In our previous blog post, we unveiled the significant differences between Snowflake & Redshift depending on current data landscape & use cases. It helped decision makers pick the best-of-breed cloud data platform for their business model in on time.
In this blog post, let’s take a deeper look into the technical features of Snowflake & Redshift that is vital for data teams.
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Snowflake is a cloud-engineered data warehouse platform that is built with a unique, hybrid architecture. The platform combines the best features of traditional shared-disk and shared-nothing models. Snowflake organizes the persisted data in a central hub and then processes queries using massively processing (MPP) compute clusters. With this unique architecture of decoupled storage & compute architecture, Snowflake delivers immense benefits such as multi-cluster processing, compute elasticity, and lightning performance for businesses.
Snowflake is a cloud-agnostic data platform that can be hosted on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). No matter whether you have multiple cloud-hosted applications in your organization, you can orchestrate everything in a Snowflake Center of Excellence (COE). Snowflake supports a wide range of data workloads such as data engineering, data science & ML, applications, cyber security, and collaboration that helps organizations to meet modern data needs. Also, Snowflake’s cloud services layer manages.
On top of these features, Snowflake partners with leading data integration, BI, ML, and native programming interfaces and tools to extend its ecosystem as a 360-degree cloud data platform for its business.
AWS Redshift is a homegrown cloud-data platform that has a traditional architecture.
YES! Redshift can be hosted only on Amazon Web Services! Redshift is not a cloud-agnostic data platform. If your organization has hybrid cloud services, this is not an appropriate choice for your COE!
The platform bundles storage and compute resources like legacy data warehouses, thereby limiting the compute elasticity, concurrency scaling, and other features of modern data warehouses. Redshift does offer Massively Parallel Processing (MPP) clusters for query processing, but the tightly coupled architecture resists compute elasticity.
Redshift offers RA3 nodes to switch to a serverless option. However, these nodes only cache the persisted data locally and then allows the compute clusters to perform queries on top of it.
Redshift supports common data workloads such as integration, ingestion, ML, query processing, and other activities. However, as the platform is deeply rooted in AWS, there are limitations in connecting the third-party tool of your choice. Redshift has the privilege of leveraging AWS in-built security features. Thereby, if you’re opting for Redshift, you can utilize the virtual private link, identity & access management, etc. with ease.
Snowflake offers four licensing editions and leverages a time-based pricing model for compute resources. Snowflake bills storage as terabytes per month and compute resources on a per-second basis.
Take a deep look at Snowflake pricing guide.
The compute cost of Snowflake is largely dependent on cluster sizes ranging from XS to 6XL.
Here’s the Snowflake credit chart based on cluster size.
Snowflake offers data-sharing features at zero cost within the same cloud platform & region. As the Snowflake compute clusters are configurable, your data teams have the upper hand over cost & resource optimization. Depending on your data needs and budget, you can seamlessly scale up or scale down your Snowflake data platform.
Redshift has complex pricing structure than any other cloud data platform.
Redshift estimates the cost based on node type, on-demand, upfront purchase, and much more options. The platform offers two node types: DC2 (Tightly couple storage & clusters) and RA3 (Decouples storage & compute).
Let’s look into node based on-demand pricing model.
The consumption of resources is estimated based on RedShift Processing Unit (RPU) as $0.45/hour.
Redshift managed storage (RMS) is expensive than snowflake and is billed at $24/TB. If you choose to utilize AWS S3 for data storage, you need to spend additional costs on purchasing Amazon Spectrum to run queries across S3. However, Redshift offers additional savings on the long-term commitment of one- or three-year upfront purchases.
Both Snowflake & Redshift offers trial versions and discounts for upfront and long-term purchases. So, it’s crucial to seek an expert advice before purchasing these products.
Snowflake’s unique architecture enables users to tune their compute clusters based on their data needs and pay only for what they use. The decoupled storage and compute architecture equips users with auto-scale and auto-suspend features. Users can resize clusters and isolate workloads on demand to enable unlimited concurrent scaling. With Snowflake, users don’t have granular control over node sizes.
As mentioned, Redshift has tightly coupled architecture, and hence it’s reluctant to concurrency scaling and compute elasticity. The resource scalability has vast limitations, and the activity may take anywhere from minutes to hours. Also, the concurrency scaling feature comes with additional charges in Redshift.
Redshift has a long way to go in the aspects of compute elasticity and concurrency scaling to combat Snowflake!
Snowflake wins the competitive edge in the aspects of ease of use & intuitiveness in the marketplace. The built-in SQL console is intuitive and accessible across all the leading browsers such as Chrome, Firefox, Safari, Opera & edge. Snowflake offers a native web interface, Snowsight to render a unified and easy-to-use experience.
Snowflake achieves an average usability rate of 4.6/5 in Gartner product reviews. Snowflake would be the ideal platform for organizations planning to establish a data-driven culture in the near future. Your users can kick-start the data workloads with beginner-level user adoption training and a cognitive data strategy!
AWS Redshift has an optimal user interface that supports data workloads. Users can utilize the Amazon Redshift Console and AWS Command Line Interface (CLI) to manage clusters and process queries. If your organization deals with a huge volume of JSON files, you must opt for Amazon Spectrum to process queries from S3 Bucket at additional costs.
