AWS DynamoDB vs. Apache Cassandra: Comparing Two NoSQL Databases
Big Data
5 MIN READ
September 30, 2023
Over the last few years, NoSQL databases have become very popular because of their ability to provide scalability and handle vast volumes of unstructured data. While speaking of NoSQL databases, AWS DynamoDB and Apache Cassandra are two of the leading choices in the given landscape.
This article will compare AWS DynamoDB and Apache Cassandra to understand their capabilities in detail. We know both databases are used to manage and store data, but certain qualities make them stand out. Understanding their differences will help you make informed choices about what is best for your specific needs.
What is AWS DynamoDB?
AWS DynamoDB is an Amazon Web Services (AWS) product and a fully managed NoSQL database service. It offers a highly scalable, flexible, and high-performance database solution for storing and retrieving (structured and semi-structured) data.
Key Features of AWS DynamoDB:
Fully managed database service.
Automatic scaling with seamless capacity management.
Supports JSON and document data models.
Rich query language with secondary indexes.
ACID-compliant for data integrity.
Integration with the AWS ecosystem and security features.
Global tables for multi-region deployments.
On-demand and provisioned capacity modes.
Encryption at rest and in transit.
What is Apache Cassandra?
Apache Cassandra is a well-known open-source distributed NoSQL database management system. In addition to its excellent scalability and fault tolerance, it is appropriate for managing huge amounts of data distributed across several nodes in a decentralised architecture. Apache Cassandra is known for being adaptable when handling different data types and having adjustable consistency levels for read and write operations.
Key Features of Apache Cassandra:
Decentralised, masterless architecture for horizontal scalability.
Tunable consistency levels for read and write operations.
Flexible data model with support for wide-column and JSON data.
CQL (Cassandra Query Language) for querying data.
Active open-source community and third-party integrations.
Built-in support for multi-data centre deployments.
High availability with no single point of failure.
Tunable compaction and storage options.
Support for custom data types and user-defined functions (UDFs).
AWS DynamoDB vs. Apache Cassandra – Comparison
Aspect
AWS DynamoDB
Apache Cassandra
Scalability
Fully managed, auto-scaling capabilities
Decentralised architecture for horizontal scaling
Consistency Models
Supports eventual and strong consistency
Offers tunable consistency levels
Data Model
Key-Value store with document support
Wide-column store with support for JSON
Query Language
Supports a rich query language with indexes
CQL (Cassandra Query Language) for querying
ACID Transactions
Supports ACID transactions
Limited support for transactions
Deployment
Hosted on AWS infrastructure
Self-hosted or managed with third-party services
Ease of Use
Simplified management, but with some complexity
Requires more manual configuration
Community Support
Strong community and AWS resources
Active open-source community
Cost
Pay-as-you-go pricing model
Self-managed may have lower operational costs
AWS DynamoDB vs. Apache Cassandra: Pros and Cons
AWS DynamoDB
Pros:
Fully Managed: DynamoDB is a fully managed database service, reducing operational overhead.
Auto-Scaling: It can automatically handle data volume and traffic changes, ensuring consistent performance.
Rich Query Support: DynamoDB supports a rich query language with indexing capabilities.
ACID Compliance: It ensures data consistency and reliability through ACID transactions.
Integration with AWS: Seamlessly integrates with other AWS services for a robust ecosystem.
Security Features: Offers encryption at rest and in transit, enhancing data security.
Cons:
Cost: This can be expensive, particularly for large-scale workloads.
Complexity: While managed, it can still require complex configuration for optimal performance.
Apache Cassandra:
Pros:
Scalability: Cassandra’s decentralised architecture allows for horizontal scaling across multiple nodes.
Tunable Consistency: Offers flexibility with tunable consistency levels for read and write operations.
Flexible Data Model: Supports wide-column data and JSON formats.
Active Community: Boasts an active open-source community with ongoing development.
Multi-Data Center Support: Built-in support for multi-data centre deployments.
No Single Point of Failure: High availability with no single point of failure.
Cons:
Complex Setup: Requires more manual configuration and management compared to DynamoDB.
Self-hosted Option: If self-hosted, it may have higher operational costs compared to a managed service.
Scalability in DynamoDB vs. Cassandra:
DynamoDB:
DynamoDB offers automatic scalability, making it easy to handle growing workloads.
It can automatically adjust its capacity based on demand, ensuring consistent performance.
Users can choose between on-demand and provisioned capacity modes to optimise cost and scale.
Apache Cassandra:
Cassandra provides scalability through a decentralised architecture.
It allows users to add nodes to the cluster to increase capacity and handle more data.
Scaling in Cassandra requires manual intervention but provides fine-grained control over cluster growth.
Query Performance in DynamoDB and Cassandra
Aspect
DynamoDB
Cassandra
Query Performance
Excellent for simple, single-key lookups
Strong for complex, multi-key queries
Query Language
Supports rich query language and indexing
Utilises CQL (Cassandra Query Language)
Secondary Indexes
Enables efficient querying on attributes
Requires careful schema design for indexing
Aggregation
Limited support for complex aggregations
Offers robust aggregation capabilities
Scaling
Auto-scales for consistent query speed
Scales horizontally for distributed queries
Conclusion
When comparing performance between DynamoDB and Apache Cassandra, it’s essential to consider your specific use case and requirements.
At Ksolves, with our expertise in Apache Cassandra development services, we understand the importance of choosing the right database solution. Our skilled Apache Cassandra experts can help you develop solutions that align with your business goals, whether you require real-time analytics, intricate data processing, or scalable storage.
Our expertise with Big Data technologies ensures you get the most out of your data-driven work. To explore more, get in touch with our team.
Anil Kushwaha, Technology Head at Ksolves, is an expert in Big Data and AI/ML. With over 11 years at Ksolves, he has been pivotal in driving innovative, high-volume data solutions with technologies like Nifi, Cassandra, Spark, Hadoop, etc. Passionate about advancing tech, he ensures smooth data warehousing for client success through tailored, cutting-edge strategies.
AUTHOR
Big Data
Anil Kushwaha, Technology Head at Ksolves, is an expert in Big Data and AI/ML. With over 11 years at Ksolves, he has been pivotal in driving innovative, high-volume data solutions with technologies like Nifi, Cassandra, Spark, Hadoop, etc. Passionate about advancing tech, he ensures smooth data warehousing for client success through tailored, cutting-edge strategies.
Share with