DBMS > BigObject vs. Blueflood vs. Google Cloud Datastore vs. SAP SQL Anywhere vs. SQLite

System Properties Comparison BigObject vs. Blueflood vs. Google Cloud Datastore vs. SAP SQL Anywhere vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Document store

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

bigobject.io

blueflood.io

cloud.google.com/datastore

www.sap.com/products/technology-platform/sql-anywhere.html

www.sqlite.org

Technical documentation

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

cloud.google.com/datastore/docs

help.sap.com/docs/SAP_SQL_Anywhere

www.sqlite.org/docs.html

Developer

BigObject, Inc.

Rackspace

Google

SAP

formerly Sybase

Dwayne Richard Hipp

Initial release

2015

2013

2008

1992

2000

Current release

17, July 2015

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

Open Source

Public Domain

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

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

predefined scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

yes, details here

yes

not rigid because of 'dynamic typing' concept.

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

SQL-like DML and DDL statements

SQL-like query language (GQL)

yes

SQL-92 is not fully supported

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

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

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Lua

using Google App Engine

yes, in C/C++, Java, .Net or Perl

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

Multi-source replication using Paxos

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

via file-system locks

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

User concepts

Access control

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

fine grained access rights according to SQL-standard

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