Amazon Timestream for LiveAnalytics vs Snowflake
A detailed comparison
Compare Amazon Timestream for LiveAnalytics 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 Amazon Timestream for LiveAnalytics and Snowflake so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Amazon Timestream for LiveAnalytics 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.
Amazon Timestream for LiveAnalytics vs Snowflake Breakdown
 |
 |
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| Database Model | Time series database |
Cloud data warehouse |
| Architecture | Timestream is a fully managed, serverless time series database service that is only available on AWS. |
Snowflake can be deployed across multiple cloud providers, including AWS, Azure, and Google Cloud |
| License | Closed source |
Closed source |
| Use Cases | IoT, DevOps, time series analytics |
Big data analytics, Data warehousing, Data engineering, Data sharing, Machine learning |
| Scalability | Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention |
Highly scalable with multi-cluster shared data architecture, automatic scaling, and performance isolation |
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Amazon Timestream for LiveAnalytics Overview
Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by AWS. Launched in 2020, Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it an ideal choice for IoT, monitoring, and analytics applications that require high ingestion rates, efficient storage, and fast querying capabilities. As a part of the AWS ecosystem, Timestream for LiveAnalytics easily integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud. AWS also offers Timestream for InfluxDB which is a managed version of InfluxDB that is compatible with InfluxDB 2.x APIs and released in partnership with InfluxData.
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.
Amazon Timestream for LiveAnalytics for Time Series Data
Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it a suitable choice for a wide range of applications that require high ingestion rates and efficient storage. Its dual-tiered storage architecture, consisting of the memory Store and magnetic Store, allows users to manage data retention and optimize storage costs based on data age and access patterns. Additionally, Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.
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.
Amazon Timestream for LiveAnalytics Key Concepts
- Memory Store: In Amazon Timestream for LiveAnalytics, the Memory Store is a component that stores recent, mutable time series data in memory for fast querying and analysis.
- Magnetic Store: The Magnetic Store in Amazon Timestream for LiveAnalytics is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
- Time-to-Live (TTL): Amazon Timestream for LiveAnalytics allows users to set a TTL on their time series data, which determines how long data is retained in the Memory Store before being moved to the Magnetic Store or deleted.
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.
Amazon Timestream for LiveAnalytics Architecture
Amazon Timestream for LiveAnalytics is built on a serverless, distributed architecture that supports SQL-like querying capabilities. Its data model is specifically tailored for time series data, using time-stamped records and a flexible schema that can accommodate varying data granularities and dimensions. The core components of Timestream’s architecture include the Memory Store and the Magnetic Store, which together manage data retention, storage, and querying. The Memory Store is optimized for fast querying of recent data, while the Magnetic Store provides cost-efficient, long-term storage for historical 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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Amazon Timestream for LiveAnalytics Features
Serverless architecture
Amazon Timestream for LiveAnalytics serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.
Dual-tiered storage
Timestream’s dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, automatically manages data retention and optimizes storage costs based on data age and access patterns.
SQL-like querying
Amazon Timestream for LiveAnalytics supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.
Timestream for InfluxDB
For workloads that require near real-time queries with single millisecond latency AWS recommends using Timestream for InfluxDB rather than LiveAnalytics. Timestream for InfluxDB also provides compatibility with InfluxDB APIs for users who want an AWS managed service without having to update their code.
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.
Amazon Timestream for LiveAnalytics Use Cases
IoT applications
Amazon Timestream for LiveAnalytic’s support for high ingestion rates and efficient storage makes it an ideal choice for monitoring and analyzing data from IoT devices, such as sensors and smart appliances.
Devops
LiveAnalytics can be used for general DevOps workloads like monitoring application health and utilization. For use cases that require real time monitoring with the lowest latency possible, AWS recommends using Timestream for InfluxDB.
Analytics
Amazon Timestream for LiveAnalytics can be used to track analytics data like web and application data. The built-in time series analytics functions can then be used to aggregate and analyze data to get valuable insights with increased developer productivity.
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
Amazon Timestream for LiveAnalytics Pricing Model
Amazon Timestream for LiveAnalytics offers a pay-as-you-go pricing model based on data ingestion, storage, and query execution. Ingestion costs are determined by the volume of data ingested into Timestream, while storage costs are based on the amount of data stored in the Memory Store and Magnetic Store. Query execution costs are calculated based on the amount of data scanned and processed during query execution. Timestream also offers a free tier for users to explore the service and build proof-of-concept applications without incurring costs.
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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