Introduction

Data is the new oil; however virtual it might be, it proves to be the core even for petty businesses to the biggest internet enterprises. Accumulation of data peaked as enterprises aspire to keep track of their data for analytics and record purposes.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

However, in order to keep proper track of these mighty volumes of data, a proper data warehousing solution must be in place. A full data warehouse solution assists users in the accessibility, integrations, and, more importantly, the security front.

This write-up mainly focuses on the best-in-class data warehousing solution, a detailed comparison of Snowflake vs. Redshift vs..

In order to understand the key differences between Snowflake and Redshift, we have to study the pricing, security and integrations, performance, and maintenance requirements.

What is Snowflake?

Snowflake is a SaaS data platform that allows data storage and analysis using a cloud-based model. Introduced in 2012, Snowflake is a cloud based data warehouse, on a three-tier architecture with independent layers of storage, compute, and services.

The platform uses a SQL engine powered by a hybrid database designed specifically for the cloud. Snowflake also offers highly scalable data management solutions through virtual warehousing platforms such as:

• Microsoft Azure
• Google Cloud Platform
• Amazon Web Services

These cloud platforms allow companies to run data analysis queries at scale independently using both structured and semi-structured data. Snowflake is more flexible, fast, and easy to use than traditional data warehousing systems.

But what makes Snowflake unique is its architecture and scaling options. The architecture separates the storage and compute components and allows them to scale independently. You can expand your storage, use more computation, and pay only for what you require.

This makes Snowflake an economical solution for organizations dealing with huge data volumes or initializing data lake for their ETL process. In addition, Snowflake has several other benefits and can be used as data lakes, data marts, and operational data stores.

Benefits of Snowflake Data Warehouse

When compared to other data warehousing platforms, Snowflake offers the following benefits:

• Performance: With independent scaling options, you can expand storage or invest in computing for a short time to maintain consistent speed and performance.

• Hybrid data support: Both structured and semi-structured data are supported by Snowflake without requiring external transformation.

• Cost-effective: Snowflake separates payments for compute and storage, which means you can use a single component without paying for the entire package.

• Multi cluster architecture: Queries are independently processed in each of the three virtual warehouses, which eliminates concurrency issues. Each warehouse can also be scaled separately to align with data requirements.

• Data sharing: Snowflake allows data sharing with other Snowflake users and also with organizations that use other data warehousing solutions.

• Secure: Snowflake offers various levels of data security in the form of encryption. HIPAA compliance and other data protection measures. 

Apart from this, Snowflake also ensures fast and seamless performance through distributed availability zones across the three virtual data warehouses.

While the platform offers several advantages, Snowflake is more suited for large data volumes or complex query resolution. If you are unsure whether Snowflake is the right choice for your business, here are some use cases to simplify the platform's applications.

When to use Snowflake Data Warehouse

Snowflake works best for organizations that have different data storage and processing requirements. You should choose Snowflake if you require:

• Quick and efficient large-scale data storage with minimal query processing.
• Management of turbulent data volume that changes at short intervals of time
• Multiple data warehouses to store different data types
• A data warehousing platform which isn't highly dependent on AWS tools

Overall, Snowflake is an ideal cloud-based data platform for businesses that require unique scaling options for large-scale data management.

What is AWS Redshift?

AWS Redshift is a fully-managed data warehouse platform developed by Amazon. It is a fast and scalable solution designed to process large-scale data requirements involving petabytes with just a few clicks.

Developed in 2012, Redshift started as a part of cloud services offered by AWS. The platform is based on PostgreSQL and uses a columnar database to store data sequentially, making compression and retrieval quicker.

While Redshift is optimized for OLAP queries, its superfast query delivery speed sets it apart from other analytical data warehouses. By leveraging Massively Parallel Processing (MPP), Redshift divides and processes multiple bits of a single query in parallel to speed up the process.

In addition, the platform is powered by the Advanced Query Accelerator (AQUA), which offloads queries and utilizes cache to boost query processing speed.

Due to its powerful query processing capabilities, Redshift is more suited for large-scale data analysis rather than data storage or OLTP applications. The platform can also be used for large-scale data migrations and S3 data analysis queries, offering multiple benefits.

Benefits of AWS Redshift Data Warehouse

AWS Redshift has several advantages compared to traditional data warehouses, especially in analytical applications. Some of its benefits are:

• AWS Integration: As AWS already develops Redshift, it seamlessly integrates with AWS tools such as SageMaker, Athena, DynamoDB, Schema Conversion Tools (SCT), and Kinesis Data Firehose. If your data is stored in an Amazon-powered database, Redshift will considerably simplify the data migration process.

• SQL Query Support: Redshift is based on PostgreSQL, which means it supports all kinds of SQL queries. It is also compatible with all ETL and business intelligence tools, even if Amazon does not develop them.

