DBMS > HEAVY.AI vs. SAP HANA vs. TigerGraph vs. Warp 10

System Properties Comparison HEAVY.AI vs. SAP HANA vs. TigerGraph vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

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

Primary database model

Relational DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

www.sap.com/products/hana.html

www.tigergraph.com

www.warp10.io

Technical documentation

docs.heavy.ai

help.sap.com/hana

docs.tigergraph.com

www.warp10.io/content/02_Getting_started

Developer

HEAVY.AI, Inc.

SAP

SenX

Initial release

2016

2010

2017

2015

Current release

5.10, January 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache License 2.0

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++ and CUDA

C++

Java

Server operating systems

Linux

Appliance or cloud-service

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

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

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

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java

Server-side scripts

Stored procedures

SQLScript, R

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

based on HBase

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Role-based access control

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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