Amazon Timestream vs InfluxDB

Overview

InfluxDB

Stacks1.0K

Followers1.2K

Votes175

Amazon Timestream

Stacks13

Followers50

Votes0

Amazon Timestream vs InfluxDB: What are the differences?

Amazon Timestream and InfluxDB are time-series databases designed to efficiently handle and analyze data with timestamps. Let's discuss the key differences between Amazon Timestream and InfluxDB.

Scalability and Performance: Amazon Timestream is built to handle massive scale and high-performance requirements. It can ingest trillions of time series events per day and store petabytes of data, making it suitable for large-scale applications. InfluxDB, on the other hand, is also designed for scale, but it may have limitations in handling very large datasets compared to Timestream.
Managed Service vs. Self-hosted: Amazon Timestream is a fully managed service provided by Amazon Web Services (AWS). This means that AWS takes care of all the operational aspects of the database, such as infrastructure provisioning, scaling, and maintenance. InfluxDB, on the other hand, needs to be self-hosted, requiring users to manage their own infrastructure and ensure scalability and availability.
Cost Model: Amazon Timestream follows a pay-as-you-go pricing model, where users pay for the storage used, data ingestion, and query execution. It offers different pricing tiers to accommodate various use cases. InfluxDB, on the other hand, is open-source and free to use, but users need to incur the costs of hosting their infrastructure and managing the database themselves.
Supported Integrations: Amazon Timestream integrates seamlessly with other AWS services, such as AWS IoT, Amazon CloudWatch, and AWS Glue, making it easy to build and integrate with existing AWS workflows. InfluxDB has a wide range of integrations available but may require more manual configuration and setup compared to the native integrations provided by Timestream.
Data Model and Query Language: Amazon Timestream follows a table-like data model, where time series data is grouped into tables with dimensions and measures. It uses a SQL-like query language called Timestream Query for querying and analyzing data. InfluxDB uses a tag-value data model, where each time series is identified by tags and can have multiple values associated with it. It provides a query language called InfluxQL for executing queries.
Community and Ecosystem: InfluxDB has a vibrant open-source community and a large ecosystem of plugins and tools built around it. This allows users to leverage community-supported solutions and benefit from a wide range of integrations. While Amazon Timestream is relatively new compared to InfluxDB, it benefits from being part of the AWS ecosystem and can leverage other AWS services seamlessly.

In summary, Amazon Timestream excels as a fully managed, scalable time-series database within the AWS ecosystem, ideal for users seeking a serverless and integrated solution. InfluxDB, being open source, provides greater flexibility and control over the database environment, making it a strong choice for those who prioritize customization and independence from specific cloud providers.

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Detailed Comparison

InfluxDB	Amazon Timestream
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.	It is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. It saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies.
Time-Centric Functions;Scalable Metrics; Events;Native HTTP API;Powerful Query Language;Built-in Explorer	High performance at low cost; Serverless with auto-scaling; Data lifecycle management; Simplified data access; Purpose-built for time series; Always encrypted
Statistics
Stacks 1.0K	Stacks 13
Followers 1.2K	Followers 50
Votes 175	Votes 0
Pros & Cons
Pros 59 Time-series data analysis 30 Easy setup, no dependencies 24 Fast, scalable & open source 21 Open source 20 Real-time analytics Cons 4 Instability 1 Proprietary query language 1 HA or Clustering is only in paid version	No community feedback yet
Integrations
No integrations available	Amazon Kinesis Grafana Amazon SageMaker Amazon Quicksight Apache Flink

What are some alternatives to InfluxDB, Amazon Timestream?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Amazon Timestream vs InfluxDB: What are the differences?

Amazon Timestream and InfluxDB are time-series databases designed to efficiently handle and analyze data with timestamps. Let's discuss the key differences between Amazon Timestream and InfluxDB.

Scalability and Performance: Amazon Timestream is built to handle massive scale and high-performance requirements. It can ingest trillions of time series events per day and store petabytes of data, making it suitable for large-scale applications. InfluxDB, on the other hand, is also designed for scale, but it may have limitations in handling very large datasets compared to Timestream.
Managed Service vs. Self-hosted: Amazon Timestream is a fully managed service provided by Amazon Web Services (AWS). This means that AWS takes care of all the operational aspects of the database, such as infrastructure provisioning, scaling, and maintenance. InfluxDB, on the other hand, needs to be self-hosted, requiring users to manage their own infrastructure and ensure scalability and availability.
Cost Model: Amazon Timestream follows a pay-as-you-go pricing model, where users pay for the storage used, data ingestion, and query execution. It offers different pricing tiers to accommodate various use cases. InfluxDB, on the other hand, is open-source and free to use, but users need to incur the costs of hosting their infrastructure and managing the database themselves.
Supported Integrations: Amazon Timestream integrates seamlessly with other AWS services, such as AWS IoT, Amazon CloudWatch, and AWS Glue, making it easy to build and integrate with existing AWS workflows. InfluxDB has a wide range of integrations available but may require more manual configuration and setup compared to the native integrations provided by Timestream.
Data Model and Query Language: Amazon Timestream follows a table-like data model, where time series data is grouped into tables with dimensions and measures. It uses a SQL-like query language called Timestream Query for querying and analyzing data. InfluxDB uses a tag-value data model, where each time series is identified by tags and can have multiple values associated with it. It provides a query language called InfluxQL for executing queries.
Community and Ecosystem: InfluxDB has a vibrant open-source community and a large ecosystem of plugins and tools built around it. This allows users to leverage community-supported solutions and benefit from a wide range of integrations. While Amazon Timestream is relatively new compared to InfluxDB, it benefits from being part of the AWS ecosystem and can leverage other AWS services seamlessly.

Amazon Timestream vs InfluxDB

Overview