• Speed: Redshift handles large amounts of query volumes through intelligent storage and resource allocation. Even if the volume fluctuates, the platform maintains its speed and performance.

• Independent scaling: Redshift scales automatically depending on data and query requirements. It adds and removes computational nodes to handle changing data volumes without external intervention. 

While Redshift's analytical capabilities make it an obvious choice for business intelligence requirements, it is more useful for businesses with large data requirements when compared to other databases.

When to use Redshift

AWS Redshift will be highly useful for your business if you:

• Already store data in AWS-powered databases such as Amazon S3 or Athena or use other AWS services.
• Require regular, high-volume query processing in addition to data storage and migration.
• Want minimal monitoring in terms of scaling and infrastructure

Redshift lacks certain basic transactional features, such as insert or delete, which limits its usage for OLTP requirements. However, it is a powerful tool for OLAP processes with 10x processing speed compared to other data warehouses.

Snowflake vs Redshift: A Detailed Comparison

Snowflake vs Redshift Pricing

As Snowflake and Redshift being the major players in cloud data warehousing systems, they both have different pricing models and modules for different plans although Snowflake and Redshift provide offers based on demand and volume.

When it comes to the on-demand pricing, Amazon's Redshift is less expensive than Snowflake. Adding to this, Redshift allows you to save in addition to the on demand rates with their 1 year/3 year reserved instance customer pricing.

Redshift's pricing is based on two factors, the total number of hours and the total number of clusters. There is a standard hourly pricing as per Redshift which is common for all users. But the size of the clusters differ with businesses which happens to be the differentiating factor in the overall pricing. There is Redshift's pricing scale based on the size of clusters, much like a pricing chart based on the cluster size. So, the overall pricing per hour is calculated by multiplying the size of the cluster with the standard pricing per hour.

As far as Snowflake is concerned, the computational process is siloed from the warehousing process which means the pricing is also discrete. Snowflake offers 7 variants of data warehousing options, where the basic package starts from $ 2/hour. As the computational pricing is discrete, the average cost per second for computation is $ 0.00056.

Redshift and Snowflake offer 30% to 70% discounts for users who pre pay for their product.

Snowflake is a bit more expensive than Redshift as it charges separately for warehousing and computation. However, when customers avail reserved instance pricing through Redshift, the total expense margin drops considerably when compared to Snowflake.

Snowflake vs Redshift Security

Data security is the most crucial aspect when it comes to using cloud based data warehouses. In this modern age, with technology growing incessantly, the security systems have been put up with a lot of scrutinising and yet, security breaches happen. This commonly happens when the login credentials are shared over social media to fellow employees or lack of two factor authentication could also pave the way for breaches.

As these data are obtained from various open source platforms, it consists of multiple data output formats and a lot of sensitive information, say, transaction details, customer information, etc. In this modern technological age, the amount of data pulled is far more than the volume of data that is actually secured. This is where data warehouses made the best of void in the market by fitting themselves in with top notch security features.

Snowflake and Redshift grew to be the leaders in cloud based data warehousing systems with their ability to scale data quickly and also in a secure way, let's dive deep into the security features

The sign in credentials for Amazon's Redshift management platform is managed by AWS account credentials as all the features come under Amazon's web services. However, in Snowflake the site access is gained through blacklisting and whitelisting of IP.

With Amazon's Redshift, credentials for cluster encryption for other users are provided by associating cluster security groups with a cluster. Adding to this, data encryption to the user created tables can be enabled while launching the cluster itself.

Snowflake's schema allows you to enable multi-factor authentication and single sign on for parent accounts. But this is not the case when it comes to Amazon's Redshift, the entire operation is handled with AWS's credentials and access management accounts.

Loading data in Redshift comes in two types, server-side encryption and client-side encryption. The decryption process is taken up transparently when you load data from server-side and decrypts the data as it loads the table when you load data encryption is done from the client-side. Data is always on a transit within the AWS cloud and to protect it, Amazon Redshift uses hardware accelerated SSL which helps to copy, backup and restore data.

With Snowflake computing, each and every object in the account is secured, say, the virtual warehouse name, database, clusters, tables, users, etc. The major advantage with Snowflake computing is that it encrypts data automatically that's kept for both loading and unloading.

Snowflake vs Redshift Integrations

Integrations are one key factor users consider before opting for a cloud based data storage or warehousing system. Data is complex, it doesn't come in hand with the use of just one technology to study or visualize your data. This is why integrations play a vital role in data management.

Maintaining your business's data and data management system is essential. However, the process becomes challenging with Redshift as it involves a lot of complexities to be understood and dealt with. Whereas with Snowflake, the process of vacuuming and analyzing becomes easy with its ability to switch data between compute and storage.

