DBMS > Google BigQuery vs. HBase vs. Neo4j vs. STSdb vs. Tarantool

System Properties Comparison Google BigQuery vs. HBase vs. Neo4j vs. STSdb vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	44.46
Rank	#23	Overall
	#1	Graph DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

cloud.google.com/bigquery

hbase.apache.org

neo4j.com

github.com/STSSoft/STSdb4

www.tarantool.io

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

neo4j.com/docs

www.tarantool.io/en/doc

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Neo4j, Inc.

STS Soft SC

Initial release

2010

2008

2007

2011

2008

Current release

2.3.4, January 2021

5.19, April 2024

4.0.8, September 2015

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

GPL version3, commercial licenses available

Open Source

GPLv2, commercial license available

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java

Java, Scala

C and C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Windows

BSD
Linux
macOS

Data scheme

yes

schema-free, schema definition possible

schema-free and schema-optional

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

string, double, decimal, uuid, integer, blob, boolean, datetime

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

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

.NET Client API

Open binary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

C#
Java

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

yes

User defined Procedures and Functions

Lua, C and SQL stored procedures

Triggers

yes

via event handler

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes using Neo4j Fabric

none

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Causal Clustering using Raft protocol

available in in Enterprise Version only

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

More information provided by the system vendor

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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