Data is the new commodity in today’s tech-driven world. With the increasing dependencies of the world on data, it proves to be the fundamental asset for small and mid-sized businesses to the big enterprises. Dependence upon data increased as enterprises started tracking records of their data for analytics and decision-making objectives.
The international big data market is predicted to grow to 103 billion U.S. dollars by 2027 with a share of 45 percent, and the software segment will occupy a notable big data market volume by 2027.
However, to keep a managed record of these overwhelming volumes of data, a proper data warehousing solution must be adapted. A data warehouse helps users in the accessibility, integrations, and more critically on the security aspect. This blog post focuses on the discussion of state-of-the-art data warehousing solutions and their detailed comparison, i.e.,
Snowflake vs. Redshift. To understand the differences between Snowflake and Redshift, we will go through some key aspects of both platforms.
What is Redshift?
Redshift can be considered a highly managed, cloud-based data warehouse service seamlessly integrated with various business intelligence (BI) tools. The only thing left is Extract, Transform, Load - ETL process to load data into the warehouse and start making informed business decisions.Amazon makes it easier for you to initiate with a few hundred gigabytes of data and scale up or down the capacity as per your requirements. It enables businesses to enjoy the perks of their data to get fruitful business insights about themselves or their customers.
If you want to launch your cloud warehouse, you have to launch a set of nodes known as a Redshift cluster. Once you have triggered the cluster, data sets can be loaded to run different data analysis operations. Irrespective of the size of your data set, you can leverage upon fast query performance by using the same SQL-based tools and BI utilities.
What is Snowflake?
Like Redshift, Snowflake is another powerful and renowned relational database management system -RDBMS. It’s introduced as an analytic data warehouse to support structured and semi-structured data that follows a Software-as-a-Service (SaaS) infrastructure.
This means it’s not set up on an existing database or a big data platform (like Hadoop). Instead, Snowflake serves as an SQL database engine with a unique infrastructure specifically developed to offer cloud services.This data and analytics solution is also quick, interactive, and offers more scalability than conventional data warehouses.
Redshift vs Snowflake - Comparison
If you have used both Redshift ETL and Snowflake ETL, you’ll probably be aware of several similarities between the two platforms. However, there are additional unique capabilities and other functionalities that each platform offers differently.Suppose you’re gearing up to run your data analytics operations entirely on the cloud. In that case, the similarities between these two state-of-the-art cloud data warehousing platforms are far more than their differences.
Snowflake offers cloud-based storage and analytics in the form of the Snowflake Scalable Data Warehouse. In this case, users can analyze and store data on cloud media.Next, data will be stored in Amazon S3. If you’re using Snowflake ETL, you can benefit from the public cloud environment without any need to integrate utilities like Hadoop.These cloud warehouse infrastructures are powerful and provide some unique features for handling overwhelming amounts of data.To choose a suitable solution for your company, one must compare integrations, features, maintenance, security, and costs.
Snowflake vs Redshift: Integration and Performance
If your business is already based on AWS, then Redshift might seem like the smart choice. However, you can also opt for Snowflake on the AWS Marketplace with on-demand utilities. If you’re already using AWS services like Athena, Database Migration Service (DMS), DynamoDB, CloudWatch, Kinesis Data Firehose, etc., Redshift shows promising compatibility with all these extensions and utilities. However, if you’re planning to use Snowflake, you need to note that it doesn’t support the same integrations as Redshift. This, in turn, will make it complex to integrate the data warehouse with services like Athena and Glue. However, Snowflake is compatible with other platforms like Apache Spark, IBM Cognos, Qlik, Tableau, etc. As a result, you can conclude that both platforms are just about even equally useful and workable. While Redshift is the more defined solution, Snowflake has completed notable miles over the last couple of years.
Snowflake vs Redshift: Database Features
Snowflake makes it simpler to share data between different accounts. So if you want to share data, for instance, with your customers, you can share it without any need to copy any of the data.This is a very smart approach to working with third-party data. But at the moment, Redshift doesn’t provide such functionality. Redshift is not compatible with semi-structured data types like Array, Object, and Variant. But Snowflake is.When it comes to handling String data types, Redshift Varchar limits data types to 65535 characters. You also have to opt from the column length ahead.On the other hand, the String range in Snowflake is limited to 16MB, and the default size is the maximum String size. As a result, you don’t have to know the String size at the start of the exercise.
Snowflake vs Redshift: Maintenance
With Amazon’s Redshift, users are encouraged to look at the same cluster and compete over on-desk resources. You have to utilize WLM queues to handle it, and it can be much complex if you consider the complex set of rules that must be acknowledged and managed. Snowflake is free from this trouble. You can easily initiate different data warehouses (of various sizes) to look at the same data without any need to copy it, and multiple copies of the same data can be distributed to different users and tasks in the simplest way possible. If we talk about Vacuuming and Analyzing the tables on regular basic copying, Snowflake ensures a turnkey solution. With Redshift, it can become troublesome as it can be an overwhelming task to scale up or down. Redshift Resize operations can also become extremely expensive suddenly and lead to notable downtime. This is not the case with Snowflake due to the separate compute and storage domains, and you don’t have to copy data to scale up or down. You can just switch data compute capacity whenever required.
