DynamoDB vs Amazon Redshift - The Ultimate Comparison
Written by Nishani Dissanayake
Published on September 14th, 2022
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DynamoDB and Amazon Redshift are two widely used cloud-based database solutions offered by Amazon Web Services (AWS).
In this article, I will be discussing what these two services are and their similarities and differences.
DynamoDB and Amazon Redshift: An Overview
DynamoDB is a fully managed key-value and document-based database service offered by AWS. It is a reliable and flexible solution that swiftly and efficiently manages NoSQL data.
Amazon Redshift is a fully managed, highly beneficial solution to work with large data volumes. It is a cloud-based data warehousing service with robust performance rates. But unlike DynamoDB, Amazon Redshift involves SQL in analyzing data across data lakes, operational databases, and data warehouses.
Shared Attributes for DynamoDB and Amazon Redshift
Amazon Redshift and Amazon DynamoDB are both powerful cloud-based services fully managed by AWS, thus, enabling the developers to focus less on administration and infrastructure maintenance. In addition, though there are many differences between the two services, they are reliable and can fulfill high scalability requirements.
Performance of DynamoDB vs Amazon Redshift
Amazon DynamoDB can cope with any level of on-demand traffic and can handle more than 20 million requests per second, along with 10 trillion requests per day.
With the high I/O performance of the SSDs, there is low latency (millisecond latency) regardless of the table size. Following best practices in maintaining a DynamoDB table layout and using indexes and keys effectively in queries will boost the performance even more.
In addition, the user can improve the performance of DynamoDB by ten times using DynamoDB Accelerator (DAX) even at million requests per second.
As a reliable and robust data warehousing service, Amazon Redshift has a high performance even when using complex queries spanning millions of rows of data.
Columnar data storage, data compression, result caching, query optimizer, and several other performance features facilitate the speedy query execution of Amazon Redshift. You can find more about those here.
Amazon DynamoDB is a highly available database service with automatic data replicating over three availability zones. Therefore, it will provide uninterrupted service even during natural disasters or power outages.
Redshift also performs data replication within the data warehouse cluster. It mirrors the data of each drive to other nodes present within the cluster. Redshift continuously backs up data to S3. In case of a node failure, it automatically provisions new nodes and restores the data from S3 or the other drives within the cluster.
Storage and Limitations
DynamoDB has no restriction over the maximum size of the table. But the total item size (including the attribute name and attribute value) of each item in a table should not exceed 400 KB.
In Amazon Redshift, the maximum amount of cluster tables allowed depends on the cluster node type. For example, 8xlarge cluster node types allow 20000 tables while large and xlarge cluster node types allow 9900 tables.
DynamoDB offers two capacity modes for the users to select:
- On-demand - DynamoDB tables are automatically scaled up and down in on-demand mode based on the workload. This mode is ideal when the workload is unknown or the application traffic is unpredictable.
- Provisioned capacity mode - In provisioned capacity mode, a specific capacity needs to be requested by the user, depending on the utilization. This mode works best with applications where the traffic is highly predictable or when it is consistent.
DynamoDB can support any table size and provides seamless scalability depending on the requirement.
On the other hand, Amazon Redshift operates in Nodes and Clusters. So, you can add more nodes or upgrade existing ones for scaling. But as analytic workloads tend to be highly unpredictable, Amazon Redshift Concurrency Scaling is an ideal option to automatically scale the resources up and down, abiding by a maximum cluster size the user specifies.
DynamoDB offers two pricing models, and the standard pricing calculation depends on the reads, writes, and storage consumption. Other than the core features, enabling any additional features like backups will add a separate cost.
- On-demand - As the on-demand option allows the resources to ramp up and down based on the requirement, the pricing depends on the application traffic.
- Provisioned capacity - This pricing option charges the user based on the reads and writes the user specifies for the application. Auto-scaling can also adjust the table capacity depending on the specified criteria.
Apart from that, DynamoDB provides 25 GB of data storage, 2.5 million streams read requests, and 100 GB of data transfer out to the internet under the free tier.
The starting price for Amazon Redshift is $0.25 per hour. The user can then scale up as necessary based upon the requirement to a PB (petabyte) or more for $1,000 per TB per year. You have to pay as you go, and there are no upfront costs.
Apart from that, there are multiple pricing options for features like Concurrency Scaling and Redshift ML. You can find more necessary information along with the pricing examples here.
In addition, if you are creating an Amazon Redshift cluster, you’re eligible for a two-month free trial of DC2.Large node.
When and Where to Select Which Database Service
Use Cases for DynamoDB
The best NoSQL database model to use when working with AWS is DynamoDB. It is fast, efficient, scalable, and ensures high performance even during backups. Also, users can focus more on the application itself rather than paying for database administration and maintenance as AWS fully manages it.
With its plethora of features, DynamoDB proves to be a highly cost and time-effective solution compared to relational database systems. Furthermore, it is highly efficient with a high I/O need, with its single-digit millisecond performance.
Use Cases for Amazon Redshift
Amazon Redshift is the ideal solution when there is a requirement for a Petabyte-scale data warehousing service without the hassle of infrastructure maintenance. In addition, Redshift is comparatively cost-efficient and reliable for analyzing clickstream data and weblogs. So, it is great to analyze machine-generated data efficiently.
Overall, Amazon Redshift is perfect for handling mission-critical workloads and in instances where you have to perform complex queries for analytics over a large number of rows.
Amazon DyamoDB and Redshift are potent and highly available cloud-based database services that many users use. This article discussed their similarities and differences and several use cases too. So, this guide will help you to understand how to pick the best solution based on your requirement.
Thank you for reading!