DBMS > Firebase Realtime Database vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Synapse Analytics vs. Sqrrl

System Properties Comparison Firebase Realtime Database vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Synapse Analytics 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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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

Embeddable relational database for web apps written in pure JavaScript

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

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/datastore

google.github.io/lovefield

azure.microsoft.com/services/synapse-analytics

sqrrl.com

Technical documentation

firebase.google.com/docs/database

cloud.google.com/datastore/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.microsoft.com/azure/synapse-analytics

Developer

Google

acquired by Google 2014

Google

Microsoft

Amazon

originally Sqrrl Data, Inc.

Initial release

2012

2008

2014

2016

2012

Current release

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

JavaScript

C++

Java

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

ADO.NET
JDBC
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

Java
JavaScript
Objective-C

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

JavaScript

C#
Java
PHP

Actionscript
C

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

using Google App Engine

Transact SQL

Triggers

Callbacks are triggered when data changes

Callbacks using the Google Apps Engine

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

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

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

yes, based on authentication and database rules

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

yes

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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