Successful organizations rely on sound intelligence, and as their decisions become more data-driven than ever, it’s vital that all of the data they collect reaches the best data warehouse for analytics. Therefore the battle arises between Snowflake vs Redshift.
Both Snowflake and Redshift are two big names in this field, and they both offer similar services. They’re extensive data analytics databases capable of reading and analyzing massive amounts of data. In this post, I will share everything you need to know about Snowflake vs Redshift.
Let’s see the topics of discussion first:
- What is Snowflake?
- How does Snowflake work? – Architecture
- Features of Snowflake
- Disadvantages of Snowflake
- What is Redshift?
- How does Redshift work? – Architecture
- Features of Redshift
- Disadvantages of Redshift
- Difference between Snowflake vs Redshift
- Frequently Asked Questions
- Conclusion
What is Snowflake?
Snowflake is a cloud-based data warehouse that runs on Amazon Web Services or Microsoft Azure. It’s great for enterprises that don’t want to devote resources to the setup, maintenance, and support of in-house servers because there’s no hardware or software to choose, install, configure, or manage. And utilizing any ETL tool, data can be migrated into Snowflake. Snowflake’s design and data exchange capabilities, however, set it distinct. Customers can utilize and pay for storage and computing separately thanks to the Snowflake architecture, allowing storage and computing to scale independently.
How does Snowflake work? – Architecture
Snowflake follows a straightforward, flexible architecture. It separates the storage and computes tasks, so businesses that need a lot of storage but don’t need a lot of CPU cycles, or vice versa, don’t have to pay for an integrated package that includes both. Users can scale up or down according to their needs and only pay for the resources they utilize. Storage is charged in terabytes per month, whereas computation is charged per second.
Source: Snowflake Docs
Snowflake Architecture covers the following three layers.
Database Storage
When data is imported into Snowflake, it is reorganized into a columnar format that is internally optimized and compressed. Snowflake saves the optimized data to the cloud.
Snowflake is in charge of all aspects of data storage, including organization, file size, structure, compression, metadata, statistics, and other aspects of data storage. Customers cannot see or access the data objects stored by Snowflake; they can only access them through SQL query operations executed through Snowflake.
Query Processing
The processing layer is where queries are executed. Snowflake employs “virtual warehouses” to process queries. Each virtual warehouse is an MPP compute cluster made up of numerous compute nodes procured from a cloud provider by Snowflake.
Each virtual warehouse is its own compute cluster, with no other virtual warehouses sharing compute resources. As a result, the performance of one virtual warehouse has no bearing on the performance of others.
Cloud Services
The cloud services layer organizes the entire system and uses ANSI SQL. It reduces the requirement for data warehouse management and tuning to be done manually. This layer contains the following services:
- Authentication
- Infrastructure management
- Metadata management
- Query parsing and optimization
- Access control
Features of Snowflake
Let’s quickly go through the features of Snowflake.
- High Availability and Advanced Security: Snowflake is designed to run constantly and suffer component and network failures with minimal impact on clients.
- Free Data Sharing: Snowflake’s architecture allows users to share data with one another. It also enables businesses to share data with anyone, whether or not they are a Snowflake customer.
- High Performance: Because of the cloud’s elasticity, you can scale up your virtual warehouse to take advantage of more compute resources if you need to load data faster or perform a large number of queries.
- Concurrency: Snowflake’s innovative multicluster architecture addresses concurrency issues: queries from one virtual warehouse never influence queries from another, and each virtual warehouse may scale up or down as needed.
- Support Structured and Semi-structured Data: For analysis, you can aggregate structured and semi-structured data and load it into a cloud database without first converting or transforming it into a set relational schema.
Disadvantages of Snowflake
As we move deeper to know more about Snowflake vs Redshift, let’s have a quick look at the cons of Snowflake.
- Snowflake is the wrong choice if you’re running a firm that relies on on-premise technology that is difficult to combine with cloud-based services.
- When you establish a virtual warehouse, a minute’s worth of Snowflake credits will be spent, but you will be charged by the second after that.
- The SQL editor in Snowflake should also be upgraded to handle autocomplete functions far better than it does today.
What is Redshift?
Amazon Redshift is a cloud-based, fully managed petabyte-scale data warehousing service. Starting with a few hundred gigabytes of data, you may scale up to a petabyte or more. This allows you to gain fresh insights for your company and customers by analyzing your data.
