation, rather than using a standard query language like SQL. Some NoSQL databases have their own query languages (Cassandra has CQL) that are specialized for their particular data model.

The aforementioned qualities make NoSQL databases a strong option in situations where traditional relational databases might not perform well.

Handling nested JSON objects

In the ever-evolving world of NoSQL databases, the choice between MongoDB and Cassandra becomes crucial when dealing with nested or complex JSON objects. MongoDB and Cassandra offer different approaches due to their underlying data models and architectures. The efficiency of read and write operations in these databases is significantly influenced by how they handle such data structures. Whether we’re building a real-time analytics engine, or any application requiring efficient storage and retrieval of complex JSON data, understanding the strengths and limitations of each database system will empower us to make an informed decision.

How does MongoDB handle nested JSON?

MongoDB, a document-oriented database, shines with its native support for JSON-like, schema-less data structures, allowing for the seamless storage of nested documents and arrays. This makes it an excellent choice for applications that require agile development and the ability to store complex hierarchical data within a single document. MongoDB’s dynamic schema2 offers the flexibility to evolve our data model over time without significant downtime or complex migrations.

  • Native support: MongoDB is designed as a document-oriented database, making it inherently well-suited for storing, reading, and writing nested JSON objects. It stores data in BSON (Binary JSON) format, which can naturally represent complex, hierarchical data structures with nested objects and arrays.
  • Querying: MongoDB provides powerful querying capabilities for nested objects and arrays, including the ability to project specific elements of an array, match against elements of nested documents, and perform aggregations. This makes it easy to work with complex data structures without requiring significant data transformation.
  • Indexing: MongoDB supports indexing on nested fields, which can significantly improve the performance of read operations involving complex JSON data.

How does Cassandra handle nested JSON?

On the other hand, Cassandra, a wide-column store, excels at handling vast amounts of data across many commodity servers, providing high availability without compromising performance. While not inherently document-oriented like MongoDB, Cassandra can be designed to store and query nested JSON data through its map and set data types, and with its JSON support, it allows for semi-structured data to be efficiently managed. However, this comes with the need for a more deliberate data modeling approach to ensure performance and scalability.

  • Data modeling: Cassandra is a wide-column store, which does not natively store data in a nested JSON format. Instead, complex data structures must be mapped to Cassandra’s table structure, often involving denormalization or the use of user-defined types (UDTs) for nested objects. This requires careful data modeling to ensure efficiency.
  • Read and write operations: While Cassandra can efficiently handle high write and read throughput, the efficiency of operations involving nested JSON objects depends heavily on the data model. Well-designed tables can support efficient reads by minimizing the need for joins (which Cassandra does not support) and leveraging partition keys and clustering columns for fast data retrieval.
  • No joins support: Cassandra does not offer built-in support for joins, subqueries, or many other relational database features. This design choice is intentional, to ensure high performance and scalability by avoiding operations that could lead to distributed joins across nodes, which are expensive and complex in distributed systems. Having said that, we often design our schema based on query patterns, meaning we store data in such a way that it can be retrieved in a single query without needing joins. This might involve duplicating data across different tables (denormalization) to ensure that each query can be served efficiently by a single table.
  • Support for JSON: Cassandra does offer JSON support for inserting and retrieving data, allowing developers to insert data in JSON format and retrieve data as JSON. However, this does not change the underlying storage model but can simplify working with JSON data.


Feature MongoDB Cassandra
Native support for JSON (Including nested, complex JSON objects) MongoDB is designed as a document-oriented database, making it inherently well-suited for storing, reading, and writing nested JSON objects, including nested complex JSON objects. Cassandra is a wide-column store, which doesn’t natively support nested JSON structures in the same way document-oriented databases like MongoDB do.

Instead, complex data structures must be mapped to Cassandra’s table structure, often involving denormalization, or we would typically need to flatten the data through the use of user-defined types (UDTs) or Cassandra’s data types like map, list, and set to model data with some level of nesting, but these are not equivalent to fully nested JSON objects. This requires careful data modeling to ensure efficiency. The flattening method simplifies queries but can lead to data redundancy and larger storage requirements.
Normalization MongoDB does not require us to normalize data into separate tables. Instead, we can store nested data directly within a single document in a collection, which can simplify data retrieval and reduce the need for join operations. Cassandra’s architecture and data modeling practices are fundamentally designed around denormalization, not normalization.

