DBMS > atoti vs. Microsoft Azure Data Explorer vs. ReductStore vs. Transwarp KunDB

System Properties Comparison atoti vs. Microsoft Azure Data Explorer vs. ReductStore vs. Transwarp KunDB

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Editorial information provided by DB-Engines
Name	atoti  Xexclude from comparison	Microsoft Azure Data Explorer  Xexclude from comparison	ReductStore  Xexclude from comparison	Transwarp KunDB  Xexclude from comparison
Description	An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.	Fully managed big data interactive analytics platform	Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.	OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle
Primary database model	Object oriented DBMS	Relational DBMS column oriented	Time Series DBMS	Relational DBMS
Secondary database models		Document store If a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell Event Store this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps) Spatial DBMS Search engine support for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine Time Series DBMS see docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking measures the popularity of database management systems Trend Chart	Score 0.61 Rank #243   Overall #10   Object oriented DBMS	Score 3.80 Rank #81   Overall #43   Relational DBMS	Score 0.05 Rank #384   Overall #44   Time Series DBMS	Score 0.14 Rank #341   Overall #149   Relational DBMS
Website	atoti.io	azure.microsoft.com/­services/­data-explorer	github.com/­reductstore www.reduct.store	www.transwarp.cn/­en/­product/­kundb
Technical documentation	docs.atoti.io	docs.microsoft.com/­en-us/­azure/­data-explorer	www.reduct.store/­docs
Developer	ActiveViam	Microsoft	ReductStore LLC	Transwarp
Initial release		2019	2023
Current release		cloud service with continuous releases	1.9, March 2024
License Commercial or Open Source	commercial free versions available	commercial	Open Source Business Source License 1.1	commercial
Cloud-based only Only available as a cloud service	no	yes	no	no
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Implementation language	Java		C++, Rust
Server operating systems		hosted	Docker Linux macOS Windows
Data scheme		Fixed schema with schema-less datatypes (dynamic)		yes
Typing predefined data types such as float or date		yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types		yes
XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.		yes
Secondary indexes		all fields are automatically indexed		yes
SQL Support of SQL	Multidimensional Expressions (MDX)	Kusto Query Language (KQL), SQL subset		yes
APIs and other access methods		Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API	HTTP API
Supported programming languages		.Net Go Java JavaScript (Node.js) PowerShell Python R	C++ JavaScript (Node.js) Python Rust
Server-side scripts Stored procedures	Python	Yes, possible languages: KQL, Python, R		yes
Triggers		yes see docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy		yes
Partitioning methods Methods for storing different data on different nodes	Sharding, horizontal partitioning	Sharding Implicit feature of the cloud service		horizontal partitioning
Replication methods Methods for redundantly storing data on multiple nodes		yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce Offers an API for user-defined Map/Reduce methods	no	Spark connector (open source): github.com/­Azure/­azure-kusto-spark		no
Consistency concepts Methods to ensure consistency in a distributed system		Eventual Consistency Immediate Consistency		Immediate Consistency
Foreign keys Referential integrity		no		yes
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data		no		ACID
Concurrency Support for concurrent manipulation of data	yes, multi-version concurrency control (MVCC)	yes		yes
Durability Support for making data persistent		yes		yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.	yes	no
User concepts Access control		Azure Active Directory Authentication		yes
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More resources
	atoti	Microsoft Azure Data Explorer	ReductStore	Transwarp KunDB
Recent citations in the news	Overview Of Atoti: A Python BI Analytics Tool – AIM 14 May 2021, Analytics India Magazine FRTB product of the year: ActiveViam 28 November 2023, Risk.net provided by Google News	We’re retiring Azure Time Series Insights on 7 July 2024 – transition to Azure Data Explorer | Azure updates 31 May 2024, Microsoft Update records in a Kusto Database (public preview) | Azure updates 20 February 2024, Microsoft Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates 8 January 2024, Microsoft Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints ... 5 February 2024, Microsoft Migration of Azure Virtual Network injected Azure Data Explorer cluster to Private Endpoints | Azure updates 4 December 2023, Microsoft provided by Google News

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