Within the last few years, the amount of data has increased dramatically. This has necessitated the development of data warehouse technologies capable of efficiently managing and analyzing all incoming data.
With a rise in terabytes of raw data being collected at different levels, Data warehouses have evolved into an essential part of harnessing data to acquire broader business and consumer insights.
All of the information collected by any organization must end up in the best place for analytics to make data-driven decisions for business growth.
Amazon Redshift and Snowflake are two best-in-class data warehouse cloud technologies. Both platforms are industry leaders in the data warehouse market, and selecting one over the other might be difficult.
Choosing one out of two is not about which solution is superior to the other; it is about which solution will fit best to your needs. Here in this article, we will discuss more Amazon Redshift and Snowflake.
What is AWS Redshift?
Amazon Redshift is a fully-managed and cloud-based data warehouse platform that can store and analyze enormous amounts of data in real-time for analytical insights. To begin utilizing Redshift, you must first work with Redshift clusters.
After properly allocating the clusters, you can upload data sets to conduct data analysis. You can share data across many clusters. It enables customers to query data from numerous databases, clusters, and multiple AWS accounts, without transferring the data.
A cluster is made of sets of node, which has their own CPU, Storage, and memory. Every cluster has a leader node who handles all work such as query execution, communication, and node management.
Redshift Spectrum is a feature of Redshift which allows users to conduct SQL queries directly on data stored in the S3. Also, it supports different data types such as Parquet, JSON, file format, and Avro. As a result, the spectrum provides quicker data access and analysis.
Furthermore, Redshift ML enables customers to incorporate Machine Learning capabilities within the cluster by facilitating a safe and straightforward interaction between Sagemaker and Redshift. You can also connect Redshift with different Business Intelligence(BI) tools.
Some of the significant advantages of using Snowflake are:
- Provides a user-friendly console for data management and query.
- It easily integrates with other AWS services as Redshift is a native offering of AWS.
- Uses PostgreSQL syntax to work seamlessly with SQL data.
AWS Redshift is regarded as the ideal data warehouse for situations in which-
- Your company is already using AWS, and you are looking for a fully managed data warehouse. It will be easy for you to get started with Redshift.
- High query load on the application.
- Workloads process structured data.
What is Snowflake?
Snowflake is a Software as a Service(SaaS) and cloud-based data warehouse that delivers structured and layered data analytic insights. It allows you to store and scale data independently with contemporary data architecture and maximum flexibility. Snowflake uses SQL, which makes it easy for anybody who knows SQL to comprehend and interact with it.
The virtual warehouse idea is used in Snowflake’s simple, quick, and flexible design. This virtual warehouse runs on top of the database storage service and allows you to create several data warehouses using the same data. In addition, snowflake separates computing and storage and will enable you to integrate third-party services such as s3 and EC2.
Some of the significant advantages of using Snowflake are:
- First, the warehouse platform does not need to be installed, configured, or managed by organizations.
- Integrates with the majority of the data ecosystem tools and AWS services.
- Provides an easy-to-use SQL interface
- Easy to set up and use
- Account-to-account data exchange is enabled
Snowflake is regarded as the ideal data warehouse for circumstances in which –
- Frequent scaling is required
- lighter query load
- An automated data warehouse solution is required with a low operational overhead.
AWS Redshift vs Snowflake: Main difference
The main difference between AWS Redshift and Snowflake is that AWS Redshift is more suited to high-performance workloads compared to Snowflake.
You can also use ML capabilities and business intelligence tools with it. Let’s look at some of the significant differences between both platforms.
AWS Redshift vs Snowflake: Architecture
AWS Redshift employs a cluster-based design with numerous nodes, with a leader node in charge of all the cluster’s tasks. Redshift uses industry-standard JDBC or ODBC for client application communication. Snowflake’s architecture is integrated with a SQL query engine.
AWS Redshift vs Snowflake: Performance
Snowflake and Redshift use massively parallel processing(MPP) and columnar storage for concurrent computing, allowing for sophisticated analytics and considerable time savings on large operations. Redshift performs well on most data types; however, it suffers while dealing with semi-structured data, for instance, JSON files.
