DBMS > HBase vs. HEAVY.AI vs. Heroic vs. Microsoft SQL Server vs. Transwarp Hippo

System Properties Comparison HBase vs. HEAVY.AI vs. Heroic vs. Microsoft SQL Server vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Website

hbase.apache.org

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

github.com/spotify/heroic

www.microsoft.com/en-us/sql-server

www.transwarp.cn/en/subproduct/hippo

Technical documentation

hbase.apache.org/book.html

docs.heavy.ai

spotify.github.io/heroic

learn.microsoft.com/en-US/sql/sql-server

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

HEAVY.AI, Inc.

Spotify

Microsoft

Initial release

2008

2016

2014

1989

2023

Current release

2.3.4, January 2021

5.10, January 2022

SQL Server 2022, November 2022

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++ and CUDA

Java

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Linux
Windows

Linux
macOS

Data scheme

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift
Vega

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

All languages supporting JDBC/ODBC/Thrift
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++
Java
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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