DBMS > Apache Drill vs. DolphinDB vs. H2 vs. Machbase Neo vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Drill vs. DolphinDB vs. H2 vs. Machbase Neo vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

TimeSeries DBMS for AIoT and BigData

Fully managed big data interactive analytics platform

Primary database model

Document store
Relational DBMS

column oriented

Secondary database models

Relational DBMS

Spatial DBMS

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	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

azure.microsoft.com/services/data-explorer

Technical documentation

drill.apache.org/docs

docs.dolphindb.cn/en/help200/index.html

www.h2database.com/html/main.html

machbase.com/dbms

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

Developer

Apache Software Foundation

DolphinDB, Inc

Thomas Mueller

Machbase

Microsoft

Initial release

2012

2018

2005

2013

2019

Current release

1.20.3, January 2023

v2.00.4, January 2022

2.2.220, July 2023

V8.0, August 2023

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community version available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

free test version available

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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

All OS with a Java VM

Linux
macOS
Windows

hosted

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

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

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

yes

all fields are automatically indexed

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

yes

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

JDBC
ODBC

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Supported programming languages

C++

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Java

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes

Java Stored Procedures and User-Defined Functions

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

horizontal partitioning

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

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

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

volatile and lookup table

User concepts

Access control

Depending on the underlying data source

Administrators, Users, Groups

fine grained access rights according to SQL-standard

simple password-based access control

Azure Active Directory Authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Analyse Kafka messages with SQL queries using Apache Drill
23 September 2019, Towards Data Science

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Using Apache Iceberg for Developing Modern Data Tables
3 October 2023, Open Source For You

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

Apache Drill improves big data SQL query engine
31 August 2021, TechTarget

provided by Google News

“Luxembourg is a perfect target area”: Korean accelerator exec
27 October 2022, Delano.lu

provided by Google News

Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog
23 May 2023, azure.microsoft.com

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, Microsoft

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, azure.microsoft.com

Azure Data Explorer and Stream Analytics for anomaly detection
16 January 2020, azure.microsoft.com

Controlling costs in Azure Data Explorer using down-sampling and aggregation
11 February 2019, azure.microsoft.com

provided by Google News

Share this page