If your business works with a lot of Amazon products or services, it would be sensible to build Amazon ecosystem in which the integration can be made easier, say, DynamoDB, Athena, Kinesis Data Firehose, EMR, SageMaker, Glue, Database Migration Service (DMS), CloudWatch Schema Conversion Tools (SCT), etc.

These above mentioned data warehouse architectural systems find it hard to work along with Snowflake when compared to Redshift. However, Snowflake on the other hand provides terrific integration options with Informatica, IBM Cognos, Qlik, Power BI, Apache Spark, Tableau, etc.

However, when businesses hugely rely on JSON storage then Snowflake certainly has an upper hand over Redshift. The in-built architecture and Snowflake schemas allows users to query and store easily whereas with Redshift, spilitage of queries results in strained processes.

Snowflake vs Redshift Maintenance

Maintenance could make a major difference when selecting a cloud data warehouse, if your business doesn't have a dedicated analyst spending hours on the data maintenance operations.

Scaling up and down i.e. switching compute data warehouse or resize with Snowflake can be done in a matter of seconds whereas with Redshift, scaling up and down is hard and takes a lot of time. The reason behind this is that compute and storage are separate, so naturally it doesn't have to copy any data to scale up and down, virtual data warehouse and compute capacity can be switched at will.

After a series of transformations i.e. updates or deletes, Redshift requires the administrator to do the clean ups which is popularly known as vacuuming. This is not the case when it comes to Snowflake, it requires no maintenance of such sort. Redshift's Vacuuming process is well documented in this post.

Snowflake vs Redshift Performance

Although Snowflake and Redshift are the two best performing cloud data warehouses in the market, they do have their own functional differences and matches. They both leverage massive parallel processing which enables computing in a simultaneous manner, columnar storage and keeping up the jobs within a specific timeframe.

But the key difference is that Redshift generally takes a longer time for query optimization but as these queries are run repeatedly and on a daily basis, they tend to be faster. This isn't the case when it comes to Snowflake, it offers a much better performance with raw queries.

Snowflake has always been a tool that performed concurrent scaling, as its computation and storage are different. Amazon Redshift has newly implemented concurrent scaling too. Here's Amazon Redshift's concurrency scaling document for your reference.

Similarities between Snowflake & AWS Redshift

While Snowflake and AWS Redshift have different use cases, they have certain similar features, such as:

• Data processing: Both Snowflake and Redshift are powered by Massively Parallel Processing (MPP) and columnar databases to speed up data processing.

• Scalability: Both data warehouse platforms scale horizontally, allotting more storage and compute resources depending on data requirements.

• Integration: Both platforms seamlessly integrate with other ETL, data processing, and business intelligence tools, even if they are not developed by AWS.

• SQL-based: The platforms are both SQL-based, which means they support standard SQL queries to process and analyze data.

• On-demand pricing: Both Snowflake and Redshift support on-demand pricing. Customers need to only pay for the storage and compute resources they use without making any initial commitments. 

• Security: Snowflake and AWS Redshift both implement advanced security measures such as encryption and ensure regulatory compliance for sensitive data protection.

Have you decided yet? Which is the data warehousing platform for you?

Finding the best data warehousing platform involved a lot of check boxes to be ticked, say, security, integrations, fault tolerant, auto backup, speed, performance, etc. The integrations are based on your ultimatum with the data you possess, whether it's for analytic visualization purpose, data transformation purpose, etc.

Both Snowflake and Redshift provide really good integrations to your data but the decision solely depends on what kind of integration would help your business scale.

Every individual who uses technology for their benefit would be generating 1.7 megabytes of data every second by 2020, which means a total of 40 Zettabytes. This includes internet users who generate 2.5 quintillion everyday. What stops you from having a data management system on your own, click here to visit our site to understand your business and the data it generates.

TL;DR

• Snowflake and Redshift are the two major players when it comes to cloud based data warehousing systems. In pricing, Snowflake is a bit more expensive than Amazon's Redshift but it couldn't be the only form of differentiating factor.
• Security is one aspect which both Snowflake and Redshift have been watchful, which results in their top notch security architecture that couldn't be breached.
• Snowflake has the upperhand when it comes to dealing with JSON storage, not only that, it requires just a minimal amount of maintenance when compared to Redshift.
• Amazon's Redshift is capable of integration with DynamoDB, Athena, Kinesis Data Firehose, EMR, SageMaker, Glue, Database Migration Service (DMS), CloudWatch Schema Conversion Tools (SCT), etc. This builds an ecosystem of Amazon web services which comes in hand with integrations.
• Snowflake doesn't work wonders with the above mentioned integration but it is capable of its own seamless integrations with Informatica, IBM Cognos, Qlik, Power BI, Apache Spark, Tableau, etc.
• Snowflake requires less maintenance when compared to Redshift however the performance capabilities are not very different.
  

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