DBMS > LevelDB vs. Netezza vs. SAP HANA vs. TigerGraph

System Properties Comparison LevelDB vs. Netezza vs. SAP HANA vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Data warehouse and analytics appliance part of IBM PureSystems

In-memory, column based data store. Available as appliance or cloud service

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

github.com/google/leveldb

www.ibm.com/products/netezza

www.sap.com/products/hana.html

www.tigergraph.com

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

help.sap.com/hana

docs.tigergraph.com

Developer

Google

IBM

SAP

Initial release

2011

2000

2010

2017

Current release

1.23, February 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Illumos
Linux
OS X
Windows

Linux

included in appliance

Appliance or cloud-service

Linux

Data scheme

schema-free

yes

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

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC
OLE DB

JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Fortran
Java
Lua
Perl
Python
R

C++
Java

Server-side scripts

Stored procedures

yes

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

Users with fine-grained authorization concept

yes

Role-based access control

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