Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Apache Cassandra and AWS Redshift so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Apache Cassandra and AWS Redshift perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Apache Cassandra vs AWS Redshift Breakdown


 
Database Model

Distributed wide-column database

Data warehouse

Architecture

Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service

AWS Redshift utilizes a columnar storage format for fast querying and supports standard SQL. Redshift uses a distributed, shared-nothing architecture, where data is partitioned across multiple compute nodes. Each node is further divided into slices, with each slice processing a subset of data in parallel. Redshift can be deployed in a single-node or multi-node cluster, with the latter providing better performance for large datasets.

License

Apache 2.0

Closed source

Use Cases

High write throughput applications, time series data, messaging systems, recommendation engines, IoT

Business analytics, large-scale data processing, real-time dashboards, data integration, machine learning

Scalability

Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added

Supports scaling storage and compute independently, with support for adding or removing nodes as needed

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Apache Cassandra Overview

Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.


Apache Cassandra for Time Series Data

Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.

AWS Redshift for Time Series Data

AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.


Apache Cassandra Key Concepts

  • Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
  • Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
  • Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
  • Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.

AWS Redshift Key Concepts

  • Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
  • Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
  • Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
  • Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.


Apache Cassandra Architecture

Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.

AWS Redshift Architecture

Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.

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Apache Cassandra Features

Linear Scalability

Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.

High Availability

With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.

Tunable Consistency

Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.

AWS Redshift Features

Scalability

Redshift allows you to scale your cluster up or down by adding or removing compute nodes, enabling you to adjust your storage capacity and query performance based on your needs.

Performance

Redshift’s columnar storage format and MPP architecture enable it to deliver high-performance query execution for large-scale data warehousing workloads.

Security

Redshift provides a range of security features, including encryption at rest and in transit, network isolation using Amazon Virtual Private Cloud (VPC), and integration with AWS Identity and Access Management (IAM) for access control.


Apache Cassandra Use Cases

Messaging and Social Media Platforms

Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.

IoT and Distributed Systems

With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.

E-commerce

Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.

AWS Redshift Use Cases

Data Warehousing

Redshift is designed for large-scale data warehousing workloads, providing a scalable and high-performance solution for storing and analyzing structured data.

Business Intelligence and Reporting

Redshift integrates with various BI and reporting tools, enabling organizations to gain insights from their data and make data-driven decisions.

ETL and Data Integration

Redshift supports data loading and extraction, transformation, and loading (ETL) processes, allowing you to integrate data from various sources and prepare it for analysis.


Apache Cassandra Pricing Model

Apache Cassandra is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.

AWS Redshift Pricing Model

Amazon Redshift offers two pricing models: On-Demand and Reserved Instances. With On-Demand pricing, you pay for the capacity you use on an hourly basis, with no long-term commitments. Reserved Instances offer the option to reserve capacity for a one- or three-year term, with a lower hourly rate compared to On-Demand pricing. In addition to these pricing models, you can also choose between different node types, which offer different amounts of storage, memory, and compute resources.