DBMS > Google Cloud Bigtable vs. Google Cloud Datastore vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Sqrrl

System Properties Comparison Google Cloud Bigtable vs. Google Cloud Datastore vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Key-value store
Wide column store

Document store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

cloud.google.com/bigtable

cloud.google.com/datastore

www.progress.com/marklogic

azure.microsoft.com/services/cosmos-db

sqrrl.com

Technical documentation

cloud.google.com/bigtable/docs

cloud.google.com/datastore/docs

www.progress.com/marklogic/documentation

learn.microsoft.com/azure/cosmos-db

Developer

Google

MarkLogic Corp.

Microsoft

Amazon

originally Sqrrl Data, Inc.

Initial release

2015

2008

2001

2014

2012

Current release

11.0, December 2022

License

Commercial or Open Source

commercial

restricted 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

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL92

SQL-like query language

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

using Google App Engine

yes

via XQuery or JavaScript

JavaScript

Triggers

Callbacks using the Google Apps Engine

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic single-row operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

Atomic updates per row, document, or graph entity

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, with Range Indexes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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