AWS Redshift vs Snowflake
A detailed comparison
Compare AWS Redshift and Snowflake for time series and OLAP workloads
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of AWS Redshift and Snowflake so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how AWS Redshift and Snowflake perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
AWS Redshift vs Snowflake Breakdown
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 |
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| Database Model | Data warehouse |
Cloud data warehouse |
| Architecture | AWS Redshift utilizes a columnar storage format for fast querying and supports standard SQL. Redshift uses a distributed, shared-nothing architecture, where data is partitioned across multiple compute nodes. Each node is further divided into slices, with each slice processing a subset of data in parallel. Redshift can be deployed in a single-node or multi-node cluster, with the latter providing better performance for large datasets. |
Snowflake can be deployed across multiple cloud providers, including AWS, Azure, and Google Cloud |
| License | Closed source |
Closed source |
| Use Cases | Business analytics, large-scale data processing, real-time dashboards, data integration, machine learning |
Big data analytics, Data warehousing, Data engineering, Data sharing, Machine learning |
| Scalability | Supports scaling storage and compute independently, with support for adding or removing nodes as needed |
Highly scalable with multi-cluster shared data architecture, automatic scaling, and performance isolation |
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AWS Redshift Overview
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.
Snowflake Overview
Snowflake is a cloud-based data warehousing platform that was founded in 2012 and officially launched in 2014. It is designed to enable organizations to efficiently store, process, and analyze large volumes of structured and semi-structured data. Snowflake’s unique architecture separates storage, compute, and cloud services, allowing users to independently scale and optimize each component.
AWS Redshift for Time Series Data
AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.
Snowflake for Time Series Data
While Snowflake is not specifically designed for time series data, it can still effectively store, process, and analyze such data due to its scalable and flexible architecture. Snowflake’s columnar storage format, combined with its powerful query engine and support for SQL, makes it a suitable option for time series data analysis.
AWS Redshift Key Concepts
- Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
- Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
- Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
- Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.
Snowflake Key Concepts
- Virtual Warehouse: A compute resource in Snowflake that processes queries and performs data loading and unloading. Virtual Warehouses can be independently scaled up or down based on demand.
- Micro-Partition: A storage unit in Snowflake that contains a subset of the data in a table. Micro-partitions are automatically optimized for efficient querying.
- Time Travel: A feature in Snowflake that allows users to query historical data at specific points in time or within a specific time range.
- Data Sharing: The ability to securely share data between Snowflake accounts, without the need to copy or transfer the data.
AWS Redshift Architecture
Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.
Snowflake Architecture
Snowflake’s architecture separates storage, compute, and cloud services, allowing users to scale and optimize each component independently. The platform uses a columnar storage format and supports ANSI SQL for querying and data manipulation. Snowflake is built on top of AWS, Azure, and GCP, providing a fully managed, elastic, and secure data warehouse solution. Key components of the Snowflake architecture include databases, tables, virtual warehouses, and micro-partitions.
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AWS Redshift Features
Scalability
Redshift allows you to scale your cluster up or down by adding or removing compute nodes, enabling you to adjust your storage capacity and query performance based on your needs.
Performance
Redshift’s columnar storage format and MPP architecture enable it to deliver high-performance query execution for large-scale data warehousing workloads.
Security
Redshift provides a range of security features, including encryption at rest and in transit, network isolation using Amazon Virtual Private Cloud (VPC), and integration with AWS Identity and Access Management (IAM) for access control.
Snowflake Features
Elasticity
Snowflake’s architecture allows for independent scaling of storage and compute resources, enabling users to quickly adjust to changing workloads and demands.
Fully Managed
Snowflake is a fully managed service, eliminating the need for users to manage infrastructure, software updates, or backups.
Security
Snowflake provides comprehensive security features, including encryption at rest and in transit, multi-factor authentication, and fine-grained access control.
Data Sharing
Snowflake enables secure data sharing between accounts without the need to copy or transfer data.
AWS Redshift Use Cases
Data Warehousing
Redshift is designed for large-scale data warehousing workloads, providing a scalable and high-performance solution for storing and analyzing structured data.
Business Intelligence and Reporting
Redshift integrates with various BI and reporting tools, enabling organizations to gain insights from their data and make data-driven decisions.
ETL and Data Integration
Redshift supports data loading and extraction, transformation, and loading (ETL) processes, allowing you to integrate data from various sources and prepare it for analysis.
Snowflake Use Cases
Data Warehousing
Snowflake provides a scalable, secure, and fully managed data warehousing solution, making it suitable for organizations that need to store, process, and analyze large volumes of structured and semi-structured data.
Data Lake
Snowflake can serve as a data lake for ingesting and storing large volumes of raw, unprocessed data, which can be later transformed and analyzed as needed.
Data Integration and ETL
Snowflake’s support for SQL and various data loading and unloading options makes it a good choice for data integration and ETL
AWS Redshift Pricing Model
Amazon Redshift offers two pricing models: On-Demand and Reserved Instances. With On-Demand pricing, you pay for the capacity you use on an hourly basis, with no long-term commitments. Reserved Instances offer the option to reserve capacity for a one- or three-year term, with a lower hourly rate compared to On-Demand pricing. In addition to these pricing models, you can also choose between different node types, which offer different amounts of storage, memory, and compute resources.
Snowflake Pricing Model
Snowflake offers a pay-as-you-go pricing model, with separate charges for storage and compute resources. Storage is billed on a per-terabyte, per-month basis, while compute resources are billed based on usage, measured in Snowflake Credits. Snowflake offers various editions, including Standard, Enterprise, Business Critical, and Virtual Private Snowflake, each with different features and pricing options. Users can also opt for on-demand or pre-purchased, discounted Snowflake Credits.
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