Mimir vs Snowflake
A detailed comparison
Compare Mimir 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 Mimir and Snowflake so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Mimir 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.
Mimir vs Snowflake Breakdown
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| Database Model | Time series database |
Cloud data warehouse |
| Architecture | Grafana Mimir is a time series database designed for high-performance, real-time monitoring, and analytics. It features a distributed architecture, allowing for horizontal scaling across multiple nodes to handle large volumes of data and queries. It can be deployed on-prem due to being open source or as a managed solution hosted by Grafana |
Snowflake can be deployed across multiple cloud providers, including AWS, Azure, and Google Cloud |
| License | APGL 3.0 |
Closed source |
| Use Cases | Monitoring, observability, IoT |
Big data analytics, Data warehousing, Data engineering, Data sharing, Machine learning |
| Scalability | Horizontally scalable |
Highly scalable with multi-cluster shared data architecture, automatic scaling, and performance isolation |
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Mimir Overview
Grafana Mimir is an open-source software project that provides a scalable long-term storage solution for Prometheus. Started at Grafana Labs and announced in 2022, Grafana Mimir aims to become the most scalable and performant open-source time series database for metrics. The project incorporates the knowledge and experience gained by Grafana Labs engineers from running Grafana Enterprise Metrics and Grafana Cloud Metrics at massive scale.
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.
Mimir for Time Series Data
Grafana Mimir is well-suited for handling time series data, making it a suitable choice for scenarios involving metric storage and analysis. It provides long-term storage capabilities for Prometheus, a popular open-source monitoring and alerting system. With Grafana Mimir, users can store and query time series metrics over extended periods, allowing for historical analysis and trend detection. It is especially useful for applications that require scalable and performant storage of time series data for metrics monitoring and observability purposes.
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.
Mimir Key Concepts
- Metrics: In Grafana Mimir, metrics represent the measurements or observations tracked over time. They can include various types of data, such as system metrics, application performance metrics, or sensor data.
- Long-term Storage: Grafana Mimir provides a storage solution specifically tailored for long-term retention of time series data, allowing users to store and query historical metrics over extended periods.
- Microservices: Grafana Mimir adopts a microservices-based architecture, where the system consists of multiple horizontally scalable microservices that can operate independently and in parallel.
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.
Mimir Architecture
Grafana Mimir adopts a microservices-based architecture, where the system comprises multiple horizontally scalable microservices. These microservices can operate independently and in parallel, allowing for efficient distribution of workload and scalability. Grafana Mimir’s components are compiled into a single binary, providing a unified and cohesive system. The architecture is designed to be highly available and multi-tenant, enabling multiple users and applications to utilize the database concurrently. This distributed architecture ensures scalability and resilience in handling large-scale metric storage and retrieval scenarios.
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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Mimir Features
Scalability
Grafana Mimir is designed to scale horizontally, enabling the system to handle growing data volumes and increasing workloads. Its horizontally scalable microservices architecture allows for seamless expansion and improved performance.
High Availability
Grafana Mimir provides high availability by ensuring redundancy and fault tolerance. It allows for replication and distribution of data across multiple nodes, ensuring data durability and continuous availability of stored metrics.
Long-term Storage
Grafana Mimir offers a dedicated solution for long-term storage of time series metrics. It provides efficient storage and retrieval mechanisms, allowing users to retain and analyze historical metric data over extended periods.
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.
Mimir Use Cases
Metrics Monitoring and Observability
Grafana Mimir is well-suited for monitoring and observability use cases. It enables the storage and analysis of time series metrics, allowing users to monitor the performance, health, and behavior of their systems and applications in real-time.
Long Term Metric Storage
With its focus on providing scalable long-term storage, Grafana Mimir is ideal for applications that require retaining and analyzing historical metric data over extended periods. It allows users to store and query large volumes of time series data generated by Prometheus.
Trend and anomaly detection
By using Mimir for storing long term historical data it can be useful for detecting trends in your metrics and also for comparing current metrics to historical data to detect outliers and anomalies
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
Mimir Pricing Model
Grafana Mimir is an open-source project, which means it is freely available for usage and does not require any licensing fees. Users can download the source code and deploy Grafana Mimir on their own infrastructure without incurring direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
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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