Redshift Vs Snowflake: How to Choose the Right Data Warehouse

By |2022-07-22T06:38:22+00:00March 11th, 2022|

The rising volume of data has been met with a commiserate increase in data storage and computing technologies that can scale effectively without costing businesses an arm and a leg. The decision of building a cloud data warehouse on Redshift vs Snowflake is often complex and involves a number of factors that need to be considered. Cloud data warehouses are seemingly the perfect solution to cater to the increase in overall data volume as they allow businesses to create a unified view and run complex queries on large data sets.

According to a survey by Yellow Brick Data, 75% of the organizations are looking to invest in data warehouses and data lakes for better security, agility, and a streamlined business intelligence process.

If you are in the process of choosing a cloud data warehouse service, we have compared Snowflake Vs. Redshift—two leading cloud data warehouses in the market. Read ahead to find out the merits of both data warehouses and which one is more suited to your organizations’ data analytics needs.

What is Snowflake?

Snowflake is a popular cloud data warehouse, built on Amazon Web Services or Microsoft Azure. What sets Snowflake apart from other options in the market is that you can scale computing and storage separately. This is useful in scenarios when there is a sudden spike in data processing needs. For example, when a company runs a new marketing campaign or during the final round of voting on a popular reality TV show.

You can integrate Snowflake with other analytical tools and backend enterprise applications to perform complex queries on your data. Let’s understand how Snowflake integration works and what its architecture looks like.

Snowflake integration architecture                                                          Snowflake Architecture

The cloud data warehouse comprises of three layers:

  1. Storage Layer: This layer is like a mailroom where all incoming data is stored. It is responsible for organizing and tracking all data. For efficient retrieval, data is stored in micro partitions. Usually, data lies in a cloud storage layer such as Amazon S3 or Azure Blob Storage. The storage layer compresses data and records metadata.
  2. Query processing: This is a computing layer through which you can analyze data by requesting it. This layer has multiple virtual warehouses that are a cluster of computing resources. Each virtual warehouse has a dedicated computing capacity that doesn’t compete with another warehouse. The querying layer also has a cache system, which stores frequently accessed queries.
  3. Cloud services: This is the top layer, which is responsible for coordinating all activities in Snowflake. It supports infrastructure management, metadata management, authentication, access control, and query parsing and optimization.

Benefits of Using Snowflake

Now that you know what goes on behind the scenes and what Snowflake’s architecture looks like, let’s see why you should opt for this data warehouse.

  1. Businesses that see temporary spikes in traffic do not need to invest in hardware, software, or maintenance since Snowflake scales computing separately.
  2. Since Snowflake integration automatically optimizes data storage, you can easily combine structured and unstructured data.
  3. You can create accounts from Snowflake’s interface for seamless data sharing with other Snowflake users or external customers.
  4. The cloud data warehouse is suitable for a business with many users since each virtual warehouse scales up and down independently, without affecting other queries.
  5. Snowflake offers extensive security features such as multi-factor authentication, OAuth, and Federated federated authentication. Plus, you can store data in multiple regions for higher availability.

What is AWS Redshift?

AWS Redshift is a column-based data warehouse that can scale up to petabytes. In a column-based system, data is stored sequentially as compared to a row-based system. The column-based system makes it easier to compress and retrieve data. The data warehouse is optimized for OLAP queries.

Amazon Redshift is based on Postgres SQL, so most SQL-based applications can seamlessly integrate with it. You can also easily integrate it with BI tools, third-party data integration tools, as well as data mining and analytical tools.

AWS Redshift Warehouse Architecture

 AWS Redshift Architecture

When you compare the architecture of Amazon Redshift architecture Vs.with Snowflake’s, there is a considerable difference between the two.

Amazon has a collection of computing resources called nodes, where data is stored. They are organized as clusters, and each cluster runs on an AWS engine. You can have up to 128 nodes. There is a Leader Node that manages all communication with all client programs.

AWS Redshift is super-fast when it comes to querying speeds because of its Massively Parallel Processing design, which ensures clusters can work independently without affecting other clusters’ performance.

You can start small with AWS Redshift with a 160 GB node and then keep on adding nodes to leverage parallel processing.

Benefits of Using AWS Redshift

There are certain merits of using AWS Redshift. Let’s see what makes AWS Redshift stand out from others in the market.

  1. Since Redshift is based on AWS infrastructure, it seamlessly integrates with AWS services. If you don’t want to use AWS services, you can use any third-party tools.
  2. AWS Redshift offers superior performance compared to other options in the market due to MPP technology.
  3. The cloud data warehouse comes with strong security protocols, including access management, SSL encryption for data, column-level access control, and encryption for client-side and server-side data.

Now that you have an idea about both data warehouses. Let’s do a head-on comparison, so you can know which one is suitable for you.

Redshift Vs Snowflake: Pricing

Cost Vs. benefit analysis is the best way to determine the right choice before investing in anything. Both data warehouses offer different pricing structures.

Snowflake works on the pay-as-you-go model. Its pricing is divided into two components: storage and compute. Storage is charged per terabyte and begins at a flat rate of $23/terabyte and accrued per month. Compute pricing starts at $0.00056 per second, per credit, for On-Demand Standard Edition.

