Redshift and RDS are managed services offered by AWS. This means that AWS will manage the infrastructure, and you only need to worry about your data and applications. Redshift is a data warehouse service, while RDS is a relational database service.
Amazon Redshift is a fast, scalable data warehouse that makes it simple and cost-effective to analyse all your data across your data warehouse and data lake. Amazon Redshift is based on PostgreSQL. Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud.
Amazon RDS is available on several database instance types - optimised for memory, performance or I/O - and provides you with six familiar database engines to choose from, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server.
Understanding Redshift and RDS
Key features of AWS RDS:
- RDS is a cloud-based, fully-managed relational database service offered by AWS.
- RDS offers six different database engines to choose from.
- RDS is built on top of virtualised instances, which can be scaled for performance or storage as needed.
- Administrative tasks related to running a database are automated with RDS.
- Security and compliance features are built-in to RDS.
- RDS works on top of virtualized instances.
- Scaling for performance or storage is possible with RDS.
- The maximum capacity for RDS is 32 vCPUs and 244GB of RAM.
- The maximum storage capacity for RDS is 64 TB for the AWS Aurora database engine.
Key features of AWS Redshift:
- Redshift is a scalable data warehouse service offered by Amazon that uses columnar storage to optimize query performance.
- Redshift offers a choice of node types, each with different CPU and memory configurations.
- Redshift is a completely managed data warehouse as a service and can scale up to petabytes of data while offering lightning-fast querying performance.
- Scaling up or down on newer generation instances is a quick and easy process with the elastic resize feature.
- For older instances that do not support elastic resize, scaling can only happen incrementally over a few hours.
- AWS Redshift uses a cluster-based architecture with multiple nodes to maintain an optimal mix of scalability and performance.
- Among the nodes, one of the nodes is designated as a leader node, and this node is responsible for client communication, query optimization, execution plan creation, and sending tasks to individual nodes for execution.
- Like RDS, Redshift pricing is also including storage and compute resources, and customers can choose to pay only for what they use.
- While Redshift is mainly designed to support large analytical workloads, it can also be used for OLTP if necessary. However, using Redshift in this way is not recommended.
Redshift Vs RDS – Comparison
Now that we have gone over the AWS Relational database service and Redshift, it is time to compare the two managed services. We will be looking at the most critical factors that a data architect would use to decide between these technologies.
- Redshift Vs RDS: Scaling
- Redshift vs RDS: Pricing
- Redshift vs RDS: Storage Capacity
- Redshift vs RDS: Performance
- Redshift vs RDS: Maintenance
- Redshift vs RDS: Security
- Redshift vs RDS: Data Structure
- Redshift vs RDS: Data Replication
Scaling:
When comparing Redshift vs RDS, one of the most important factors to consider is scaling. Both Redshift and RDS allow customers to scale as needed in order to meet performance requirements and budget restrictions.
Scaling in RDS is very simple and can be done in just a few minutes via the AWS console. Redshift, on the other hand, has a more complex architecture which makes scaling less seamless.
That said, Redshift does offer an elastic resize feature for supported instances, which can minimize downtime. Additionally, Redshift's concurrency scaling option allows it to support virtually unlimited concurrent users without sacrificing performance.
Pricing:
If you're looking for a cheaper option for storage and compute, RDS is the way to go. However, if you need more scalability and power, Redshift is worth the investment. Keep in mind that with Redshift, you'll typically use multiple nodes, which will increase the price per hour.
Storage capacity:
When it comes to storage capacity and scalability, Redshift definitely has the upper hand. With the ability to scale up to petabytes of data, it's clear that Redshift is built for larger workloads.
However, RDS should not be discounted; it can still scale to a respectable 32 TB. So, when choosing between the two, it really comes down to the size of your data set. If you're dealing with large amounts of data, Redshift is the way to go. But if your data set is more minor, RDS will be more than sufficient.
Performance:
When it comes to performance, Redshift and RDS offer different benefits. RDS is better for queries that don't span millions of rows, since Redshift has a more sophisticated query optimizer and execution planner.
However, for queries that require scanning and aggregating millions of rows, Redshift performs better. Additionally, both databases offer performance improvements through key distribution mechanisms.
RDS offers sharding capability, while Redshift has options for SORT KEY and DIST KEY. Carefully designing keys can help extract more performance from either database.
Maintenance:
RDS is much easier to maintain than Redshift, since most of the administrative tasks are automated. With Redshift, you need to manually execute the VACUUM command to clear delete markers, and also periodically run the ANALYZE command to keep table statistics up to date. This can be a lot of work for the cluster administrator.
Security:
RDS and Redshift both offer robust security features to protect your data. RDS provides additional security features beyond the standard AWS features, so it is essential to manage these settings carefully.
Oracle native network encryption and Oracle transparent data encryption are two examples of these extra security features. Redshift also offers SSL support to ensure that your data is encrypted in transit.
Both platforms offer virtual private cloud (VPC) isolation to protect your data further. Ultimately, it is up to the customer to decide who has access to their data and what permissions they have. AWS Identity and Access Management (IAM) provides granular control over these settings.
Data structure:
RDS is a relational data store that uses a row-oriented structure, while Redshift has a columnar structure. RDS querying may vary according to the engine used, while Redshift conforms to the Postgres standard.
Redshift does not do an excellent job of enforcing unique constraints in insertion keys, so it is expected that end-users will manage it themselves. RDS offers support for unique key constraints in all of its database engines.
Data replication:
There are a few key things to remember when moving data to Amazon Redshift or RDS. First, it is essential to set up a robust system that can send data to Redshift/RDS in an accurate, reliable, and secure manner.
Second, you should remember that you are looking to move data into Amazon Redshift or RDS for critical business processes and insights. Hence, it is crucial to set up a system that can handle these workloads in a reliable and efficient manner.
Redshift vs RDS – Use cases
Both RDS and Redshift offer a database as a service, but they have many distinctions, as we explored earlier. They're both useful for different reasons and situations. We'll go over the various scenarios in which these services work best below.
When to use Redshift?
A redshift is a great option for those who want a petabyte-scale data warehouse that is more powerful than traditional database engines. Redshift's analytical and reporting capabilities are ideal for businesses with heavy workloads that need to be able to quickly process large amounts of data. Redshift is also a good choice for companies that anticipate complex queries and a constant query workload.
However, it is essential to note that Redshift does not automatically ensure the uniqueness of insertion keys - businesses will need to manage this themselves. Additionally, businesses will need to have a team in place that is willing to work with Redshift's DIST KEYS and SORT KEYS in order to get the most out of the database.
When to use RDS?
RDS is a good choice if you want to use traditional databases in the cloud and your only requirement is to offload database management. Your data volume is in TBs, and you do not anticipate a significant increase in the near future. RDS hits its storage limit at 64 TB.
You have an online transaction processing use case and want instant results with lesser data. You don’t have queries that span across millions of rows, and the query complexity is limited.
Your reporting and analytical workloads are minimal and do not interfere with your OLTP workloads. Your budget is tighter, and you have no intention of spending money anticipating future astronomical workloads.
Conclusion
Now you know everything that you need to know about RDS and Redshift. You should have a good understanding of the key similarities and differences between these two types of databases. You also know some real-world usage examples and guidelines for choosing the right database for your needs.
Before choosing a database, it's essential to understand your specific needs. If you're not sure which type of database is right for you, be sure to test both RDS and Redshift to see which one works best for your needs.
Whichever database you choose, make sure that you understand how to use it properly. In particular, be sure to learn about the different features and options that each database offers. With this knowledge, you'll be able to get the most out of your chosen database and use it to its full potential.
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