The first step in creating a data warehouse is to set up an Amazon Redshift cluster, which is a collection of machines. You can upload your data set and then run data analysis queries after you’ve provisioned your cluster. Regardless of the size of the data set, Amazon Redshift provides rapid query performance using the same SQL-based tools and business intelligence applications you’re already familiar with.
Check out: Amazon Database Services
How does Redshift work? – Architecture
Let’s try to understand the working of Redshift through its components.
Redshift Cluster: The basic infrastructure component of Redshift is a cluster of nodes. A cluster typically consists of one leader node and several compute nodes. There is no additional leader node when there is only one compute node. Each compute node has its own processor, memory, and storage disc. Client applications are unaware of the existence of compute nodes and never interact with them directly.
The leader node is in charge of all communications with the client apps. The leader node is also in charge of compute node coordination. The leader node is also in charge of query parsing and execution plan development. When the leader node receives a query, it constructs an execution plan and distributes the produced code to the compute nodes. Each compute node is given a piece of the data. The leader node is in charge of aggregating the results in the end.
Users can choose between two types of nodes in Redshift: Dense Storage nodes and Dense Compute nodes. Customers can choose them based on the nature of their needs, whether they are stored or compute-intensive. The number of nodes in Redshift’s cluster can be increased, individual node capacity can be increased, or both.
Features of Redshift
Let’s have a quick look at the features of Redshift.
- Amazon Redshift is a fully managed service that requires very little user participation.
- It has the capacity to store a Petabyte of data.
- Scaling requires very little effort and is only limited by the customer’s financial ability to pay.
- Amazon Redshift is faster than most data warehouse systems, and it has a distinct advantage when it comes to running sophisticated queries repeatedly.
- On-demand pricing is available through Amazon Redshift. This, together with the ability to create clusters from snapshots, can help customers better manage their budgets. It also offers a lot of flexibility when it comes to selecting node types for various workloads.
- It comes with a full set of security features, including lighting.
Disadvantages of Redshift
Now we will have a look at the cons of Redshift.
- This isn’t a good fit for transactional systems.
- While you wait for AWS to release a new patch, you may need to revert to an older version of Redshift.
- The cost of Amazon Redshift Spectrum is determined by the number of bytes scanned.
- Redshift is out of date in terms of features and data types, and the dialect is very similar to PostgreSQL 8.
- Hanging queries in external tables can cause complications.
Difference between Snowflake vs Redshift
Let’s have a sharp look at the difference between Snowflake and Redshift.
Snowflake | Redshift |
Snowflake Elastic Data Warehouse is a cloud-based data storage and analytics service offered by Snowflake Computing. | Amazon Redshift is a cloud-based, fully managed petabyte-scale data warehousing service. |
Snowflake is available on the AWS Marketplace and Microsoft Azure and has some extremely amazing on-demand features. | Redshift should be your first pick if you’re working with the Amazon ecosystem. |
Security and compliance options vary by tier. |
Security and compliance are enforced in a comprehensive fashion for all users. |
More automated database maintenance features. |
Manual maintenance. |
Snowflake offers instant scaling. | Redshift takes minutes to add more nodes. |
Snowflake is a bit more expensive as it charges separately for warehousing and computation. | Amazon’s Redshift is less expensive than Snowflake. |
Check out: AWS Solutions Architect Interview Questions
Frequently Asked Questions
Is Snowflake better than Redshift?
Yes, Snowflake is a much better platform for a beginner. Whereas Redshift is a cost-optimized solution for enterprise-level implementations.
Is Snowflake more expensive than Redshift?
Yes, Snowflake is costlier than Redshift for on-demand use. But as per the usage, major data warehouses players such as Redshift, BigQuery, and Snowflake have different pricing models.
Which one is faster; Redshift or Snowflake?
Redshift takes a little longer time to optimize queries whereas Snowflake offers better performance even with raw queries. Therefore Snowflake is quite faster than Redshift.
Conclusion
The choice between Snowflake vs Redshift will be based on your budget and business needs. For example, if your company is responsible for managing large workloads ranging from millions to billions of dollars, Redshift is the clear winner whereas Snowflake is a modern data warehousing solution designed specifically for today’s data teams and it is better on almost all fronts, and for most businesses than Amazon Redshift.
Related/Reference
- Top 50 AWS Interview Questions
- AWS Certified Solution Architect Associate SAA-C03.
- AWS RDS: Introduction and Tutorial
- Creating AWS Elastic Compute Cloud EC2 Instance
- Snowflake Official Documentation
- AWS Certified DevOps Engineer – Professional DOP-C02 Exam
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