DBMS > Apache Druid vs. Google BigQuery vs. Oracle Berkeley DB vs. SAP IQ vs. YDB

System Properties Comparison Apache Druid vs. Google BigQuery vs. Oracle Berkeley DB vs. SAP IQ vs. YDB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

Widely used in-process key-value store

Columnar RDBMS optimized for Big Data analytics

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Document store
Relational DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/berkeleydb.html

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

docs.oracle.com/cd/E17076_05/html/index.html

help.sap.com/docs/SAP_IQ

ydb.tech/en/docs

Developer

Apache Software Foundation and contributors

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP, formerly Sybase

Yandex

Initial release

2012

2010

1994

2019

Current release

29.0.1, April 2024

18.1.40, May 2020

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

commercial license available

commercial

Open Source

Apache 2.0; commercial license available

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

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X
Unix

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL interfaced based on SQLite is available

yes

SQL-like query language (YQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C#
C++
Java
Perl
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

SAP/Sybase Replication Server

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Access rights defined for Yandex Cloud users

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