Users are advised to adopt distribution keys for maximum performance, defining database segments to store a particular row of data. Snowflake separates computing and storage, allowing for parallel workloads and the execution of numerous queries simultaneously.
The workloads do not affect each other, resulting in speedier performance. Unlike Redshift, Snowflake can accommodate semi-structured and structured both types of data.
AWS Redshift vs Snowflake: Data Backup and Recovery
Redshift has both automatic and manual snapshot systems that are used in case of any unseen event to recover the data. AWS S3 is used to store snapshots through a secured SSL connection. Snowflake employs fail-safe. The fail-safe technique provides a 7-day interval during which any lost Snowflake data can be restored.
AWS Redshift vs Snowflake: Security
Amazon Redshift offers end-to-end encryption. End-to-end encryption in Redshift may be customized to meet your specific security needs. You may also isolate your network within a VPC and connect it to your current IT infrastructure using VPN.
AWS CloudTrail integration enables audits to assist you in meeting compliance obligations, and It also meets PCI, ISO, HIPAA, and BAA requirements. Snowflake also offers encryption with VPC and VPN features.
Read also: AWS Cloudtrail vs CloudWatch
Still, the only difference between Snowflake and Redshift is that the robustness of Snowflake security grows with the tier or edition of the product you are using. Further, Snowflake adheres to several data protection standards, including PCI, HIPPA, SOC 1 Type 2, SOC 2 Type 2, and HITRUST.
AWS Redshift vs Snowflake: Use Case
Redshift is appropriate for any organization that deals with vast amounts of data and queries that require a speedy response. It’s also an excellent option for companies searching for a data warehouse with a precise price strategy and few administrative expenditures. On the other hand, the snowflake is appropriate for businesses seeking an accessible setup data warehouse with practically infinite, automated scalability and outstanding performance.
AWS Redshift vs Snowflake: Pricing
For on-demand pricing, Redshift is far more affordable than Snowflake. Also, Redshift provides one and three years reserved Instances (RI), which allows you to save more money.
Furthermore, Redshift charges by the hour and node, whereas Snowflake charges by usage pattern. When the two services are compared in terms of cost per service increment, Redshift is at least 1.3 times more affordable. Also, in the pricing structure, Redshift combines storage and computing, but Snowflake separates the two.
Snowflake offers five product editions, and the pricing and performance of every edition vary. So you can choose the one which fits your bill. Also, both Amazon Redshift and Snowflake provide a free tier account.
AWS Redshift vs Snowflake: Complete Comparison
| AWS Redshift | Snowflake | |
| Owner | Amazon | Snowflake |
| Architecture | AWS Redshift uses the shared-nothing MPP architecture | Snowflake’s architecture is integrated with a SQL query engine. |
| Suitable For | AWS Redshift is suitable for Large Enterprises | Snowflake is suitable for enterprises who are looking for an easy-to-deploy data warehouse with unlimited automatic scaling |
| Security | Users and AWS both have responsibilities to ensure the data is secure | Snowflake is a secure cloud platform that complies with many data protection standards |
| Data Backup and Recovery | AWS Redshift has both automatic and manual snapshot systems for data backup and recovery. | Snowflake has a fail-safe approach, which recovers lost data within 7 days |
| Maintenance | AWS Redshift clusters require some manual maintenance | Snowflake requires no maintenance |
| Price | AWS Redshift offers on-demand and managed storage pricing. Redshift is more affordable than its counterpart when it comes to on-demand pricing. | Snowflake offers on-demand, pre-purchase, and tiered pricing plans. |
Wrapping up
Snowflake and Redshift are industry leaders in data warehouses, and they differ in multiple aspects. Snowflake is a SaaS solution that does no maintenance, and AWS Redshift clusters require manual upkeep. Also, Snowflake distinguishes between computing and storage, allowing for more flexible configuration and pricing.
Redshift also offers price flexibility with reserved and spot instances. Finally, snowflake has limited data customization features, and Redshift provides features like distribution and partitioning.
Related posts
Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.