Depending on your usage, you can enable any amount of virtual data warehouses for computing. Virtual data warehouses are available in 8 different sizes, and the smallest size costs one credit or $2 per hour. It does not charge you for any idle time.

Redshift vs Snowflake: Snowflake Warehouse Sizes and Credit Usage

Snowflake Virtual Data warehouses sizes

Snowflake’s On-Demand pricing model can be enticing initially, but it can be very unpredictable in the long run, and its cost increases as you go higher.

As compared to Snowflake, Redshift has a simple pricing structure based on redshift clusters. The formula for Redshift On-Demand pricing is:

Amazon Redshift Monthly Cost = [Price Per Hour] x [Cluster Size] x [Hours per Month]

Redshift also offers Reserved Instance Pricing, which allows you to unlock 75 percent savings. In a reserved instance, you pay a pre-defined amount whether a cluster is active or not. You can unlock significant savings with Redshift if you lock yourself with a long-term Reserved Instance.

Redshift Vs Snowflake: Security

The reality of our world is that data is your most important asset and hence, you cannot take any risks when it comes to security.

Both data warehouses take security quite seriously and offer a host of features that ensure your data is always protected.

AWS Redshift offers sign-in credentials, column-level access control, Access Management, clusters encryption, and SSL connections that keep the connection between your client and clusters private. You can also use client-side or server-side encryption to encrypt your data while uploading so it is not vulnerable during transit.

As far as Snowflake is concerned, it offers somewhat similar security features as Redshift. It gives you SCIM to manage user identities and groups. Key Pair Authentication, Multimulti-factor authentication, and OAuth are some features for user authentication. All data stored is encrypted with AES-256 encryption, which is rekeyed periodically. Redshift also offers various security validations for compliance, including Soc 1 Type II and Soc 2 Type II. HIPAA, PCI DSS, HITRUST CSF, FedRAMP Moderate, and IRAP Protected compliance.

Redshift Vs Snowflake: Maintenance

Snowflake is an apparent winner compared to AWS Redshift, when it comes to maintenance because of separate storage and compute, which makes it easier to scale up and down. You can either change a warehouse’s size or increase the number of clusters. The best part about Snowflake is the auto suspend and auto-resume feature, which lets you run the query and once you are done using it, it spends scales? down the warehouse, so you are not charged for it.

In AWS Redshift, queries are sent in a queue. With concurrent scaling, AWS Redshift automatically adds additional capacity in clusters. However, you have to manage which queries are sent to concurrency scaling through WLM queues.

Redshift Vs Snowflake: Data Support

For a long time, Snowflake had an edge over Redshift because of Snowflake’s excellent support for semi-structured data, especially JSON. However, Redshift was quick to catch up, and in 2020 it introduced a new data type called SUPER that supports most semi-structured data, including JSON. SUPER is a generic data type that is schema-less in nature.

It also introduced PartiQL, an extension of SQL that allows easy querying of semi-structured data.

Snowflake and Redshift also support other popular data formats, including XML, AVRO, Parquet, etc.

Redshift Vs Snowflake: Which is the Right Data Warehouse for You?

There is no definitive answer as to what data warehouse you should choose; it all depends on your organization’s data analysis needs and existing infrastructures.

There are certain instances when you can choose one over the other. Let’s see what those are:

When to use AWS Redshift?

AWS Redshift would be a better choice if you already use AWS products since it seamlessly integrates with the AWS ecosystem. With Redshift, you can also leverage AWS analytical tools as Redshift supports native connectivity. Redshift is also better suited in situations when you have humongous data (in petabytes) to scale.

The data warehouse is optimized for OLAP transactions, which means you can do analytical queries on large volumes of data. However, it lacks basic database modification functions such as insert, delete, or update required in OLTP data warehouses. If you are in the e-commerce business, for example, or you want a data warehouse for an airline or hotel booking website, Redshift might not be the best choice.

When to use Snowflake?

If you are not using the AWS ecosystem, then Snowflake can be a viable solution for you. Although Snowflake does not seamlessly integrate with AWS products, it does support various analytical tools such as Power BI and Tableau.

Since storage and compute are separate in Snowflake, it is best in the situations where you get temporary high workloads, so you can increase the compute capacity without increasing storage.

Just like Redshift, Snowflake is also optimized for OLAP transactions.

ETL your Data Faster with Astera Centerprise

The decision of Redshift Vs Snowflake is on you. However, regardless of which cloud data warehouse you choose, Astera Centerprise can help you get started with your data warehouse solution without any hassle. Astera Centerprise is a code-free data integration platform with powerful ETL/ELT capabilities. It can load data from a variety of data sources in your data warehouse.

Astera Centerprise supports native connectivity to Snowflake and AWS Redshift, so you can add them as destinations for your ETL data pipelines within no time. With Astera Centerprise, you can extract and deliver data from various sources, including popular databases, cloud storage, and file formats such as JSON, XML, Delimited to your data warehouse.

The code-free data integration platform also lets you enrich your data with built-in transformations. You can use it for transforming data and validate it to get rid of any redundancies, inaccuracies, and formatting errors before sending it to your destination.

Astera Centerprise can help you ETL your data faster into the data warehouse of your choice, so you can start taking advantage of the scalability, agility, and power offered by these powerhouse platforms.

Download Astera Centerprise today to seamlessly transfer data to your cloud data warehouse without writing a single line of code!

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