Lack of Joins: Cassandra does not support joins. Attempting to normalize data in Cassandra would lead to inefficient querying patterns, requiring application-level joins that are costly in terms of performance and complexity.
Querying MongoDB provides a rich set of query operators to navigate and query nested objects and arrays. We can query based on nested field values, array elements, and even use projection operators to return specific parts of a document. Cassandra does not offer native support for querying inside nested JSON structures stored as text. For structured querying of nested data, we would typically use User-Defined Types (UDTs) to model part of the nested structure, or store serialized JSON in a text column and handle the deserialization in our application.
Indexing MongoDB allows us to create indexes on fields that are deeply nested within a JSON document, improving the performance of queries that access these fields.

Multikey indexes for arrays: When a field contains an array, MongoDB automatically uses a multikey index to index each element of the array. This is particularly useful for nested arrays within JSON documents, enabling efficient queries on array elements.

Compound indexes: MongoDB supports compound indexes that can include nested fields. This feature is useful for optimizing queries that filter or sort on multiple fields, including those within nested structures.

Partial indexes: Partial indexes allow indexing only a subset of a collection based on a specified filter expression. This can include conditions on nested fields, making the indexes more efficient by only including relevant documents.
Cassandra’s support for indexing nested JSON fields is more limited. While Cassandra can store JSON data, it does so by mapping JSON fields to columns. Indexing is performed on these columns, not directly within nested JSON structures. Cassandra supports User-Defined Types (UDTs), which can be used to model nested data structures to some extent. We can create indexes on UDT fields, but this requires modeling our data in a way that aligns with Cassandra’s columnar structure, rather than working directly with arbitrary nested JSON.
Joins Since MongoDB allows nested JSON documents to be stored and queried within single documents, it reduces the need for joins, but MongoDB also provides mechanisms to perform operations similar to SQL joins through its aggregation framework. MongoDB supports a form of joining documents from different collections primarily through the $lookup operator in its aggregation framework. Cassandra does not support joins in the way relational databases do. This limitation is by design, stemming from Cassandra’s focus on scalability, distributed architecture, and performance. In distributed databases like Cassandra, the overhead and complexity of performing joins across potentially large datasets spread over many nodes can significantly impact performance and scalability.

Which one to choose?

  • For applications primarily dealing with nested JSON data: MongoDB is generally better suited and often recommended for applications that frequently store, read, and manipulate nested JSON objects. Its document model, combined with powerful querying, indexing, and aggregation capabilities, makes it highly efficient for working with complex data structures.
  • Considerations beyond nested JSON: While MongoDB is more naturally aligned with nested JSON data, the choice between MongoDB and Cassandra should also consider other factors such as scalability, data distribution, and consistency requirements. Cassandra might be preferred in scenarios requiring linear scalability across multiple data centers or when write throughput and availability are prioritized.

Recommended approach for nested JSON data in Cassandra

Cassandra, being a column-family database, does not directly support nested structures in the same way as MongoDB does. However, there are different approaches that can be used to model nested data.

Denormalization over normalization

In Cassandra, denormalization is preferred over normalization because normalization is generally not a viable option due to the following reasons:

  • No Joins: Cassandra does not support joins. Normalizing data into multiple tables would necessitate joins to reassemble the data for queries, which Cassandra cannot do. Normalizing data into multiple tables would necessitate joins to reassemble the data for queries, which Cassandra cannot do.
  • Data Modeling: Cassandra’s data modeling is based on query patterns rather than data relationships. The recommended approach is to model our tables based on the queries we intend to run. This often means creating multiple, purpose-built tables that might duplicate data3.
  • Performance: Cassandra is optimized for high write and read throughput across distributed systems. Normalization would require multiple read operations from different tables to reconstruct a single object, which would be slower and more complex to handle at scale.
  • Partitioning and clustering: Cassandra’s architecture relies heavily on partitioning data across nodes. Normalized data would be more difficult to partition effectively and could lead to “hotspots,” where one partition has a significantly higher load than others.

