Redshift is a Cloud-based Data Warehousing solution designed by Amazon Web Services to manage large datasets ranging up to petabyte-scale. It supports various Business Intelligence tools that enable developers to run faster queries and analyze all their data. Redshift is a fully managed data warehouse solution with advanced features.
However, there is often a dilemma when choosing between Amazon Redshift vs SQL Server. SQL servers are the open-source Relational Database Management System preferred by most developers to deploy web-based software applications. Also, the solution helps in creating and managing RDBMS systems.
This article provides a comprehensive comparison of Redshift vs SQL servers to give a clear idea of their working.
What is Amazon Redshift?
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Redshift is a popular Cloud-based Data Warehousing solution from Amazon Web Services. It helps perform data analysis at a large scale and delivers fast query performance. Also, Redshift is a highly cost-efficient storage system that manages petabytes of data. The solution comprises various features that further help in automating administrative tasks related to configuration, monitoring, or provisioning cloud data.
It also supports different business intelligence tools that help collect, store, and analyze data. Various high-profile companies today use AWS Redshift as it is fast, offers high-level security, and enables disaster recovery across regions in seconds.
Why Amazon Redshift?
Amazon Redshift is one of the fastest and fully managed data warehouse solutions that support various business intelligence tools for analyzing large data sets and delivering insights. Further, it supports numerous options like data compression, columnar storage, zone maps that help administrators reduce the I/O requirement essential for running queries.
It has a massively parallel processing (MPP) architecture under which large data sets are converted into small tasks. Thus, enabling companies to run high query performance at a faster speed. Another advantage of using redshift is users have to only for the resources they use. It also supports automated backup and fast restore features.
Key Features of Amazon Redshift
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- Network isolation: Redshift allows administrators to isolate their network for security purposes. Under this feature, administrators can configure firewall rules and restrict network access to an organization’s data warehouse cluster.
- Automated Backup and Fast Restore: Amazon Redshift automatically creates a backup for new data and stores it for a user-defined period. Users have full access to restore the cluster at any time via AWS Management Console.
- Fault-Tolerant: Each node within the cluster automatically creates a duplicate copy of the data if a node fails. Also, redshift maintains a continuous backup of data within the clusters to Amazon S3.
- Fully Managed Database: Amazon Redshift is a complete cloud-based suite that monitors cluster health, creates an automated backup, manages and scales data warehouse. Further, administrators can reduce the data size as per the need and performance. Also, it helps save time and energy for your employees and enables them to focus on other aspects for better results.
- Scalability: Redshift offers fast speed and consistent performance with no single point of failure. It has the capability to run unlimited concurrent queries and can store 8 PB of compressed data.
- Robust Security: It supports granular column and row-level security controls. Thus, only privileged users can access or view data. Apart from this, Redshift also offers end-to-end encryption and audit logging feature.
What is SQL Server?
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SQL Server is a Relational Database Management System designed exclusively for Windows environments by Microsoft. It has a Client-Server architecture and supports ANSI SQL. The purpose to introduce SQL service was to enable different users to create, manage, and implement RDBMS systems simultaneously. The application software is often used at the backend to store and process all system data.
Also, with the proper administration of SQL servers, users can recover, optimize, and maintain server performance. It is easy to install and configure Microsoft SQL Server compared to other database management software. Also, it has built-in transparent data compression features and other exclusive features that make it a top choice among the rest.
Why SQL server?
Unlike other database management systems, SQL server does not lack consistency. It has an easy interface that stores all the data in the backend and processes it. Another feature that makes it a great option is data recovery and restoration.
Today, most companies prefer the application software because it provides automatic updation, which further helps reduce maintenance costs. It supports log files and data caching features for better security.
Also, the popular RDBS software reduces the risk of losing data by creating frequent backups. One can quickly install the application software using the setup wizard. Thus, reducing manual workloads with an SQL server.
Key Features of SQL Server
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- Lower Cost of Ownership: SQL server includes various data mining, disk partitioning, and data management tools that aid companies in maintaining and storing sensitive data.
- Data Restoration and Recovery Features: In most cases, the data may get corrupted with server shutdowns. But, the application software supports various features that help developers to create frequent backups, recover, and restore lost information. Users can easily restore the entire database with SQL servers.
- Enhanced Security: The software uses encryption algorithms to keep data secure at all times. With encrypted data, attackers find it hard to break the security layers. Thus, it reduces the risk of threats and cyberattacks.