Overall, AWS Redshift secures a score of 4.5/10 for usability in Gartner product reviews.
Snowflake has the upper hand among the leading cloud data platforms in the marketplace. The platform tends to perform queries at an incredible pace (i.e. Approximately 6-7 folds faster than other cloud data warehouses) in a series of TPC-DS benchmark tests.
Snowflake performs the best out of the box without any fine-tuning or operational burdens.
When compared to Snowflake, Redshift breaks in the aspects of performance. The bundled storage and compute clusters hinder the performance of data warehouse, as the clusters compete under heavy workloads. AWS Redshift allows only 15 concurrent queries/cluster and 10 concurrent clusters. Beyond this limit, the platform orchestrates and processes 50 queued queries across clusters and this Workload Management (WLM) are challenging with complex rules.
If your cloud infrastructure is deeply rooted in AWS, then Redshift may work for you. However, Snowflake is also the go-to choice in AWS marketplace by users.
Redshift performs optimally only when sort and distribution keys are planned appropriately. These keys can elevate the operational burden and human capital in the overall costs.
Snowflake is a near-zero maintenance platform that automatically optimizes performance whenever required. The administrators can seamlessly manage user roles, accessibility permissions, resource optimization, security, and governance in Snowflake. Also, they can configure or manually scale up or scale down the resources independently by enabling workload isolation.
With built-in optimization, micro partitions, automated columnar storage, and auto-scalability features, Snowflake offers turnkey solutions and breaks the barriers of operational burdens in the data platforms.
Redshift requires significant efforts to set up, maintain and safe keep the enterprise datasets. The administrators must have extensive knowledge and hands-on experience in sort & distribution keys, WLM, user access, and other platform-specific performance-tuning operations. Your admin team must plan for periodical database vacuuming, resource allocation and optimization, compression, and much more tuning operations. As Redshift enables granular configuration, your admins can resize clusters, nodes, and individual disk & memory space shared between nodes.
If your organization is built up from the ground with AWS ecosystem, you can consider AWS Redshift.
Snowflake renders advanced encryption standards (AES) for both data at rest & in transit. The platform restricts granular column accessibility permissions but does provide accessibility for schemas, tables, views, procedures, & other database objects. Snowflake adheres to SOC1 Type II, SOC2 Type II, SOC3 Type II, HIPAA, PCI DSS, FedRAMP, DSS, and ISO/IEC security compliances.
On top of these standard compliances, Snowflake offers myriad of security features such as
AWS Redshift offers in-built security features of the cloud platform such as AWS Identity and Access Management (IAM) for its users. The user accessibility is based on AWS account privileges and only privileged users can create, delete or resize cluster configurations.
Redshift has comprehensive security features to safe keep an organization’s data.
Being the world’s largest cloud computing platform, AWS adheres to a wide range of data security and governance policies.
Snowflake elevates enterprise data protection with two major features: Time-travel & fail-safe.
Enables accessing historical data (i.e., data that has been changed or deleted) at any point within a defined period. The standard data retention period is 1 day, but the Enterprise & Business Critical customers can configure the time travel up to 90 days.
Offers a 7-day period during which historical data may be recoverable by Snowflake. This period starts immediately after the Time-Travel retention period ends. The fail-safe feature can be leveraged in case of extreme operational failures.
Redshift provides a data recovery feature known as snapshots with manual and automated options. These snapshots are stored in an S3 bucket and when a user wants to retrieve it, a separate cluster is created automatically to process the data.
By default, the automated snapshots are enabled for 8 hours or every 5 GB per node, and the manual snapshots for every 1 day. Redshift users can configure the manual snapshots based on their needs and the data retention extends even after the cluster deletion. In both manual and automated options, snapshots are automatically deleted at the end of retention period and the users have no control to delete them.
Snowflake enables seamless data sharing with customers, third parties, partners, and vendors by leveraging Snowflake data exchange platform. Users can create reader accounts for non-native users and share the Snowflake data objects. Also, Snowflake doesn’t create another data copy and share the objects. Instead, it enables zero-copy clone accessibility which makes real-time streaming & data governance much easier. The data sharing features on the Snowflake are with no extra infrastructure cost. This feature cuts down the additional cost spent on data-sharing tools and other resources.
AWS Redshift encompasses a data share feature that enables users to share across clusters, accounts, or regions. In the aspects of data sharing, redshift doesn’t permit data sharing with non-AWS users.
Snowflake provides excellent customer support in two categories: Premier and Priority. Priority tickets are resolved with the highest importance than premier tickets.
Snowflake holds an extensive online community and resource library to educate and share knowledge. The platform includes course materials, certifications, and workshops for various organizational roles.
AWS, being the world’s largest cloud platform, has step-by-step guides, tutorials, and a training portal with certifications. Hence, organizations opting for Redshift can help users explore the data platform seamlessly.
Still, fingers crossed on Snowflake or AWS Redshift is the prime data warehouse destination for your business.
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Both Snowflake & AWS Redshift offers trial versions to embark on a data-driven culture. Our team can help you find the best cloud data platform that fits your organizational model by considering the existing data architecture, human capital, cloud investments, desired goals and much more. We showcase the benefits and downfalls of each platform with optimal POC.
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