DBMS > Databend vs. Microsoft Azure Data Explorer vs. SWC-DB vs. TempoIQ

System Properties Comparison Databend vs. Microsoft Azure Data Explorer vs. SWC-DB vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Fully managed big data interactive analytics platform

A high performance, scalable Wide Column DBMS

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Relational DBMS

column oriented

Wide column store

Time Series 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

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

github.com/datafuselabs/databend
www.databend.com

azure.microsoft.com/services/data-explorer

github.com/kashirin-alex/swc-db
www.swcdb.org

tempoiq.com (offline)

Technical documentation

docs.databend.com

docs.microsoft.com/en-us/azure/data-explorer

Developer

Databend Labs

Microsoft

Alex Kashirin

TempoIQ

Initial release

2021

2019

2020

2012

Current release

1.0.59, April 2023

cloud service with continuous releases

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL V3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

C++

Server operating systems

hosted
Linux
macOS

hosted

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

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

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Proprietary protocol
Thrift

HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C++

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Users with fine-grained authorization concept, user roles

Azure Active Directory Authentication

simple authentication-based access control

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