Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools. MySQL, on the other hand, is a Relational Database Management System. This open-source tool is one of the best RDBMS available in the market that is being used to develop web-based software applications.
This article discusses the Redshift MySQL comparison to give you an idea about them in detail based on 8 key factors. This would help you make an educated decision when picking a tool tailored to your needs.
Without comparison, you can also check our article on how to move your data from MySQL to Redshift.
Table of Contents
Introduction to Amazon Redshift
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Amazon Redshift is essentially a storage system that allows companies to store petabytes of data across easily accessible “Clusters” that you can query in parallel. Every Amazon Redshift Data Warehouse is fully managed which means that the administrative tasks like maintenance backups, configuration, and security are completely automated.
Amazon Redshift is primarily designed to work with Big Data and is easily scalable due to its modular node design. It also allows users to gain more granular insight into datasets, owing to the ability of Amazon Redshift Clusters to be further divided into slices. Amazon Redshift’s multi-layered architecture allows multiple queries to be processed simultaneously thus cutting down on waiting times. Apart from these, there are a few more benefits of Amazon Redshift that are covered in the following section.
Key Features of Amazon Redshift
- Enhanced Scalability: Amazon Redshift is known for providing consistently fast performance, even in the face of thousands of concurrent queries. Amazon Redshift Concurrency Scaling supports nearly unlimited concurrent queries and users. By leveraging Redshift’s managed storage, capacity is added to support workloads of up to 8 PB of compressed data. Scaling is just a simple API call, or a few clicks in the console away.
- Easy Management: Amazon Redshift automates oft-repeated maintenance tasks so that you can focus on gathering actionable insights from your data. It is fairly simple to set up and operate. A new Data Warehouse can be deployed with just a few clicks in the AWS console. Key administrative tasks like backup and replication are automated. Data in Amazon Redshift is automatically backed up to Amazon S3. Amazon Redshift can replicate your snapshots to Amazon S3 asynchronously in a different region for disaster recovery. The Automatic Table Optimization selects the best distribution keys and sort method to enhance the performance efficacy for the cluster’s workload. Amazon Redshift also gives you the flexibility to work with queries in the console, or Business Intelligence tools, libraries, and SQL client tools.
- Robust Security: Amazon Redshift is known for providing robust data security features at no extra cost. Amazon Redshift allows you to configure firewall rules to take control of network access to a specific Data Warehouse Cluster. Amazon Redshift also specializes in granular column and row-level security controls that ensure that users can only view data with the right type of access. Apart from these, Amazon Redshift also delivers on its promise of reliability and compliance through tokenization, end-to-end encryption, network isolation, and auditing.
- Data Lake and AWS Integrated: Amazon Redshift allows you to work with data in various open formats that can easily integrate with the AWS ecosystem. Amazon Redshift makes it exceptionally easy to query and write data to your Data Lake in open formats such as JSON, ORC, CSV, Avro to name a few. The federated query capability allows you to query live data across multiple Aurora PostgreSQL and Amazon RDS databases to get enhanced visibility into the business operations. This is carried out without the need for any undesired data movement. The AWS Analytics ecosystem allows you to handle end-to-end analytics workflows without any hiccups. You can also bring in data from various applications like Google Analytics, Facebook Ads, Salesforce to an Amazon Redshift Data Warehouse in a streamlined manner.
- Flexible Performance: Amazon Redshift distinguishes itself by offering swift, industry-leading performance with a keen focus on flexibility. This is made possible through result caching, materialized views, efficient storage, RA3 instances, and high-performance query processing to name a few. Result Caching is used to deliver sub-second response times for repeat queries. Business Intelligence tools, dashboards, visualizations leveraging repeat queries experience a significant performance boost. At the time of execution, Amazon Redshift looks through the cache to see if there is a cached result for repeat queries. Amazon Redshift also uses sophisticated algorithms to classify and predict the incoming queries based on their run times and resource requirements to manage concurrency and performance dynamically. This helps users prioritize business-critical workloads.
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Introduction to MySQL
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MySQL is an easy-to-use RDBMS widely being used by big and small-scale businesses alike. Before delving into MySQL, you need to have an idea about RDBMS. RDBMS is a software that:
- Allows you to update indexes automatically.
- Guarantees the referential integrity between the rows of numerous tables.
- Enables a database implementation with columns, indexes, and tables.
- Interprets an SQL query and collates information from various tables.
