DBMS > Microsoft Azure Data Explorer vs. OpenTSDB vs. Transwarp Hippo

System Properties Comparison Microsoft Azure Data Explorer vs. OpenTSDB vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Fully managed big data interactive analytics platform

Scalable Time Series DBMS based on HBase

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

column oriented

Time Series DBMS

Vector 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	3.02
Rank	#83	Overall
	#46	Relational DBMS

Score	1.46
Rank	#144	Overall
	#12	Time Series DBMS

Score	0.00
Rank	#379	Overall
	#18	Vector DBMS

Website

azure.microsoft.com/services/data-explorer

opentsdb.net

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

opentsdb.net/docs/build/html/index.html

Developer

Microsoft

currently maintained by Yahoo and other contributors

Initial release

2019

2011

2023

Current release

cloud service with continuous releases

1.0, May 2023

License

Commercial or Open Source

commercial

Open Source

LGPL

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

Java

C++

Server operating systems

hosted

Linux
Windows

Linux
macOS

Data scheme

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

numeric data for metrics, strings for tags

Vector, Numeric and String

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

Kusto Query Language (KQL), SQL subset

APIs and other access methods

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

HTTP API
Telnet API

RESTful HTTP API

Supported programming languages

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

Erlang
Go
Java
Python
R
Ruby

C++
Java
Python

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Sharding

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.

selectable replication factor

based on HBase

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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.

yes

User concepts

Access control

Azure Active Directory Authentication

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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