Amazon Redshift vs MySQL- A Comparative Analysis

One of the biggest concerns for a data-driven organization is handling the data effectively. Therefore, choosing the right database is crucial for efficient data management. However, with many options, it can get challenging to find the right tool according to your use case. 

Two of the most popular storage systems organizations use are Redshift and MySQL. While they both serve to store your data, there are huge differences between both tools' use cases and functionalities. In this article, you will learn about both tools in detail, including their overview, key features, and differences. 

Amazon Redshift Overview

Created in 2012 by Amazon Web Services, Redshift is a leading cloud-based data warehouse solution. The platform is designed to operate in a serverless architecture to handle analytical workloads. Redshift is a column-based storage system that allows you to store exabytes of data efficiently in a centralized repository. In addition, Redshift shines in its ability to handle large-scale data in structured and semi-structured formats, including CSV, JSON, and Avro file format. 

Key features of Redshift: 

• Petabyte Scale Warehouse: The automated managed storage of Redshift supports workloads of up to 8 petabytes (PB) of compressed data. Using this robust storage capacity, you can add almost any type or number of data to your data warehouse. 
• ‍Federated Queries: Federated queries in Redshift allow you to query live data across different platforms in Amazon Relational Database Services (RDS). This includes querying data from Aurora PostgreSQL and MySQL without data migration. 

MySQL Overview

Created in 1995 by Oracle, MySQL is a widely popular relational database management system (RDBMS). It allows you to store data in a structured way in relational tables within rows and columns. Using Structured Query Language (SQL), you can use MySQL to query, define, and manipulate data. It is a part of the widely used tech stack, LAMP (Linux, Apache, MySQL, and PHP), which is why MySQL is used in building web applications, native applications, and other services. MySQL runs on all major computing platforms and operating systems, including Unix-based, windows, and Mac OS. Some big MySQL organizations are Airbnb, Pinterest, Shopify, and Uber.

Key features of MySQL include: 

• Open Source: You can install and use MySQL without any licensing cost because of its open-source nature. Also, its vibrant community provides support through documentation and forums in case you run into unwanted errors and issues. 
• ACID Compliance: MySQL maintains data integrity and consistency by following ACID (Atomicity, Consistency, Isolation, Durability) compliance. The first part, atomicity, ensures every transaction operation is handled as a separate unit. Consistency ensures that data is valid before and after a transaction. Isolation prevents multiple concurrent transactions with one another. Lastly, durability ensures that transaction updates are saved permanently, even in system failure. 

Redshift Vs MySQL: Key Differences

Redshift Vs MySQL: In-Depth Comparison 

Here is a detailed comparison between Redshift and MySQL:

Key Components

Redshift has the following components:

• Cluster: In Redshift, a cluster is a set of nodes that stores the data. A cluster includes a leader node and compute nodes. 
• Nodes: Nodes store data and execute queries in parallel. The quantity of nodes in a cluster directly affects the query performance and storage capacity. 

MySQL has the following components: 

• Table: A table is a collection of related data within rows and columns, where the data is stored in a structured manner. 
• Primary Key: A primary key is a unique identifier for every row in a table. It is usually used to enforce data integrity and enable efficient querying. 

Architecture

Redshift has a distributed architecture, where data in a cluster is distributed across several nodes. The cluster is mainly divided into a leader node and other compute nodes. The leader node manages coordination with query execution, and compute nodes store data and execute queries in parallel. The data in Redshift is stored in columnar format, which improves its query performance and reduces the storage required. 

Contrarily, MySQL operates in a single server architecture where the data resides on a single server. The database follows a client-server model where a MySQL server accepts connections from multiple clients and processes their queries. MySQL's architecture includes a storage engine framework that allows users to choose from one of its storage engines, such as MyISAM or InnoDB, to optimize the database for specific requirements. 

Scalability

As mentioned above, Redshift is designed to scale horizontally, which allows you to add nodes according to increasing data volume and query complexity. Its massive parallel processing capabilities automatically distribute data and queries across multiple nodes for high performance. 

On the other hand, MySQL supports vertical scaling, which is mainly limited by the capacity of a single server. It is ideal for handling intermediate-sized datasets efficiently, and you may face some issues when dealing with large-scale data processing. However, MySQL can also support horizontal scaling, but you have to put in a lot of manual effort, like replication, sharding, and using third-party tools. 

Pricing 

Amazon Redshift follows a pay-as-you-go pricing model. This pricing model allows you to pay for resources you consume based on cluster size and datasets. You can start small at $0.25 per hour and scale up to terabytes and petabytes of data and many concurrent users. To learn more about Redshift's pricing, click here. 

MySQL is an open-source software, and almost every functionality of this tool is free. However, if you have custom requirements and need specialized support, MySQL has an enterprise edition. It offers three paid products: Cloud, Software, and Embedded. Learn more about products and pricing from here. 

Redshift Vs MySQL: Use Cases

Redshift has a huge list of use cases. Below are the most common ones: 

• Dedicated Data Warehousing: Redshift is a cloud-based, fully managed petabyte-scale data warehouse primarily used to centralize data from multiple data sources. Allowing you to consolidate data is a foundation for data reporting and analytics to acquire real-time insights. 
• Business Intelligence And Analytics: Using its ability to handle large volumes of data, Redshift can serve as an ideal platform to perform analytics. You can integrate third-party tools and business intelligence platforms and leverage platforms like AWS Glue to enable advanced analytics and machine learning workflows.

On the other hand, MySQL also has many use cases. Here are some: 

• General Purpose Database: MySQL is a general-purpose database that efficiently handles small to intermediate datasets. Mainly, it is used where fast read and write operations are required, such as web applications or other transactional workloads.
• Online Transaction Processing (OLTP): MySQL, by default, is used for OLTP systems where individual records such as customers, accounts, and sessions are stored in rows efficiently. Therefore, it is mostly used in content management systems, e-commerce platforms, and financial transactions. 

Streamline Data Integration to Redshift or MySQL With Airbyte

Now that you know the difference between both tools, you might want to take action. For that, SaaS tools like Airbyte have got you covered. 

‍Airbyte is a widely known data integration tool. It is designed to automate centralizing data from disparate sources to storage systems like Redshift or MySQL. With over 350+ pre-built connectors, Airbyte offers you the choice to create data pipelines from any data source. All these connectors work independently, allowing you to refresh, update, and monitor connectors individually. Along with connectors, the platform provides many features, including an easy-to-use graphical interface, task scheduling, configuration API, alerting, logging, etc. 

Some key features of Airbyte include: 

• Custom Connectors: With this extensive library, if you don't find a specific data source, you can create a custom one using its connector development kit. This enables you to integrate data from less popular connectors. 
• Change Data Capture (CDC): Airbyte provides a CDC feature for many connectors. CDC improves upon the traditional data ingestion approach by keeping a historical record of all the processes. Therefore, you can track data changes to an operational system in real-time. 

Conclusion 

Redshift and MySQL are the best at their use cases and have unique strengths. You can choose Redshift if you want to centralize huge loads of datasets to perform analytics. However, if you want a database that can efficiently serve small to intermediate datasets, MySQL is an ideal choice. 

After selecting the tool you want to use for storage purposes, you can use Airbyte to integrate data from any sources of your choice. Its automated graphical user interface and monitoring and scheduling capabilities make data integration seem almost like a non-technical process.