Prometheus vs Snowflake
A detailed comparison
Compare Prometheus 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 Prometheus and Snowflake so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Prometheus 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.
Prometheus vs Snowflake Breakdown
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 |
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| Database Model | Time series database |
Cloud data warehouse |
| Architecture | Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes. |
Snowflake can be deployed across multiple cloud providers, including AWS, Azure, and Google Cloud |
| License | Apache 2.0 |
Closed source |
| Use Cases | Monitoring, alerting, observability, system metrics, application metrics |
Big data analytics, Data warehousing, Data engineering, Data sharing, Machine learning |
| Scalability | Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers) |
Highly scalable with multi-cluster shared data architecture, automatic scaling, and performance isolation |
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Prometheus Overview
Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.
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.
Prometheus for Time Series Data
Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.
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.
Prometheus Key Concepts
- Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
- Time Series: A collection of data points for a metric, indexed by timestamp.
- Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
- PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.
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.
Prometheus Architecture
Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.
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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Prometheus Features
Pull-based Model
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
PromQL
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Alerting
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
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.
Prometheus Use Cases
Infrastructure Monitoring
Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.
Application Performance Monitoring (APM)
Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.
Alerting and Anomaly Detection
Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.
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
Prometheus Pricing Model
Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.
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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