Snowflake vs. Redshift: 2023 Mega Comparison Guide

Snowflake is a good option for SQL-based business intelligence and analytics tasks because it has a unique architecture that allows for unlimited simultaneous queries. Meanwhile, Redshift has a massively parallel processing (MPP) architecture that is well-suited for managing larger workloads while maintaining cost-effectiveness.

	Snowflake	Redshift
Architecture	Cloud-native, multi-cluster	Traditional, single-cluster
Serverless	Yes	No
Scalability	Automatic scaling and elasticity	Manual scaling
Concurrency scaling	Unlimited concurrent queries	Limited concurrent queries
Pricing	Pay-per-use model	On-demand pricing and the reserved instance
Data loading	Parallel, optimized for cloud	Parallel, optimized for on-premises
Data Sharing	Native, secure data sharing	Limited data-sharing capabilities
Query performance	Optimized for complex queries	Optimized for simple queries
Workload location	Separate compute and storage layers	Shared compute and storage resources
Backup and recovery	Continuous, automated backups	Manual backups and snapshots
Ecosystem	Limited third-party integrations	A rich ecosystem of integrations
Data formats	Supports semi-structured data types	Limited support for semi-structured data types
Security	Granular access control and encryption	Robust security features
Maintenance	Fully managed cloud provider	Requires manual maintenance
Data size limits	Virtually unlimited	Maximum of 1 PB per cluster

Snowflake Overview & Features

Snowflake is a SaaS data warehousing solution that stands out with its unique multi-cluster shared data architecture. This architecture allows for unlimited concurrency and simultaneous query execution. Its common use cases are data management, real-time data analytics, and storage of structured and semi-structured data.

Fully managed service

When using Snowflake, you don't have to worry about deploying or managing any infrastructure as it is a fully-managed cloud-based data warehouse service.

Elasticity

Elasticity refers to the ability to instantly resize computational resources up or down as needed. Snowflake separates computing power from storage. Therefore, it can scale its compute resources up or down without impacting the storage capacity.

Performance

Due to multi-cluster shared data architecture, Snowflake offers high performance. It can easily handle large datasets and complex queries.

Security

Snowflake supports always-on, enterprise-grade encryption for data at rest and in transit. It also provides role-based access control for secure data handling and compliance.

Cost-effectiveness

For companies of all sizes, Snowflake is a cost-effective data warehouse solution. There are no up-front charges or long-term commitments. You can simply pay for the resources you utilize.

Collaboration

With Secure Data Sharing, Snowflake allows you to share live, ready-to-query data with others. Also, the workspaces feature lets you have different virtual warehouses for different teams. Thus, different teams can work simultaneously without interfering with each other's workloads.

Time Travel

The "Time Travel" feature allows users to access historical data versions for a defined period. You will need it when recovering from accidental data deletions or modifications.

Snowflake Pros & Cons

Concurrent and isolated processing

Snowflake allows simultaneous query execution by multiple users without impacting performance. It helps to maintain efficient analytics workflows.

High processing power

A benchmark test showed that Snowflake can quickly handle a lot of data - from 6 to 60 million rows in just 2 to 10 seconds.

Simplified data sharing

Snowflake makes it easier to share data both inside and outside of businesses. It permits secure, monitored data sharing with third parties or other Snowflake accounts.

Ease of use

With its intuitive interface and SQL-based query language, Snowflake lets users focus on deriving insights from data analysis.

Data lake integration

As you can integrate Snowflake with data lakes, there is no need for data duplication, streamlining data management, and analysis.

Security and Compliance

Snowflake ensures data protection and adheres to industry standards and regulations.

Vendor lock-in

Once you start using Snowflake, switching to another platform for data or apps can be challenging.

Limited data types

The usability of Snowflake for certain applications may be constrained by the fact that it does not handle all data types.

No in-memory analytics

Its performance may be constrained for specific workloads because Snowflake does not offer in-memory analytics.

Lack of integrated machine learning

Snowflake lacks integrated machine learning features. Enterprises may need to employ third-party technologies.