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Redshift vs SQL server: Key Differences
Redshift and SQL servers are popular solutions used by various developers to manage and store data. In this section, we have created comparisons of Redshift vs SQL servers based on the following factors:
1. Redshift vs SQL Server: Purpose
Redshift is implemented in the C language as a large-scale Data Warehouse solution that uses Business Intelligence tools to perform analysis. On the other hand, MySQL is implemented in C and C++ languages as an open-source RDBMS solution used by developers to perform backend operations.
2. Redshift vs SQL Server: Scalability
MySQL has the capability to load small data volumes more frequently, whereas redshift can load large data volumes less frequently but with greater efficiency. Redshift is highly scalable and can handle spikes in workloads. It has the ability to maintain consistent SLAs and improve the throughput by over 35 times simultaneously.
3. Redshift vs SQL Server: Pricing
Redshift starts at as low as $0.25 per hour and scales up to petabytes of data and thousands of concurrent users. You will need to pay hourly based on node usage. Pricing in Redshift includes both storage and computing costs.
SQL server uses a per-core licensing model. Here is the SQL server pricing for different editions:
- SQL Server Enterprise Edition: $7,128 per core
- SQL Server Standard Edition: $1,859 per core
- SQL Server Standard Edition Server Licensing: $931 plus $209 per named user client access license (CAL)
Redshift is a relatively cheaper tool than SQL server. However, the pricing is dynamic, so there is always a risk of exceeding the cost.
4. Redshift vs SQL Server: OS Support
MySQL supports various Operating Systems, including Linux, Windows, Solaris, OS X, etc. However, Redshift is a hosted warehouse solution compatible with various SQL-based clients. It supports numerous applications, including business intelligence (BI) tools, analytics, and other reporting tools to run fast queries.
5. Redshift vs SQL Server: Cloud Support
Redshift is a Cloud-based solution, whereas MySQL is suitable for all environments. Redshift is a data warehouse solution offered by Amazon that allows users to store petabytes of data in the clusters and run queries simultaneously. On the other hand, SQL Server is a relational database management system designed by Microsoft for cloud and other environments.
6. Redshift vs SQL Server: Security
Both solutions make sure that the data remains protected at all times. They comprise various features that add more security to sensitive data. MySQL offers data encryption, secure connections, granular controls, and authorization services to protect data. However, Redshift provides Load Data Encryption, VPCs, SSL Connections, Column-level Access Control, and more features to safeguard the data.
7. Redshift vs SQL Server: XML and API
MySQL supports XML on its services and uses Native API along with JDBC and ODBC connections, whereas Amazon Redshift offers no XML support and allows the use of Business Intelligence tools along with JDBC and ODBC connections.
Redshift vs SQL Server: Use Cases
Most companies that prefer business intelligence tools to run queries on semi-structured and structured data, go with Amazon Redshift. It is one of the cost-effective data warehouse solutions that help deploy applications faster and create quality reports.
Companies often select Microsoft SQL Server in cases that need a relational database management system to store and manage data for CRM, ERP, and Automation. Also, for Kubernetes support.
Conclusion
In this article, you learned about the key differences between Amazon Redshift vs SQL Server. This article highlighted its features and reasons why you can select Amazon Redshift or SQL server to store and manage data. Redshift is a fully managed data warehouse solution, whereas SQL Server is a relational database management system. Amazon Redshift is fault-tolerant and has a massively parallel processing (MPP) architecture. On the other hand, SQL server Client-Server architecture supports ANSI SQL. Both the tools are scalable, help run queries, and deliver insightful reports for better performance and results. Thus, follow the above-listed factors to compare Redshift vs SQL server and select the suitable one for your business.
As you collect and manage your data across several applications and databases in your business, it is important to consolidate it for a complete performance analysis of your business. However, it is a time-consuming and resource-intensive task to continuously monitor the Data Connectors. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse like Amazon Redshift, Databases like MS SQL Server, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.
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Tell us about your experience of comparing Amazon Redshift vs SQL Server! Share your thoughts with us in the comments section below.
Satyam boasts over two years of adept troubleshooting and deliverable-oriented experience. His client-focused approach has enabled seamless data pipeline management for numerous SMEs and Enterprises. Proficient in Hevo’s ETL architecture and skilled in DBMS sources, he ensures smooth data movement for clients. Satyam leverages automated tools to extract and load data from various databases to warehouses, implementing SQL principles and API calls for day-to-day troubleshooting.
