DBMS > Google BigQuery vs. Microsoft Azure Table Storage vs. VoltDB vs. YugabyteDB

System Properties Comparison Google BigQuery vs. Microsoft Azure Table Storage vs. VoltDB vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A Wide Column Store for rapid development using massive semi-structured datasets

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	1.46
Rank	#159	Overall
	#74	Relational DBMS

Score	3.11
Rank	#99	Overall
	#51	Relational DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/storage/tables

www.voltdb.com

www.yugabyte.com

Technical documentation

cloud.google.com/bigquery/docs

docs.voltdb.com

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Google

Microsoft

VoltDB Inc.

Yugabyte Inc.

Initial release

2010

2012

2010

2017

Current release

11.3, April 2022

2.19, September 2023

License

Commercial or Open Source

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Open Source

Apache 2.0

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.

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

Java, C++

C and C++

Server operating systems

hosted

Linux
OS X

for development

Linux
OS X

Data scheme

yes

schema-free

yes

depending on used data model

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

only a subset of SQL 99

yes, PostgreSQL compatible

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

yes

Transaction concepts

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

Since BigQuery is designed for querying data

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Snapshots and command logging

yes

based on RocksDB

In-memory capabilities

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights based on private key authentication or shared access signatures

Users and roles with access to stored procedures

yes

More information provided by the system vendor

Specific characteristics

YugabyteDB is an open source distributed SQL database for cloud native transactional...
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Competitive advantages

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
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Typical application scenarios

Systems of record and engagement for cloud native applications that require resilience,...
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Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
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Licensing and pricing models

Apache 2.0 license for the database
» more

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