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Amazon RDS vs Amazon Redshift: What are the differences?

Introduction: Here we will explore the key differences between Amazon RDS and Amazon Redshift.

  1. Database Purpose: Amazon RDS is a relational database service designed for OLTP workloads, offering support for various database engines such as MySQL, PostgreSQL, SQL Server, etc. On the other hand, Amazon Redshift is a data warehousing service ideal for OLAP workloads, specifically optimized for analyzing large datasets.

  2. Data Scaling: Amazon RDS allows for vertical scaling, meaning you can resize your database instance, but there are limits to how much you can scale vertically. In contrast, Amazon Redshift enables horizontal scaling by adding nodes to the data warehouse cluster, providing superior scalability for handling massive amounts of data.

  3. Query Performance: Amazon RDS is optimized for transactional processing and may not perform as efficiently for complex analytical queries on large datasets. Amazon Redshift, being a cloud-based data warehouse, is specifically designed to offer fast query performance for analytical workloads, including complex joins and aggregations.

  4. Pricing Model: Amazon RDS charges based on the instance type, storage, and data transferred. In contrast, Amazon Redshift pricing is based on a combination of the type and number of nodes in the cluster, along with the amount of data stored, providing more flexibility for cost optimization depending on usage patterns.

  5. Backup and Restore: Amazon RDS provides automated backups and restores, allowing you to recover your database to any point in time within the retention period. With Amazon Redshift, you can take snapshots of your data warehouse at specific points in time and restore them, but the process is more geared towards creating and restoring full cluster backups.

  6. Concurrency: Amazon RDS is suited for handling multiple concurrent connections typical in OLTP applications. Amazon Redshift, being optimized for analytics, can efficiently handle multiple queries running in parallel and support high levels of concurrency for complex analytical workloads.

In Summary, Amazon RDS is ideal for OLTP workloads with relational database needs, while Amazon Redshift is tailored for OLAP workloads with a focus on analytical processing and scalability.

Advice on Amazon RDS and Amazon Redshift

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Replies (3)
John Nguyen
Recommends
on
AirflowAirflowAWS LambdaAWS Lambda

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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on
AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Pros of Amazon RDS
Pros of Amazon Redshift
  • 165
    Reliable failovers
  • 156
    Automated backups
  • 130
    Backed by amazon
  • 92
    Db snapshots
  • 87
    Multi-availability
  • 30
    Control iops, fast restore to point of time
  • 28
    Security
  • 24
    Elastic
  • 20
    Push-button scaling
  • 20
    Automatic software patching
  • 4
    Replication
  • 3
    Reliable
  • 2
    Isolation
  • 41
    Data Warehousing
  • 27
    Scalable
  • 17
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage

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What is Amazon RDS?

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

What is Amazon Redshift?

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

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Jul 9 2019 at 7:22PM

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What are some alternatives to Amazon RDS and Amazon Redshift?
Apache Aurora
Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
Oracle
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
Heroku Postgres
Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
Google Cloud SQL
Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.
See all alternatives