MySQL is scalable, intuitive, and swift when compared to its contemporaries. It houses a Client-Server architecture. At the core of the MySQL database lies the MySQL Server. This server is availed as a separate program and handles all the database instructions, commands, and statements. This is how MySQL Server and MySQL database work in tandem:
- MySQL first creates a database that lets you build any number of tables you want. These tables are used to store and manipulate data and define the relationships between different tables.
- A client requests the command prompt or GUI screen by utilizing specific SQL expressions in MySQL.
- The server application responds with the requested expressions and displays the desired results on the client-side.
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The major benefits of using MySQL are as follows:
- MySQL uses the standard form of SQL.
- MySQL works well with PHP, the most widely appreciated language for website development.
- MySQL can be easily customized. The open-source GPL license lets programmers tweak the MySQL software to fit their specific environments.
- It also supports large databases, with up to 50 million rows per table.
A fully managed No-code Data Pipeline platform like Hevo helps you integrate data from 100+ data sources (including 40+ Free Data Sources) like MySQL to a destination of your choice such as Amazon Redshift in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line.
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Understanding Redshift MySQL Differences
So far in this article, you have seen the features and benefits of Amazon Redshift and MySQL. To understand, which tool would suit you better, you can consider the following 8 factors. The factors to keep in mind to understand the Redshift MySQL differences are as follows:
Redshift MySQL Differences: Purpose
Amazon Redshift is a large-scale Data Warehouse service offered by Amazon that helps serve as the single source of truth for carrying out an insightful analysis using Business Intelligence tools. MySQL on the other hand is an open-source Relational Database Management System developed by Oracle. MySQL is most widely used for the backend operations of website development due to its affinity for PHP. Amazon Redshift is implemented in the C language while MySQL is implemented in C and C++ languages.
Redshift MySQL Differences: Scalability
When you compare MySQL and Amazon Redshift in terms of scalability then MySQL is more suited to effectively load small volumes of data more frequently. Amazon Redshift, on the other hand, can load large volumes of data less frequently with greater efficiency.
In terms of performance, Amazon Redshift beats MySQL by a large margin with an impressive query time, especially when data is compressed. Redshift’s compression saved up to 60% of disk space which firmly establishes a dominant profile for Amazon Redshift.
Redshift MySQL Differences: OS Support
When considering Server Operating Systems as a factor for Redshift MySQL comparison, Amazon Redshift is a hosted solution. MySQL, on the other hand, supports Linux, OS X, Windows, Solaris, and FreeBSD.
Redshift MySQL Differences: Cloud Support
Amazon Redshift is a Cloud-based Data Warehousing tool. MySQL was developed for all use cases.
Redshift MySQL Differences: XML and API
Amazon Redshift offers no XML support to the users. MySQL on the other hand does offer XML support on its services. Amazon Redshift gives you swift querying abilities over Structured data utilizing familiar SQL-based clients, Business Intelligence tools, and standard JDBC and ODBC connections. MySQL uses ADO.NET and Native API along with JDBC and ODBC connections.
Redshift MySQL Differences: Security
MySQL protects your data with authentication services, secure connections, data encryption, and fine-grained controls and authorization. Amazon Redshift offers a multitude of features to protect your data such as Cluster Security Groups, SSL Connections, Load Data Encryption, VPCs, and Column-level Access Control to name a few.
Redshift MySQL Differences: Pricing
MySQL is an open-source tool which means you don’t have to pay for this service. However, you do need to pay for the services of Amazon Redshift. Amazon Redshift offers different types of nodes to accommodate your workloads.
You can choose from DC2 or RA3 based on data size, growth, and required performance. The RA3 nodes allow you to optimize your Data Warehouse by paying and scaling for compute and managed storage independently. DC2 nodes can be utilized when you have compute-intensive Data Warehouses with local SSD storage to deal with. DS2 nodes can be put to use when you need to create large Data Warehouses using Hard Disk Drives (HDD). Here are the pricing plans offered by Amazon Redshift:
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Conclusion
This article gives you an idea about the factors to keep in mind for Redshift MySQL comparison. It also explores the basics of the two tools, highlighting the features and benefits of each.
Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be challenging, and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications like Amazon Redshift or MySQL into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
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Amit is a Content Marketing Manager at Hevo Data. He enjoys writing about SaaS products and modern data platforms, having authored over 200 articles on these subjects.