High cost

Compared to certain other virtual warehouses, Snowflake can be more expensive.

Redshift Overview & Top Features

AWS Redshift is a fully managed, cloud-based data warehousing solution provided by AWS. It offers scalability and high performance for analyzing large datasets, with the ability to handle petabyte-scale data.

With its columnar storage format and parallel processing capabilities, Redshift allows data engineers to execute complex analytical queries quickly. Finally, its integration with the AWS ecosystem provides you with a comprehensive set of tools and services.

Spectrum Integration

Redshift uses the Spectrum function from AWS services to let you directly access and query data stored in Amazon S3. You don't need to copy or move data into Redshift first.

Effective Data Compression

Redshift supports a number of compression methods such as Zstandard, LZO, and gzip.

Robust data encryption

Redshift offers encryption at many levels. For safe data-at-rest encryption, it makes use of the Key Management Service (KMS) provided by Amazon Web Services.

Flexible Data Distribution

Redshift provides a variety of data distribution models. This allows users to enhance query performance across compute nodes.

Data Replication and Availability

By replicating data across different Availability Zones, Redshift achieves high availability.

Fine-Grained Access Control

By integrating with AWS Identity and Access Management, they make it possible to manage user permissions at various system layers with granular access control.

Redshift Pros

• Scalability. Redshift allows enterprises to easily expand their data warehouse as their needs change. It can handle petabyte-scale data with its massive parallel processing architecture.

• Fast Query Performance. Redshift provides quick response times for complex analytical queries written in standard SQL. By using query optimization and parallel processing techniques, Redshift provides great performance even with big datasets.

• Seamless integration with the AWS ecosystem. Redshift integrates well with AWS Glue, Amazon QuickSight, AWS Data Pipeline, and AWS CloudFormation.

• Cost-effective. Redshift follows a pay-as-you-go pricing model, allowing businesses to pay only for the resources they actually use. Redshift has cheaper operating costs than more established data warehousing options.

• Data Security. Redshift offers various data security capabilities and interfaces with AWS IAM.

• High fault tolerance and availability. Redshift replicates data across different Availability Zones. It takes care of data replication and node recovery automatically. This lowers the possibility of data loss or service outages.

Redshift Cons

• Costs associated with storage. Redshift has charged for both computation and storage resources, while other solutions only charge for computation. This can make Redshift more expensive compared to those alternatives.

• Data transfer costs. Redshift charges for data transfers both into and out of the cloud. If you're sending a lot of data, this can add up.

• Cluster setup. Before you can use Redshift, a cluster needs to be set up. If you're not familiar with Redshift, this process could take some time.

• Cluster maintenance. Redshift cluster maintenance is a recurring requirement for tasks like data vacuuming. Businesses that lack the capacity to run their own clusters may find this to be a hardship.

• Data security. Redshift is a cloud-based platform, therefore it's crucial to take precautions to protect your data. You will have to employ role-based access control, encryption, authentication, and other security practices.

• Data governance. Redshift doesn't come with any built-in data governance features. This indicates that companies must put their own data governance policies and procedures in place.

• Performance. Redshift's performance can vary based on the type of query being executed. This is because Redshift uses a columnar storage format, which may result in inefficiencies when queries need to access multiple columns simultaneously.

Best ETL tools for BigQuery and Redshift

The use of ETL tools is essential for data integration. They gather data from various sources, convert it into a format that can be used, and then feed it into the desired data warehouse.

The following ETL tools comparison discusses the best Snowflake ETL tools and Redshift ETL tools.

1. Portable

Portable is one of the best cloud-based ETL solutions.

It helps you move data between different data warehouses and platforms. You can use it as a powerful tool to strengthen your data analysis and automate your data pipelines.

Key features

• Ensures interoperability with varied data sources by supporting more than 500 connectors. Like, long-tail connectors.

• User-friendly UI for quick adoption and easy onboarding.

• Highly trustworthy, and well-tested across a range of platforms and data sources.

• Connects to well-known data sources like Amazon S3, BigQuery, Redshift, and Snowflake.

