Redshift vs. Snowflake for Data warehousing: Which One is For You?

As the name suggests, a data warehouse is like a warehouse but for data that holds and maintains all your company’s data until you need it. In addition to storage purposes, a data warehouse also analyzes your data. It makes everything centrally located – allowing you to quickly retrieve whatever you require to make critical business decisions or gain insights. How do you know which data warehouse best supports your data? Will it be Amazon Redshift or Snowflake?

To help you decide, we will be talking about the specifics of both products, so continue reading the article below to find out which data warehouse program is the best for you. However, before we compare them head-to-head, let us provide a brief overview of each tool, so you know what we are talking about. Let’s start!

Amazon Redshift

Understanding the specifics of each data warehouse before you compare both is important so you don’t miss any of the details. Starting off with the Amazon redshift, let’s learn why companies use this program and what it has in store for you.

Amazon Redshift is a cloud-based data warehouse usually used to store copious amounts of data. When we say copious amounts of data, we talk about exabytes! It’s a part of the Amazon Web Services developed in 2012, and since then, it has made the processing of both structured and unstructured data almost effortless. Moreover, it has even made the migrations of databases easier.

Some Benefits

Not only does this program compile all your company’s data in one place, but it also makes sure that your data is up to date so that when you must make essential decisions in a rush, you can trust the information you have. It does this by conducting a log analysis of your data. Redshift also provides you with the tools you need to create interactive reports without risking the security of your data.

Snowflake

Now, let’s look into the benefits of Snowflake. Although both are data warehouses and provide similar services, both have qualities that make one dissimilar from the other.

Just like Redshift, Snowflake is also a cloud-based platform. Once you become part of this, you no longer need a separate data mart or lake because you can safely save and share all your structured and semi-structured data through this one platform.

Some Benefits

Snowflake ensures that its users don’t have to worry about the file management process and that people can work on individual files without hassle. Since it is a cloud-based platform, it performs all its functions at high speed, making it more flexible than other data warehouses.

Which One of These Two is For You?

Here comes the tricky part; how do you decide which one is better for your use case? Well, from their brief descriptions, it may seem like they’re both pretty much alike; let’s look at the features that make them different.

1. Security:

Redshift keeps your data secure by providing multiple levels of security, such as access control, encryption, and a virtual private cloud. Moreover, redshift backs your data up regularly on its own so that your data is safe in case of system failure. Snowflake won’t let you down when it comes to security because it provides end-to-end encryption, site access control, and highly sophisticated security tools such as a bug program to ensure the safety of your data.

2. Cost:

Redshift calculates the cost by seeing how many Dense Compute and Dense Storage nodes you’re using, but they can also provide you with discounts and different packages in case of a reserved instance. On the other hand, snowflake computes cost by adding up the number of Snowflake credits you use to perform tasks such as loading and unloading your data.

3. Maintenance and Management:

Redshift divide your data into nodes and those nodes into slices, and when it comes to scalability, snowflake gives you results almost instantaneously. Moreover, redshift requires you to “vacuum” your data timely, whereas snowflake users don’t need to go through this. Handling Redshift without an expert in AWS architecture also becomes difficult, but the snowflake interface is entirely user-friendly.

4. Setup

In terms of structure, redshift has an OLAP-style column-oriented platform and quickly responds to SQL queries thanks to Massively Parallel Processing (MPP) even when you’re dealing with a vast amount of data. Redshift also makes finding insights from your data easier with the help of its Machine Learning techniques. It also reduces the amount of space your data uses by compressing it when it’s stored and decompressing it when you need it.

Snowflake uses a central data repository so that it is easy for you to access your data from every device, and just like redshift, it also uses MPP to process queries. Since snowflake is a lot more flexible, it doesn’t have to perfect traditional ETL functions. Redshift mainly supports JSON fundamentals, but snowflake can support not only JSON, but XML, Avro, and Parquet as well.

Wrap Up

Since the amount of big data keeps increasing every day, the need for a data warehouse has become an absolute necessity for every company. Data warehouses keep all your data in one place, consistent, and ready for use so you can make decisions much faster without requiring a lot of technical knowledge.

To choose between Amazon Redshift or Snowflake, you need to first understand the needs of your data and which one of these programs fulfills those requirements. Once you’ve got clarity about your requirements and priorities, choosing the right one will become a breeze.

Let’s sum it all up in a tabular form so you’ll have something to take back:

AWS Redshift	Snowflake
Site access control, virtual private cloud, encryption	Site access control, bug program, end-to-end encryption
Cost based on dense compute and storage nodes in use	Cost based on snowflake credits used
Slow scalability	Instantaneous scalability
Requires expertise	User-friendly
Data cleaning required regularly	Keeps your data updated & cleaned
MPP allows redshift to answer queries quickly	Also uses MPP to answer queries quickly
Supports JSON	Supports JSON, XML, Avro, and Parquet

With that said, the journey of developing a data warehousing solution from scratch is far longer than this, even for well-established businesses. There are plenty of things to be considered, even after you’ve made your final call regarding the tool you want to go ahead with. Managing the data ingestion streams, data pipelines, and data integration services take more effort and workforce than you think.

But you don’t need to worry! There are many vendors out there that offer support in helping you settle the ecosystems for data warehouses. Among these, one of the most highly recommended is Algoscale – It’s taking major strides in the industry with its unparalleled data pipelines and data integration services. Not only do they offer a complete suite of data services, but they also help their customers choose the best tool, depending upon the specific use case, and they’re pretty economical as well. So, don’t forget to pay them a visit if you’re interested.

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