DBMS > Apache Kylin vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. RocksDB vs. Warp 10

System Properties Comparison Apache Kylin vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. RocksDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

kylin.apache.org

www.geomesa.org

azure.microsoft.com/services/synapse-analytics

rocksdb.org

www.warp10.io

Technical documentation

kylin.apache.org/docs

www.geomesa.org/documentation/stable/user/index.html

docs.microsoft.com/azure/synapse-analytics

github.com/facebook/rocksdb/wiki

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation, originally contributed from eBay Inc

CCRi and others

Microsoft

Facebook, Inc.

SenX

Initial release

2015

2014

2016

2013

2015

Current release

3.1.0, July 2020

5.0.0, May 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

Open Source

BSD

Open Source

Apache License 2.0

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

Scala

C++

Java

Server operating systems

Linux

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

C++ API
Java API

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
Java
PHP

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Transact SQL

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding, horizontal partitioning

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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