A good database management system is crucial for organizations dealing with highly critical ETL workloads. When it comes to best-in-class data warehouse cloud solutions, Amazon Redshift and Snowflake are top performers that come into mind and have revolutionized the quality of business intelligence insights.
In this blog post, we would be comparing Snowflake vs Redshift, the most used data warehouse services available in the market. So, let’s get started.
Amazon Redshift is a cloud-based data warehouse service that can be integrated with business intelligence tools to make smarter business decisions. You can start ETL (Extract, Transform, Load) process with a few hundred gigabytes of data and scale it up based on your business needs.
To launch the data warehouse, you must launch a set of nodes called the Redshift cluster. After that, data sets can be uploaded to process the data analysis queries. Irrespective of data size, take benefits of fast query performance by using the same SQL-based tools along with BI applications.
Snowflake is a powerful relational database management system built on AWS that handles both structured and semi-structured data. It provides a data warehouse that is user-friendly, faster, and flexible than those traditional data warehouse offerings.
To use a Snowflake data warehouse, one does not have to install or configure any software. Snowflake data warehouse completely runs on cloud infrastructure and uses a new SQL database engine having a unique architecture design.
Here's a quick guide on how these two cloud data warehouse solutions different from each other:
Now you must have a basic idea about Snowflake and Redshift, it would be easy to understand the difference between them. Below, we have listed the top 6 factors that would help you differentiate between Snowflake and Redshift and may influence your choice of Redshift vs Snowflake.
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Snowflake allows instant scaling in case of high demands without redistributing data or interrupting users. The auto concurrency let users set the min and max clusters size.
Redshift can scale but not as instant as Snowflake. It may take between minutes to hours for adding new nodes to the cluster. Therefore, we can say that Snowflake has an advantage over Redshift.
Both the data warehouse solution have different architecture and also they behave differently to the types of queries. Organizations that are already working with AWS or using AWS services like Athena, CloudWatch, Kinesis Data Firehose, Database Migration Service (DMS), and DynamoDB, for them Redshift is a natural choice and it can be integrated seamlessly.
Snowflake, in turn, will make it tough to integrate the data with tools like Athena and Glue. However, it provides easy integration with tools like Apache Spark, IBM Cognos, Qlik, and Tableau, etc.
Snowflake charges separately for computing and for storage whereas Redshift bundles both the charges together. If the query usage is minimal and scattered over larger time windows, Snowflake has better pricing to offer than Redshift. Snowflake clusters are not charged while there is no query load on them and shutdowns automatically when they are idle. Snowflake may offer better value to the customers having light query loads.
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Redshift charges per-hour per-node, which covers both computing and data storage. With Redshift, you can calculate the monthly price by multiplying the price per hour by the size of the cluster and the number of hours in a month.
Security is the heart of all activities. Redshift provides features and tools for Access management, Cluster encryption, Cluster security groups, Load data encryption, Sign-in credentials, Amazon Virtual Private Cloud, Data in transit, SSL connections, and Sign-in credentials.
Snowflake also boasts similar tools and features for data security and compliance with regulatory bodies. Here, a user must be aware of which edition he/she is working with because the security features are not present in all versions.
Redshift uses the COPY command to load the data whereas Snowflake uses the COPY INTO command for loading data. Snowflake allows users to use multiple clouds and third-party storage services. Because the compute part is charged separately, users can select storage services and use snowflake only as a compute engine. But Redshift offers limited flexibility in this field.
In Amazon Redshift, users have to forcibly look at the same cluster and compete over the available resources. Also, users have to use WLM queues in order to manage it which is quite challenging. But this problem does not arise in the case of Snowflake. Users can seamlessly look at the same data from different data warehouses. To vacuum and analyze the tables on a regular basis, Snowflake offers a turnkey solution. Whereas with Redshift, it becomes a problem due to the challenge in scaling up or down.
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It would be a partiality favouring any of these data warehouse solutions because each of them has its own pros and cons. It might be possible that for one business Redshift is the best choice but for others, Snowflake may be the right choice.
Snowflake is the best platform to start and grow with but you can opt for Redshift as a cost-efficient solution for enterprise-level implementations. However, we hope you must have understood the differences between Redshift and Snowflake.
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Pooja Mishra is an enthusiastic content writer working at Mindmajix.com. She writes articles on the trending IT-related topics, including Big Data, Business Intelligence, Cloud computing, AI & Machine learning, and so on. Her way of writing is easy to understand and informative at the same time. You can reach her on LinkedIn & Twitter.
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