Snowflake vs Redshift: Security
For any big data project, security is the core of all aspects. However, it can be difficult to maintain consistency as every new data source can likely make your cloud vulnerable to evolving threats. It can generate a gap between the data generated and the data that’s being secured. When it comes to security measures, it’s not a race between Snowflake and Redshift, as both platforms provide enhanced security. However, Redshift also provides tools and utilities to handle Access management, Amazon Virtual Private Cloud, Cluster encryption, Cluster security groups, Data in transition, Load data encryption, Log-in credentials, and Secured Socket List - SSL connections. Snowflake also provides similar tools and utilities to incorporate security and regulatory compliance. But you have to be conscious while the edition as features aren’t available across all its variants.
Snowflake vs Redshift: Costs
Both Snowflake ETL and Redshift ETL have very contrasting pricing structures. If you take a deeper look, you’ll get to know that Redshift is less expensive when it comes to on-demand pricing. Both solutions provide 30% to 70% discounts for businesses who choose prepaid plans.With a one-year or three-year Reserved Instance (RI) price model, you can access additional features that you can miss out on a standard on-demand pricing model.
Redshift charges customers based on a per-hour per-node basis, and you can calculate your monthly billing amount using the following formula:
Redshift Monthly Cost = [Price Per Hour] x [Cluster Size] x [Hours per Month]
Snowflake’s price is heavily dependent on your monthly usage. This is because each bill is generated at hour granularity for each virtual data warehouse. In addition to that, data storage costs are also separate from computational costs.For instance, storage costs on Snowflake can start at an average compressed amount at a fixed rate of $23 per terabyte. It will be summed up daily and billed each month. But compute costs will be around $0.00056 per second or credit on Snowflake’s On-Demand Standard Edition.However, it can quickly become troublesome because Snowflake offers seven tiers of computational warehouses, with the most basic cluster costing one credit or $2 per hour.
The resultant bill is likely to double as you go up a level.In simple words, if you want to play safe, then Redshift is a less expensive option for you as compared to Snowflake on-demand pricing. But to leverage from notable savings, you’ll have to register for their one or three-year RI.
Snowflake vs Redshift: Pros & Cons
Amazon Redshift Pros
Amazon Redshift is very interactive user-friendly.
It also requires less administration and control. For instance, all you have to do is create a cluster, choose a type of instance, and then manage to scale.
It can be easily integrated with a variety of AWS services
If your data is stored on Amazon S3, Spectrum can easily run difficult queries. You just have to enable scaling of the compute and storage independently.
It’s highly favorable for aggregating/denormalizing data in a reporting environment.
It provides very fast query execution for analytics and enables concurrent analysis.
It provides a variety of data output formats, including JSON.
Developers with an SQL background can enjoy the perks of PostgreSQL syntax and work with the data feasibly.
On-demand reserved instance price structure covers both compute power and data storage, per hour and per node.
In addition to improved database security capabilities, Amazon also has a wide array of integrated compliance models.
Offers safe, simple, and reliable backups options
Amazon Redshift Cons
Not suitable for transactional systems.
Sometimes you have to roll back to an old version of Redshift while you wait for AWS to launch a new service pack.
Amazon Redshift Spectrum will cost extra, based on the bytes scanned.
Redshift lacks modern features and data types.
There can be complexities with hanging queries in external tables.
To ensure the integrity of transformed tables, you’ll also have to rely on passive mediums.
Snowflake is suitable for enterprise-level businesses that operate mainly on the cloud.
This data warehouse platform is extremely user-friendly and compatible with most other services.
Its SQL interface is highly intuitive.
Integration is simple because Snowflake itself is a cloud-based data warehouse.
Easy to adapt and launch.
Supports a wide array of third-party services and utilities.
SaaS can be integrated with cloud services, data storage, and query processing.
Data storage and compute pricing will be based on different tier and cloud providers and charged separately.
Enable secure views and secure user-defined functions.
Account-to-account data transfer can be done via database tables.
Integrates easily with Amazon AWS.
Snowflake is not recommended if you’re running a business using on-premise infrastructure that doesn’t easily support cloud services.
A minute’s worth of Snowflake credits will also be consumed whenever you enter a virtual warehouse but charged by the second after that.
There’s much room for improvement as Snowflake’s SQL editor needs to be upgraded to handle automated functions.
The choice between Redshift and Snowflake depends upon your usage and specific business requirements. For instance, if your organization manages overwhelming workloads ranging from the millions to billions, the obvious option here is Redshift. While their model is cost-effective, companies also can reduce their expenses by opting for query speeds at a lower price value for daily active clusters. As Redshift is a renowned Amazon product, there’s also comprehensive documentation and support that can help your employees deal with any potential problem. However, the bottom line is that your data warehouse decision has to be made based on your daily usage and the amount of data you will deal with.
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