• Uses industry-accepted security techniques to protect data.

• Data visualization is made possible by integration with top BI tools like Tableau and Power BI.

2. Hevo

Hevo is a fully-managed ETL data integration platform.

It makes it simple for businesses to gather data from many sources, clean it up, and convert it before putting it into a data warehouse or data lake. Hevo is made to be simple to use, even for companies without any prior knowledge of data integration.

Key features

• Hevo has a drag-and-drop interface that makes building and managing data pipelines simple.

• It supports a wide variety of data sources such as on-premises databases, cloud-based applications, and SaaS apps.

• Data can be loaded into a number of data lakes and warehouses, including Amazon Redshift, Google BigQuery, Snowflake ELT, and Amazon S3.

• Provides a pay-as-you-go pricing structure so that companies only pay for the data they really use. You can also try out their free trial before committing to a premium subscription.

• Reduce the time it takes for organizations to derive insights from their data. This is so that organizations can save a ton of time and work since Hevo automates the data integration procedure.

You could consider Hevo alternatives and their key differences like Portable vs Airbyte to choose the best.

3. Stitch

Stitch is a robust cloud-based data integration platform. It simplifies the process of collecting and consolidating data from various sources. It allows businesses to create data pipelines and easily load data into the locations they desire for analysis and insights.

Key features

• Supports a wide range of data sources, such as databases, SaaS programs, file storage, and others.

• Eliminates the need for manual data transfers by automating the extraction and loading of data. It ensures that there is always access to the most recent information.

• Able to scale easily as data-driven needs increase and handle massive data volumes. It makes use of the cloud's capacity to deliver dependable performance and successfully manage rising data loads.

• Capabilities for alerting and monitoring to keep tabs on the performance and health of data pipelines.

• You can easily integrate popular BI tools such as Amazon Redshift, Google BigQuery, and Tableau.

4. Talend

Talend is a comprehensive data integration platform with powerful ETL features. It has a broad range of features available in both open-source and premium editions.

Key features

• For a number of databases, cloud platforms, and data sources, Talend provides a wide range of ETL connections. You can easily integrate popular Redshift ETL tools and BigQuery ELT tools with Talend.

• Data profiling, data cleansing, and data quality checks are all supported by its technologies. This ensures that the data on the target platform is of high caliber.

• The ETL process may involve complex data enrichment and manipulation.

• Talend's high-performance data processing tools ensure efficient ETL procedures even for big data.

5. Matillion

Matillion is a powerful cloud-native data integration platform. With Matillion, organizations can extract, transform, and load data into their cloud services. It facilitates easy interaction with a variety of data sources and analytics systems. They offer a user-friendly environment for creating data pipelines.

Key features

• Specifically created for cloud systems. Matillion uses the cloud to compute resources to effectively complete data integration tasks.

• Provides orchestration tools for planning and controlling the execution of intricate data activities.

• Use parallel computing to effectively manage big amounts of data. It automatically scales to meet shifting demands for data processing.

• Offers robust data governance features such as encryption, access restrictions, and compliance with data protection laws.

• Enables users to track and manage changes to their data pipelines through version control features.

• Provides collaboration tools for team members to collaborate on pipeline construction and upkeep.

Snowflake vs. Redshift: Capabilities Comparison

1. Performance

2. Ease of use

3. Security

4. Integrations

5. Support

6. Pricing

Performance

• Redshift often lags behind Snowflake in speed. For a range of workloads, including OLAP queries, data loads, and data refreshes, Snowflake was proven to be up to 2x faster than Redshift in a 2018 GigaOm benchmark.

• Due to its columnar storage style and ability to expand horizontally, Snowflake has a performance advantage. For OLAP queries, columnar storage is more effective since it stores data by column rather than by row.

• In order to boost performance, Snowflake can also scale out horizontally, adding more nodes to its cluster.

• Redshift's architecture allows for horizontal scaling, where data is distributed across multiple nodes to handle larger workloads efficiently.

• In some circumstances, Redshift can perform better than Snowflake. Redshift can be quick for specific sorts of analytical workloads, such as geospatial analysis. Snowflake, however, is typically the quicker data warehouse.