To handle the nested JSON data in Cassandra, let’s explore all the three recommended options we have:

  • Using User-Defined Types (UDTs)
  • Leveraging Cassandra’s collection types
  • Creating purpose-built tables with possible data duplication

The below nested array JSON will serve as a basis for these explanations:

  "account_id": "123456789",
  "account_holder_name": "John Doe",
  "balance": 9500.00,
  "account_type": "Checking",
  "transactions": [
      "transaction_id": "T1001",
      "transaction_type": "debit",
      "amount": 500.00,
      "date": "2024-02-25",
      "description": "Grocery Shopping at SuperMart",
      "transaction_id": "T1002",
      "transaction_type": "credit",
      "amount": 1500.00,
      "date": "2024-02-20",
      "description": "Monthly Salary",
      "transaction_id": "T1003",
      "transaction_type": "debit",
      "amount": 200.00,
      "date": "2024-02-18",
      "description": "Electricity Bill Payment",
      "transaction_id": "T1004",
      "transaction_type": "debit",
      "amount": 300.00,
      "date": "2024-02-15",
      "description": "Online Subscription Service",
      "transaction_id": "T1005",
      "transaction_type": "credit",
      "amount": 2000.00,
      "date": "2024-02-10",
      "description": "Freelance Payment Received",

Using User-Defined Types (UDTs)

Cassandra’s UDTs allow us to define a custom data type that represents the structure of nested JSON data. This is useful for encapsulating the attributes of a complex entity.

First, define a UDT using the type keyword for the transaction structure:

CREATE TYPE IF NOT EXISTS transaction_udt (
    transaction_id text,
    transaction_type text,
    amount decimal,
    date text,
    description text

Next, use this UDT in a table definition:

    account_id text PRIMARY KEY,
    account_holder_name text,
    balance decimal,
    account_type text,
    transactions list<frozen<transaction_udt>>

To insert the sample data:

INSERT INTO bank_accounts (account_id, account_holder_name, balance, account_type, transactions)
VALUES ('123456789', 'John Doe', 9500.00, 'Checking', [
    {transaction_id: 'T1001', transaction_type: 'debit', amount: 500.00, date: '2024-02-25', description: 'Grocery Shopping at SuperMart'},

This approach maintains the nested structure within a single table, allowing for efficient queries related to the bank account and their transactions. UDTs can be used in combination with collections for more complex structures. In our case, we used the list collection type to define a list of addresses, where address is an UDT.

Using Cassandra’s collection types

We can also model nested structures using Cassandra’s built-in collection types, such as lists, sets, and maps. To model the same use case without using UDTs but instead utilizing Cassandra’s collection types directly in the table, we can adjust the approach to store transactions as a collection.

However, since Cassandra collections (lists, sets, maps) do not support nesting complex structures like transactions directly, we have to simplify the data model. A common approach is to use a map for each transaction attribute, with the transaction ID as the key for each map. This approach does not directly nest the transaction details as a single entity but keeps them related by their transaction ID keys.

We’ll create a table that uses maps to store transaction attributes. Each transaction attribute (type, amount, date, description) will be stored in its own map, keyed by the transaction ID:

CREATE TABLE IF NOT EXISTS bank_accounts_collections (
    account_id text PRIMARY KEY,
    account_holder_name text,
    balance decimal,
    account_type text,
    transaction_ids set<text>,
    transaction_types map<text, text>,
    transaction_amounts map<text, decimal>,
    transaction_dates map<text, text>,
    transaction_descriptions map<text, text>

In this model:

  • transaction_ids is a set of all transaction IDs associated with the account. This helps to maintain a list of all transactions without duplicating detailed information.
  • transaction_types, transaction_amounts, transaction_dates, and transaction_descriptions are maps where the key is the transaction ID, and the value is the respective attribute of the transaction.

