Amazon Redshift vs. DynamoDB: 8 Crucial Differences & Advantages & Which One Is Right for Your Business

Amazon Redshift and Amazon DynamoDB are two distinct database offerings from Amazon Web Services (AWS). Each database caters to different business needs in terms of database structure, performance, scalability, and use cases. This article will discuss each database’s strengths, as well as the key differences between the two databases. You will then be able to determine which database best suits your business needs.

What is Amazon Redshift?

Amazon Redshift is a fully managed, cloud-based data warehouse provided by Amazon Web Services (AWS). Redshift can handle data analytics for structured data on a large scale, using column-based storage and distributed architecture. It is ideal for the warehousing of petabytes worth of data. You can use popular business intelligence (BI) tools to query Redshift data, such as Power BI and Tableau.

Key benefits of Amazon Redshift

• Highly adopted and supported: Redshift has been adopted by many different industries, including healthcare, finance, and retail. Like DynamoDB, it is part of the hugely popular AWS platform used by millions of customers. Both Amazon paid support and free community support are widely available.
• Optimized for data warehousing: Redshift is specifically designed for data warehousing because it can handle large datasets and complex analytical queries. Its use of Massively Parallel Processing (MPP) means that data is spread over multiple nodes in a cluster. This speeds processing, as queries can be processed in parallel.
• Easy to manage: Redshift is easier to manage than traditional on-premises data warehouses and databases since it takes advantage of the AWS ecosystem to handle many routine tasks. Redshift tools can handle administration tasks such as creating clusters, scaling, creating snapshots, and monitoring performance. Depending on the complexity of your data and queries, you may not need a full-time database administrator to manage Redshift.
• Easy to query: Redshift uses the SQL standard of querying. Anyone familiar with SQL, particularly PostgreSQL, can query Redshift. Redshift works seamlessly with popular BI tools that support SQL querying.

What is Amazon DynamoDB?

Amazon DynamoDB is a fully managed NoSQL database service that provides fast, flexible, and highly scalable performance to large amounts of unstructured or semi-structured data, such as documents and key-value data. You might consider a NoSQL database if you need to manage data that does not fit neatly into tables, such as documents and videos.

DynamoDB is perfect for use cases that demand high availability and low latency, such as gaming, Internet of Things (IoT), and real-time bidding systems. DynamoDB is also part of the AWS ecosystem and only works with AWS.

Key benefits of Amazon DynamoDB

• Easy to set up and use: DynamoDB is a fully managed service, meaning there is no server to set up and maintain. AWS fully manages server maintenance, including backup management and hardware provisioning.
• Highly scalable: DynamoDB uses partitions to scale horizontally. Simply add partitions as your business grows. DynamoDB can also scale automatically up or down based on traffic patterns.
• Near real-time data replication: DynamoDB offers near real-time data replication through DynamoDB global tables. This feature allows you to replicate your DynamoDB tables across multiple AWS regions in real time, allowing for high availability and disaster recovery.
• Fully managed: DynamoDB is fully managed by AWS. AWS performs most administrative tasks, such as provisioning resources, scaling, updates, and security. However, like Redshift, DynamoDB works only within AWS.

8 Key differences between Redshift and DynamoDB

Both Redshift and DynamoDB offer the ease and security of AWS, but how do they differ? You should understand the key differences before deciding on Redshift or DynamoDB for your business.

• Performance: If you need to perform intensive data analytics on big data, Redshift may have better performance than DynamoDB. Redshift’s columnar structure and its use of MPP make it ideal for complex, large-scale queries on millions of rows of data. DynamoDB also has robust performance, offering low latency (in milliseconds) regardless of the table size.
• Scalability: DynamoDB can be easier to scale than Redshift because of its horizontal scalability—DynamoDB simply adds partitions as your data grows. Since DynamoDB is a NoSQL database, it has no schema that would need to be updated. Redshift is also scalable, but it requires more manual intervention, such as resizing clusters. Redshift has a set schema, which may need restructuring as your data evolves.
• Availability: DynamoDB has an advantage over Redshift in terms of availability, making it well-suited for applications with heavy workloads. It performs data replication over three availability zones in AWS, offering nearly uninterrupted service. Redshift also has high availability. Redshift replicates data across nodes within the cluster. However, Redshift does not offer automatic data replication across availability zones like DynamoDB does.
• Storage limits: With RA3 node types, Redshift allows for unlimited data storage on Amazon S3 through Redshift Managed Storage. As long as you have the compute resources, Redshift can scale storage dynamically. Similarly, DynamoDB tables have no storage limits. It will scale automatically based on the amount of data stored. However, DynamoDB has a size limit of 400 KB per item. If your data exceeds this limit, you may need to split the data across multiple items.
• Data replication: In DynamoDB, real-time data replication across multiple AWS regions is built in, with no manual intervention necessary. Redshift has a different architecture and handles data replication differently. Redshift performs data mirroring across nodes within a cluster. However, Redshift does not perform real-time data replication automatically. Redshift takes periodic snapshots of the data for disaster recovery.
• Data structure: Redshift supports a traditional, table-based database structure that accepts SQL queries. DynamoDB stores data as key-value pairs or as documents. It uses a NoSQL query model. DynamoDB’s data structure is perfect for applications that make use of time-series data, such as IoT data.
• Use cases: If your needs include storing large amounts of structured data and the ability to consolidate data from various systems, Redshift is the better choice. But if your use case involves low-latency workloads and real-time data updates with minimum downtime, such as mobile apps, DynamoDB better suits your needs.
• Pricing: Redshift and DynamoDB have different pricing models since they are designed for different types of workloads. Redshift is the more cost-effective option for large-scale analytical workloads that have some consistency. Its pricing depends on the type and number of nodes in your cluster. Redshift’s RA3 nodes give you the flexibility to scale and pay for compute and storage independently. You can purchase reserved instances of Redshift at a discount, so if your needs are predictable, this is a cost-saving option. DynamoDB can be more cost-effective for high-volume workloads and unpredictable usage since the price scales up or down depending on usage.

Redshift and DynamoDB: Which should you choose?

If you are using AWS, Redshift and DynamoDB are both good fully managed database options. The one you choose depends on the data you need to store and the nature of your applications. If your data is structured with a set schema, and you need to perform complex queries quickly on large data sets, Redshift is the answer. Examples of businesses that use Redshift include e-commerce, financial services, and healthcare.

If you have semi-structured and unstructured data that does not fit neatly into a schema, and you want a very scalable database that supports high transaction rates, DynamoDB may be the better option. Examples of businesses that use DynamoDB for their applications include gaming apps, IoT (such as sensors and devices), and streaming applications.

Connect to your Amazon data with CData Connect Cloud

Whether you choose Redshift or DynamoDB as your AWS-managed database, you can connect it with CData Connect Cloud. Connect Cloud provides direct, live connectivity to SQL and NoSQL data from many popular BI, analytic, reporting, data visualization, ETL, and no-code tools, such as Power BI, Tableau, and Looker, among others. Sign up for a free trial of CData Connect Cloud.