DBMS > BigObject vs. Drizzle vs. Google BigQuery vs. SAP Advantage Database Server vs. SAP HANA

System Properties Comparison BigObject vs. Drizzle vs. Google BigQuery vs. SAP Advantage Database Server vs. SAP HANA

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for real-time computations and queries

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

Low-cost RDBMS with access to ISAM and FoxPro data structures

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

bigobject.io

cloud.google.com/bigquery

www.sap.com/products/advantage-database-server.html

www.sap.com/products/hana.html

Technical documentation

docs.bigobject.io

cloud.google.com/bigquery/docs

help.sap.com/hana

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

Google

Sybase, SAP

SAP

Initial release

2015

2008

2010

1993

2010

Current release

7.2.4, September 2012

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

hosted

Linux
Windows

Appliance or cloud-service

Data scheme

yes

Typing

predefined data types such as float or date

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 DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

RESTful HTTP/JSON API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Delphi
Perl
PHP

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

yes

SQLScript, R

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

caching of (temporary) tables can be configured

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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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