To insert data into this table, we would first generate unique transaction IDs and then use them to insert the transaction details into the respective maps. Here’s how we might insert an example account with transactions:

INSERT INTO bank_accounts_collections (account_id, account_holder_name, balance, account_type, transaction_ids, transaction_types, transaction_amounts, transaction_dates, transaction_descriptions) VALUES (
    'John Doe',
    {'T1001', 'T1002', 'T1003'},
    {'T1001': 'debit', 'T1002': 'credit', 'T1003': 'debit'},
    {'T1001': 500.00, 'T1002': 1500.00, 'T1003': 200.00},
    {'T1001': '2024-02-25', 'T1002': '2024-02-20', 'T1003': '2024-02-18'},
    {'T1001': 'Grocery Shopping at SuperMart', 'T1002': 'Monthly Salary', 'T1003': 'Electricity Bill Payment'}


  • Query Limitations: This design requires more effort to query specific transaction details as we need to fetch the entire map and then filter for the keys (transaction IDs) we’re interested in. It’s less convenient than having a UDT where transaction details are encapsulated as a single entity.
  • Update Complexity: Updating or deleting individual transactions can become more complex, as we must ensure consistency across all maps for the transaction IDs.
  • Performance: While this approach avoids using UDTs, it may not necessarily offer better performance. The use of multiple maps and a set increases the complexity of updates and queries.

Creating purpose-built tables with data duplication

For certain query patterns, we might create separate tables that duplicate some of the data to optimize read performance. Here’s how we could model the users and their addresses in separate tables:

Purpose-built table for transactions by type

This table is optimized for querying transactions based on their type (credit or debit):

CREATE TABLE IF NOT EXISTS transactions_by_type (
    account_id text,
    transaction_type text,
    transaction_id text,
    amount decimal,
    date text,
    description text,
    PRIMARY KEY ((account_id, transaction_type), date, transaction_id)
) WITH CLUSTERING ORDER BY (date DESC, transaction_id ASC);

Primary Key: The combination of account_id and transaction_type as the partition key allows for efficient querying of transactions by account and type. The transaction_id is used as a clustering column to ensure uniqueness and allow ordering within the partition.

Purpose-built table for transactions by date

This table facilitates querying transactions for an account by date:

CREATE TABLE IF NOT EXISTS transactions_by_date (
    account_id text,
    date text,
    transaction_id text,
    transaction_type text,
    amount decimal,
    description text,
    PRIMARY KEY ((account_id), date, transaction_id)
) WITH CLUSTERING ORDER BY (date DESC, transaction_id ASC);

Primary Key: Here, account_id is the partition key, and date is a clustering column, which allows for transactions to be ordered chronologically within each account. The transaction_id ensures uniqueness of transactions within each day.

Data duplication strategy

When using purpose-built tables, we’ll need to insert the same transaction data into multiple tables, each optimized for a specific query pattern. Here’s an example of how to insert data into both the transactions_by_type and transactions_by_date tables:

    INSERT INTO transactions_by_type (account_id, transaction_type, transaction_id, amount, date, description)
    VALUES ('123456789', 'debit', 'T1001', 500.00, '2024-02-25', 'Grocery Shopping at SuperMart');
    INSERT INTO transactions_by_date (account_id, date, transaction_id, transaction_type, amount, description)
    VALUES ('123456789', '2024-02-25', 'T1001', 'debit', 500.00, 'Grocery Shopping at SuperMart');
    // Repeat for other transactions...

This approach ensures that data is consistently replicated across tables designed for specific access patterns, facilitating fast and efficient queries. However, it also means we need to manage data consistency across multiple tables, especially in write-heavy applications. When updating or deleting transactions, ensure these operations are applied across all relevant tables to maintain data integrity.

Conclusion of the three Cassandra approaches to managing nested JSON: Each of these options has its own trade-offs in terms of complexity, flexibility, and performance. The choice depends on our specific application’s query patterns and performance requirements.

  • UDTs (Recommended): Using UDTs keeps the structure close to the original JSON and is more straightforward for simple nested objects.
  • Collection types offer flexibility for simpler nested data. However, collections are limited in size (2 billion elements) and are not ideal for very large datasets.
  • Purpose-built tables can provide the highest query performance at the cost of data duplication and increased complexity in data management.