Ease of use

• Both are fully-managed cloud-based data warehousing solutions. Users can simply access Both services through the provided interface or APIs without the need for local installations

• Snowflake user interface is more intuitive making it easier to use. Snowflake's automatic query optimization helps optimize performance without requiring manual tuning.

• Redshift needs more manual configuration compared to Snowflake as it's a traditional data warehouse solution.

• As Redshift integrates well with the AWS ecosystem, you can find many great tools for data ingestion, data processing, and data storage workflows.

Security

• Snowflake allows you to bring your own encryption keys (BYOK). Moreover, fine-grained access controls allow businesses to specify granular permissions at different levels.

• Only people with the proper authorization can access particular data sets. The built-in data masking capabilities further improve data security by hiding sensitive data.

• Redshift uses SSL connections to implement data encryption for data in transit. This protects data as it travels between sources and the Redshift cluster security groups.

• Redshift uses AWS KMS for sign-in credentials. KMS is a service for managing and controlling the encryption keys associated with the data stored in Redshift. Thus, you can ensure that only authorized users with the proper sign-in credentials can access and decrypt the data.

• To further increase network security, Redshift also uses VPC support to isolate its Redshift clusters behind its own private network.

Integrations

• Users can ingest data from several cloud storage platforms using Snowflake's native connectors. These are available for well-known data sources like Amazon S3, Google Cloud Storage, and Azure Blob Storage.

• It also offers connectors for conventional databases such as Oracle, DynamoDB, PostgreSQL, MySQL, and SQL Server to incorporate data from on-premises systems.

• Redshift offers native connectivity with Amazon S3. With that, you can use the scalability and durability of S3 storage to directly load data into Redshift from S3.

• Users may automate data cataloging and ETL procedures using Redshift's connection with AWS Glue.

• Redshift also works well with other AWS products like Amazon EMR for processing large data volumes and QuickSight for reporting and data visualization.

Support

• Snowflake provides technical assistance 24/7 through various channels like chat, email, and phone.

• The support team at Snowflake Consulting is known for their quick and knowledgeable responses to user queries and problems.

• Snowflake provides a dedicated Technical Account Manager to enterprise customers for a personalized support experience.

• Redshift consulting is carried out through AWS Support, which offers different support levels with varying response times and coverage options. Users can access round-the-clock technical support for Redshift-related issues.

• Redshift has extensive documentation available online, including user guides, developer guides, and API references created by AWS itself.

Pricing

• Snowflake pricing uses a 'pay-as-you-go' model, meaning you only pay for what you use. The cost depends on how much computing power and storage you need.

• Snowflake also offers different payment plans. These include 'on-demand' where you pay for each use, 'reserved' where you pay upfront for a set amount of resources, and 'BYOL' which lets you use a pre-existing license.

• Redshift bills on an hourly basis, with rates determined by the cluster's node count and storage consumption. Redshift pricing, too, offers a range of cost options, including on-demand, reserved, and spot instances for flexibility.

• For small workloads, Snowflake is typically more expensive than Redshift. Snowflake, however, offers a pay-as-you-go pricing model. It lets users scale up or down as needed, making it more cost-effective for heavy workloads.

Snowflake vs. Redshift: Which is best for you?

Snowflake is the perfect choice for businesses that need flexibility and agility. It stands out due to its capacity for managing a variety of tasks and support for semi-structured data like JSON. 

Redshift, on the other hand, is a wise choice for companies that already use AWS infrastructure, mainly due to its tight integration with the AWS ecosystem. It offers a user-friendly UI and easy integration with other AWS technologies.

Therefore, it is important to evaluate your specific requirements, possibly with the assistance of big data consulting. You need to consider factors such as cost, performance, and integration to determine which option is the most suitable for your needs.

Regardless of whether you use Snowflake or Redshift, you cannot ignore the importance of using a good ETL tool. Portable is an excellent ETL tool that supports over 500+ hard-to-find connectors.

If you are interested in trying it out, Portable is free to start.