DBMS > GeoMesa vs. Kyligence Enterprise vs. Spark SQL vs. Transbase vs. Transwarp Hippo

System Properties Comparison GeoMesa vs. Kyligence Enterprise vs. Spark SQL vs. Transbase vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

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

A distributed analytics engine for big data, built on top of Apache Kylin

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

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

Primary database model

Spatial DBMS

Relational DBMS

Vector DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Website

www.geomesa.org

kyligence.io/kyligence-enterprise

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

CCRi and others

Kyligence, Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

2014

2016

2014

1987

2023

Current release

5.0.0, May 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache 2.0

commercial

free development license

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

Java

Scala

C and C++

C++

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Recent citations in the news

Kyligence Grows OLAP Business in the Cloud
20 February 2020, Datanami

Kyligence adds ClickHouse OLAP engine to its analytics platform
10 August 2021, VentureBeat

Distributed OLAPer Kyligence accelerates core engine, adds real-time data support – Blocks and Files
10 August 2021, Blocks & Files

How Kyligence Cloud uses Amazon EMR Serverless to simplify OLAP | Amazon Web Services
9 November 2022, AWS Blog

The 10 Hottest Big Data Tools Of 2023
23 November 2023, CRN

provided by Google News

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

provided by Google News

Share this page