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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Amazon Timestream vs Azure SQL Database

Amazon Timestream vs Azure SQL Database

OverviewComparisonAlternatives

Overview

Azure SQL Database
Azure SQL Database
Stacks585
Followers502
Votes13
Amazon Timestream
Amazon Timestream
Stacks13
Followers50
Votes0

Amazon Timestream vs Azure SQL Database: What are the differences?

Developers describe Amazon Timestream as "Fast, scalable, serverless time series database". 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. On the other hand, Azure SQL Database is detailed as "Managed, intelligent SQL in the cloud". It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Amazon Timestream can be classified as a tool in the "Databases" category, while Azure SQL Database is grouped under "SQL Database as a Service".

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Detailed Comparison

Azure SQL Database
Azure SQL Database
Amazon Timestream
Amazon Timestream

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

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.

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High performance at low cost; Serverless with auto-scaling; Data lifecycle management; Simplified data access; Purpose-built for time series; Always encrypted
Statistics
Stacks
585
Stacks
13
Followers
502
Followers
50
Votes
13
Votes
0
Pros & Cons
Pros
  • 6
    Managed
  • 4
    Secure
  • 3
    Scalable
No community feedback yet
Integrations
No integrations available
Amazon Kinesis
Amazon Kinesis
Grafana
Grafana
Amazon SageMaker
Amazon SageMaker
Amazon Quicksight
Amazon Quicksight
Apache Flink
Apache Flink

What are some alternatives to Azure SQL Database, Amazon Timestream?

MongoDB

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

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

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.

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Microsoft SQL Server

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

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

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

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

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

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.

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