DBMS > HBase vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. Sphinx vs. TigerGraph

System Properties Comparison HBase vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. Sphinx vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Globally distributed, horizontally scalable, multi-model database service

Open source search engine for searching in data from different sources, e.g. relational databases

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

Primary database model

Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

hbase.apache.org

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

www.tigergraph.com

Technical documentation

hbase.apache.org/book.html

developer.microsoft.com/en-us/access

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

docs.tigergraph.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Sphinx Technologies Inc.

Initial release

2008

1992

2014

2001

2017

Current release

2.3.4, January 2021

1902 (16.0.11328.20222), March 2019

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Bundled with Microsoft Office

commercial

Open Source

GPL version 2, commercial licence available

commercial

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

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Windows

Not a real database server, but making use of DLLs

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

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

JSON types

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

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like query language

SQL-like query language (SphinxQL)

SQL-like query language (GSQL)

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
DAO
ODBC
OLE DB

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary protocol

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

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

since Access 2010 using the ACE-engine

JavaScript

yes

Triggers

yes

since Access 2010 using the ACE-engine

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

but no files for transaction logging

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

The original contents of fields are not stored in the Sphinx index.

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

a simple user-level security was built in till version Access 2003

Access rights can be defined down to the item level

Role-based access control

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.