To put it simply, MongoDB offers a flexible way to query data, including operations similar to joins, which is great for complex data retrieval tasks. It allows for a natural handling of nested JSON data, making it a good choice if we’re looking for ease of use and flexible data structures. On the other hand, Cassandra shines in scalability and performance, designed to handle massive data without using joins. It relies on denormalization and careful planning of data models instead of normalization, making it less straightforward for managing nested JSON data.

Choosing Cassandra means we’ll likely use User-Defined Types (UDTs) to deal with nested data. While UDTs help structure relational data in Cassandra, they’re not as flexible or easy to use as MongoDB’s approach to JSON documents.

If our main priority is straightforward management of nested JSON data and a flexible schema, MongoDB is likely our best bet. However, for applications that demand high scalability and distributed data management, and where we’re okay with a more structured method of dealing with nested data, Cassandra, with its UDTs, could be a better fit. This is particularly true for applications that are write-heavy or need specific adjustments for consistency and availability.

Frequently asked questions (FAQs)

Should normalization be performed on NoSQL databases?

In NoSQL databases, the need for normalization is not as common as it is in traditional relational databases. The primary reason for this is that NoSQL databases have unique design objectives, which often prioritize scalability, performance, and flexibility over strict data integrity and relationships.

While traditional normalization principles from relational databases don’t directly apply to NoSQL databases, considerations about data structure, duplication, and consistency are still relevant. The key is to balance the benefits of denormalization (like improved performance and simpler queries) with the potential drawbacks, such as increased storage requirements and complexity in maintaining consistency.

Is a wide column store the same as a columnar store?

No, wide-column stores and columnar stores are not the same, despite their similar names. They are designed for different use cases and have distinct architectures. Here’s a brief overview of each:

Wide Column Stores

  • Definition: Wide-column stores are a type of NoSQL database that organizes data into tables, rows, and dynamic columns. Each row is uniquely identifiable by a row key, and each row can have any number of columns. This model allows for the efficient storage and retrieval of data on a massive scale.
  • Use Cases: They are optimized for queries over large datasets and are ideal for storing data that doesn’t fit well into relational models, such as big data applications, real-time analytics, and scalable web applications.
  • Examples: Cassandra and HBase are well-known wide column stores.

Columnar Stores

  • Definition: Columnar stores are databases that store data tables by column rather than by row. This storage orientation is particularly advantageous for analytics and warehousing queries, which often only need a subset of the data within a table, making read operations more efficient.
  • Use Cases: They are primarily used for data warehousing and analytics, where operations often involve aggregating or scanning large numbers of records to compute summaries, as columnar storage allows for faster data retrieval and aggregation.
  • Examples: Amazon Redshift, Google BigQuery, and Apache Parquet are examples of columnar storage systems.

Key Differences

  • Storage Orientation: Wide column stores organize data by rows, with a dynamic schema for columns within each row. Columnar stores organize data by columns, optimizing for operations that read many rows but only a subset of columns.
  • Optimization: Wide column stores are optimized for high scalability, large-scale data storage, and complex lookup queries. Columnar stores are optimized for fast data retrieval, analytics, and data warehousing.
  • Use Case: Wide column stores are more versatile for general-purpose applications, including real-time analytics and Internet of Things (IoT) data storage. Columnar stores are specifically designed for analytical processing and business intelligence operations.

Understanding these differences is crucial when designing a data storage solution, as the choice between wide column stores and columnar stores should be based on the specific requirements of the application, such as the nature of the data, the types of queries performed, and the scalability needs.

Is MongoDB similar to Cassandra in being a wide-column store?

No, MongoDB is not a wide column store like Cassandra. MongoDB is categorized as a document-oriented database. Here are the main differences between MongoDB and wide column stores like Cassandra:

MongoDB (Document-Oriented Database)

  • Data Model: MongoDB stores data in BSON (Binary JSON) documents, which are JSON-like objects with additional support for binary data. This model allows for nested structures like arrays and embedded documents, facilitating a more natural and intuitive mapping to objects in application code.
  • Schema Flexibility: MongoDB offers a dynamic schema, enabling the fields in a document to vary from document to document. This flexibility makes it easier to evolve the data model over time without downtime.
  • Use Cases: It is well-suited for applications requiring complex queries, rapid development, and the ability to store hierarchical data structures directly, such as content management systems, e-commerce applications, and real-time analytics.

