DBMS > Amazon DocumentDB vs. Google BigQuery vs. Graphite vs. IBM Db2 vs. Microsoft Azure Synapse Analytics

System Properties Comparison Amazon DocumentDB vs. Google BigQuery vs. Graphite vs. IBM Db2 vs. Microsoft Azure Synapse Analytics

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Large scale data warehouse service with append-only tables

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Common in IBM host environments, 2 different versions for host and Windows/Linux

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	5.19
Rank	#62	Overall
	#4	Time Series DBMS

Score	123.05
Rank	#9	Overall
	#6	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

aws.amazon.com/documentdb

cloud.google.com/bigquery

github.com/graphite-project/graphite-web

www.ibm.com/products/db2

azure.microsoft.com/services/synapse-analytics

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/bigquery/docs

graphite.readthedocs.io

www.ibm.com/docs/en/db2

docs.microsoft.com/azure/synapse-analytics

Developer

Google

Chris Davis

IBM

Microsoft

Initial release

2019

2010

2006

1983

host version

2016

Current release

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free version is available

commercial

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

Python

C and C++

C++

Server operating systems

hosted

Linux
Unix

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP/JSON API

HTTP API
Sockets

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

yes

with separate tools (MQ, InfoSphere)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

Atomic single-document operations

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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.

User concepts

Access control

Access rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

yes

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