Over time, more and more organizations are transitioning from legacy platforms to cloud-based platforms. Modern data management is incomplete without data warehouses. These digital repositories of data allow you to collect datasets from multiple sources and provide you with valuable insights about the same. An efficient data warehouse maintains consistency in gathering data from different resources and applies a uniform format to analyze the collected records.
Another important use of a modern data warehouse is that of providing users with quick and comprehensive access to historical records and context regarding the data stored within the system. Cloud-based data warehouses have been helping organizations around the world manage their business records in a flexible and scalable manner.
When it comes to choosing the right data warehouse for your business, two options often stand out from the clutter – Amazon Redshift and Snowflake. Both are cloud-based data warehouses that provide users with a range of features to manage their data efficiently.
Many businesses making their shift to data warehouses find themselves in a dilemma of choosing one of these two alternatives. If you are facing the same issue, let us compare the two data warehouses and help you make the right choice.
What Is Amazon Redshift?
Amazon Redshift is a cloud-based data warehouse that uses compute nodes for storing and analyzing large volumes of data. It is a fully managed petabyte-scale data warehouse that can be readily integrated with some of the best business intelligence tools. By extracting, transforming, and loading data into Redshift, you can obtain key insights into your database and business processes.
The cloud-based data warehouse allows businesses to store a relatively lower volume of data and scale it up over time based on their needs. This makes it easy for you to start storing and managing your data on the cloud. Irrespective of how large the volume of your data is, Redshift provides you with fast query performance with the help of SQL-based tools.
Moreover, the powerful performance of the data warehouse can be credited to the use of its internal networking components. It facilitates seamless and high-speed communication between different nodes via close proximity, custom communication protocols, and high bandwidth connections.
Amazon Redshift is an ideal option for your organization if you already use AWS within your system and your workloads run on structured data.
What Is Snowflake?
Snowflake is an ideal competitor to Amazon Redshift. It is a cloud-based relational database management system with a Software-as-a-Service (SaaS) model. It is an efficient data warehouse built for storing and managing structured and semi-structured data.
One of the biggest benefits of Snowflake is that it is not built on a big data software platform or an existing database. It uses an SQL database engine having a unique architecture designed specifically for the cloud. The Snowflake architecture provides users with the feature of combining shared-disk and shared-nothing models.
With the shared-disk model, Snowflake makes use of a central data store every compute node has access. With the shared-nothing model, it allows every node in the cluster to store a specific portion of the whole database locally.
Moreover, the data warehouse is made up of three distinct layers – database storage, query processing, and cloud services.
With the first layer of database storage, Snowflake looks after the manner in which information is stored within a database. With the second layer, it processes queries with the help of “virtual warehouses.” Here, every virtual warehouse represents a cluster node that does not share compute resources and is independent of other nodes.
Key Similarities Between Redshift And Snowflake
Before looking at the difference between Amazon Redshift and Snowflake, let us have a look at some of the key similarities between the two:
- Both data warehouses support Massive Parallel Processing (MPP) to ensure faster performance
- Both data warehouses allow users to access data with the help of SQL-based query engines
- They connect business intelligence (BI) solutions to databases through column-oriented databases
- Both the platforms are built for abstracting data management tasks, allowing users to obtain valuable insights and improve the overall performance of the system
Redshift Vs Snowflake: A Detailed Comparison
Here are a few important parameters using which we can compare Amazon Redshift and Snowflake:
Pricing always plays an important role for SMEs while implementing suitable data warehouses. When it comes to the price to be paid for the platform, Redshift is cheaper than Snowflake for on-demand use of the data warehouse.
Amazon Redshift charges users on a per-hour per-node basis, including computational power as well as data storage. If you want to calculate the amount paid for using Redshift on a monthly basis, you can multiply the size of the concerned cluster and the number of hours spent in a month with the price per hour.
The pricing model of Snowflake is based on usage patterns. As it decouples data storage from computational warehouses, both aspects are billed separately. The dynamic pricing model of Snowflake helps users save money when there is a reduction in the query load.
When it comes to database management, the performance of Redshift and Snowflake is more or less similar. However, businesses prefer Snowflake more on the basis of this parameter as it makes it easy for users to share data between multiple accounts.
If you are willing to share your valuable data with a concerned party (let’s say customers), Snowflake allows you to do so without the need to copy any of your datasets. This makes database management highly efficient in the case of using Snowflake.
Unfortunately, Redshift does not offer such support to its users. In fact, the data warehouse does not support semi-structured data types like Object, Array, and Variant.
Platform maintenance in the case of Amazon Redshift can get a little more complicated in comparison to Snowflake. It requires you to use WLM queues for managing the platform and the data therein. This can prove to be fairly challenging for new and non-technical users as it involves a complicated set of rules.
With Snowflake, users do not face such roadblocks. It allows you to start different data warehouses for looking at the same data without the need for copying it. This makes it easier for you to allocate specific datasets to different tasks and users.
Data security, privacy, and compliance play a very important role when it comes to implementing a data warehouse within your organization. Especially if you operate in sectors like finance, law, and healthcare, you cannot afford to compromise the security of your records at any cost.
Both Redshift and Snowflake provide users with enhanced security features to protect their datasets. However, you may need to check the Snowflake edition you have been using to obtain specific security features as not all versions of the data warehouse offer all features.
Here are the key data security features offered by Amazon Redshift:
- Access Management – Redshift allows users to define AWS Identity and Access Management accounts to have control over specific resources.
- Sign-in Credentials – AWS account privileges allow users to control access to the Redshift Management Controls through secure sign-in credentials.
- Virtual Private Cloud (VPC) – Redshift users can launch specific clusters in the Amazon Virtual Private Cloud (VPC) for protecting access to the same.
- Cluster Security Groups – Users can define a cluster security group and associate the same with a specific cluster to obtain inbound access to the concerned Redshift cluster.
- SSL Encryption – Amazon Redshift allows users to use secure sockets layer (SSL) encryption to secure the connection between a cluster and their SQL client.
- Cluster Encryption – While launching a cluster in Amazon Redshift, users can enable cluster encryption to encrypt the records stored in user-generated tables.
- Security For Data in Transit – To protect your data in transit, Amazon Redshift provides you with SSL accelerated with hardware for communicating with Amazon DynamoDB or Amazon S3.
- Data Compliance – Amazon Redshift comes with a range of different data compliance certifications to help users adhere to regulations like GDPR, CCPA, and more.
Here are the key data security features offered by Snowflake:
- User Authentication – Snowflake uses multi-factor authentication to provide users with enhanced security and seamless support for single sign-on (SSO) via federated authentication.
- Secure Site Access – The data warehouse controls site access through secure practices of IP whitelisting and blacklisting. These practices are managed via network policies while facilitating private communication between Snowflake and other VPCs with AWS PrivateLink.
- Object Security Via DAC – Snowflake allows users to control access to all objects in the system using DAC (discretionary access control) and RBAC (role-based access control).
- Data Compliance – The data warehouse ensures seamless compliance to PCI DSS, Soc 2 Type II, and HIPAA.
Amazon Redshift Vs Snowflake: The Final Word
Based on the aspects discussed above, it can be concluded that both Redshift and Snowflake provide their users with different features. Bothe cloud-based data warehouses have their own share of pros and cons that need to be assessed based on your needs and preferences. If you are in a dilemma of choosing an ideal data warehouse for your organization, make sure you analyze your business needs well.