Cassandra (Wide Column Store)

  • Data Model: Cassandra organizes data into tables where rows are identified by a primary key. Each row can have any number of columns, which can vary from one row to another, and the schema is more structured compared to MongoDB. Columns are grouped into column families, and data is stored on disk in a way that is optimized for reading and writing wide rows efficiently.
  • Schema Flexibility: While Cassandra offers some flexibility with dynamic columns, its data model is generally more rigid than MongoDB’s, requiring a defined schema that specifies the column families.
  • Use Cases: Cassandra excels in scenarios requiring high availability, scalability across multiple data centers, and the ability to handle large volumes of writes and reads. It is ideal for applications such as messaging systems, IoT data storage, and any use case that requires linear scalability and fault tolerance.

In summary, MongoDB and Cassandra serve different purposes and excel in different scenarios due to their distinct data models and architectures. MongoDB is a document database that is best for use cases requiring complex data structures and schema flexibility, while Cassandra is a wide column store optimized for scalability and performance across large, distributed datasets.

What does it mean to have purpose-built tables with possible duplicate data in Cassandra?

When it comes to Cassandra, tables are frequently constructed with the queries that the application will execute in mind, as opposed to minimizing data redundancy. Each table is typically optimized to serve a specific type of query, with all the necessary data included within the same partition. With that said, Cassandra may involve creating multiple, purpose-built tables that duplicate data in order to optimize data retrieval operations. This is fundamentally different from the relational database practice of normalization, which seeks to minimize redundancy and maintain data integrity by splitting data into multiple related tables that are then recombined using joins.

  • Duplication for performance: To ensure that each query can be satisfied by reading from a single table, without the need for joins or multiple reads from different tables, data is duplicated across these purpose-built tables. This means that the same piece of data might exist in several tables, each structured to answer a different query.
  • Write amplification: This approach leads to write amplification, where a single logical update may result in updates across multiple tables. While this increases write complexity, it benefits read performance because each query can be satisfied by a direct and efficient table scan.
  • Denormalization by design: Unlike normalization, which aims to reduce data redundancy and maintain data integrity, the duplication in Cassandra is intentional and does not aim to conform to the RDBMS normalization forms. Denormalization in Cassandra is a design choice to leverage the database’s distributed nature and ensure linear scalability.

  1. Wide-column store databases: Wide-column store databases are a type of NoSQL database that organizes data into tables, rows, and “dynamic” columns. Dynamic columns enable each row can have a different set of columns. Unlike traditional relational databases that structure data into fixed columns and rows, wide-column stores allow each row to have a unique or different set of columns. Use Cases: Ideal for handling large datasets with variable schema across many rows, such as time-series data, or for applications requiring high performance and scalability, like web analytics and real-time sensor data analysis. 

  2. Dynamic schema: The dynamic schema (or dynamic column) feature of MongoDB refers to its ability to allow the structure of documents (akin to rows in relational databases) to vary from one to another within the same collection. This means that documents in a single collection do not need to have the same set of fields or structure, allowing for the storage of data in a more flexible way compared to traditional relational databases that require the schema of a table to be defined and fixed before data can be inserted. This flexibility enables developers to evolve their data model without having to perform schema migrations or alter table structures as their applications develop and change. It’s particularly useful for dealing with heterogeneous data, accommodating changes in data models, and rapidly developing applications where the data structure can evolve over time. 

  3. Purpose-built tables with duplicate data for queries: When we say Cassandra may involve creating multiple, purpose-built tables that duplicate data, we’re describing a deliberate design strategy aimed at maximizing the efficiency of data retrieval operations. This is fundamentally different from the relational database practice of normalization, which seeks to minimize redundancy and maintain data integrity by splitting data into multiple related tables that are then recombined using joins. 


comments powered by Disqus