DBMS > atoti vs. Google Cloud Bigtable vs. Hive vs. HugeGraph vs. Microsoft SQL Server

System Properties Comparison atoti vs. Google Cloud Bigtable vs. Hive vs. HugeGraph vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A fast-speed and highly-scalable Graph DBMS

Microsofts flagship relational DBMS

Primary database model

Object oriented DBMS

Key-value store
Wide column store

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

atoti.io

cloud.google.com/bigtable

hive.apache.org

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

docs.atoti.io

cloud.google.com/bigtable/docs

cwiki.apache.org/confluence/display/Hive/Home

hugegraph.apache.org/docs

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

Developer

ActiveViam

Google

Apache Software Foundation

initially developed by Facebook

Baidu

Microsoft

Initial release

2015

2012

2018

1989

Current release

3.1.3, April 2022

0.9

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

restricted free version is available

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

hosted

All OS with a Java VM

Linux
macOS
Unix

Linux
Windows

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like DML and DDL statements

yes

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

C++
Java
PHP
Python

Groovy
Java
Python

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

Server-side scripts

Stored procedures

Python

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

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, horizontal partitioning

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

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

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

Users, roles and permissions

fine grained access rights according to SQL-standard

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