DBMS > Google BigQuery vs. Ignite vs. JaguarDB vs. Microsoft Azure Table Storage vs. YugabyteDB

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

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Performant, highly scalable DBMS for AI and IoT applications

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

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

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	35.58
Rank	#24	Overall
	#15	Relational DBMS

Score	4.37
Rank	#77	Overall
	#11	Key-value stores
	#42	Relational DBMS

Score	0.02
Rank	#334	Overall
	#54	Key-value stores

Score	4.70
Rank	#70	Overall
	#5	Wide column stores

Score	0.92
Rank	#179	Overall
	#90	Relational DBMS

Website

cloud.google.com/bigquery

ignite.apache.org

www.datajaguar.com

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

www.yugabyte.com

Technical documentation

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

www.datajaguar.com/support.php

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

Developer

Google

Apache Software Foundation

DataJaguar, Inc.

Microsoft

Yugabyte Inc.

Initial release

2010

2015

2012

2017

Current release

Apache Ignite 2.6

2.9.4 September 2018

2.4, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3.0

commercial

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.

Implementation language

C++, Java, .Net

C++

the server part. Clients available in other languages

C and C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

hosted

Linux
OS X

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes, PostgreSQL compatible

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

RESTful HTTP API

YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

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

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

sql, plpgsql, C

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Hash and Range Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

yes

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

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

optimistic locking

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

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 privileges (owner, writer, reader) for whole datasets, not for individual tables

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

rights management via user accounts

Access rights based on private key authentication or shared access signatures

yes

More information provided by the system vendor

Specific characteristics

SQL support, group by, order by, join
» more

Competitive advantages

Extremely easy to scale-out (in seconds) 100,000 times faster
» more

Typical application scenarios

IoT data, time series data, AI data. Any type of numerical data.
» more

Key customers

Quantea, 2PiDay, Synqy, SinoLogistics, IIVI
» more

Market metrics

Advertising awareness, installed base, brand image, market share
» more

Licensing and pricing models

Free-trial license, per-node license, and subscription license.
» more

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