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 AWS DynamoDB and AWS Redshift so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how AWS DynamoDB 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.

AWS DynamoDB vs AWS Redshift Breakdown


 
Database Model

Key-value and document store

Data warehouse

Architecture

DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations

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

Closed source

Closed source

Use Cases

Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing

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

Scalability

Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication

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

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AWS DynamoDB Overview

Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.

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.


AWS DynamoDB for Time Series Data

DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.

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.


AWS DynamoDB Key Concepts

Some of the key terms and concepts specific to DynamoDB include:

  • Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
  • Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
  • Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
  • Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.

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.


AWS DynamoDB Architecture

DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:

  • Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
  • Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
  • Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.

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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AWS DynamoDB Features

Auto scaling

DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.

Backup and restore

DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.

Global tables

DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.

Streams

DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.

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.


AWS DynamoDB Use Cases

Session management

DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.

Gaming

DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.

Internet of Things

DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.

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.


AWS DynamoDB Pricing Model

DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.

On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.

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.