DBMS > Alibaba Cloud Table Store vs. Apache Druid vs. Google BigQuery vs. Oracle NoSQL

System Properties Comparison Alibaba Cloud Table Store vs. Apache Druid vs. Google BigQuery vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Wide column store

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

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

Score	0.24
Rank	#310	Overall
	#12	Wide column stores

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

www.alibabacloud.com/product/table-store

druid.apache.org

cloud.google.com/bigquery

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

www.alibabacloud.com/help/en/tablestore

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Alibaba

Apache Software Foundation and contributors

Google

Oracle

Initial release

2016

2012

2010

2011

Current release

29.0.1, April 2024

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix

hosted

Linux
Solaris SPARC/x86

Data scheme

schema-free

yes

schema-less columns are supported

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

SQL for querying

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

Java
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

manual/auto, time-based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, via HDFS, S3 or other storage engines

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

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

off heap cache

User concepts

Access control

Access rights based on subaccounts and tokens

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

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