DBMS > Apache Druid vs. Google BigQuery vs. OrientDB vs. SAP IQ vs. Sqrrl

System Properties Comparison Apache Druid vs. Google BigQuery vs. OrientDB vs. SAP IQ 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

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

Multi-model DBMS (Document, Graph, Key/Value)

Columnar RDBMS optimized for Big Data analytics

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

column-oriented

Document store
Graph DBMS
Key-value store
Wide column store

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	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

orientdb.org

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

sqrrl.com

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

www.orientdb.com/docs/last/index.html

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation and contributors

Google

OrientDB LTD; CallidusCloud; SAP

SAP, formerly Sybase

Amazon

originally Sqrrl Data, Inc.

Initial release

2012

2010

1994

2012

Current release

29.0.1, April 2024

3.2.29, March 2024

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Unix

hosted

All OS with a Java JDK (>= JDK 6)

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Ruby

Actionscript
C

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, Javascript

yes

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

SAP/Sybase Replication Server

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

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.

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)

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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

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