Snowflake or Redshift, which cloud data warehousing is more fitting for your business
Data is everywhere, starting from a mid scale departmental store to the biggest internet companies. Data has grown to be an obligatory factor for any business that aspires to keep track of their data for analytic or record purposes.
In this day and age, businesses are collecting a large volume of customer data in numerous segments. However, in order to store large volumes of data, a proper data warehouse needs to be in place to help with the seamless flow in operations.
This write up mainly focuses on the best in class data warehousing solution, a detailed comparison on Snowflake vs Redshift.
Snowflake and Redshift are the two leading data warehousing systems in the market. Their ability to process such volumes of data quickly without compromising the quality sets them apart in the market.
Snowflake is a cloud based data warehousing system for structured and semi-structured data, this data warehouse doesn’t actually rely on technologies like hadoop. It uses cloud based data storage and analytics service called Snowflake elastic whereas Redshift uses business intelligence in its cloud based data warehouse system. Redshift is easily scalable, say, the process can be started off with gigabytes of data and can be scaled easily to petabytes of data as per needs.
In order to understand the differences between Snowflake and Redshift, we have to study the pricing, security and the integrations they offer.
As Snowflake and Redshift being the major players in cloud data warehousing system, they both have different pricing 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.
Data security is the most crucial aspect when it comes to 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 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 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 other user is 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 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 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.
In Snowflake, each and every object in the account is secured, say, warehouse, database, clusters, tables, users, etc. The major advantage with Snowflake is that it encrypts data automatically that’s kept for both loading and unloading.
Integrations are one key factor users consider before opting a data 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.
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.
Have you decided yet? Which is the data warehousing platform for you?
Data is the key component that allows you to read your business. However, these valuable data must be secured and kept in a state where they are very much availed for service. That’s why the data warehouses broke into the scene.
Finding the best data warehousing platform involved a lot of check boxes to be ticked, say, security, integrations, fault tolerant, auto backup, speed, 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 provides really good integrations to your data but the decision solely depends on what kind of integration would help your business scale.
According to Statista, the global big data and business analytics market was valued at in 168.8 billion U.S. dollars in 2018 and is forecast to grow to 274.3 billion U.S. dollars by 2022, with a five-year compound annual growth rate (CAGR) of 13.2 percent. 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.
- Snowflake and Redshift are the two major players when it comes to cloud based data warehousing systems.
- When it comes to 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.
- 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.