DBMS > Apache Druid vs. Google BigQuery vs. HarperDB vs. SAP IQ

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

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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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Columnar RDBMS optimized for Big Data analytics

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store

Relational DBMS

column-oriented

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

www.harperdb.io

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

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation and contributors

Google

HarperDB

SAP, formerly Sybase

Initial release

2012

2010

2017

1994

Current release

29.0.1, April 2024

3.1, August 2021

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free community edition available

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

Node.js

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-less columns are supported

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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 data manipulation statements

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Custom Functions

since release 3.1

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

A table resides as a whole on one (or more) nodes in a cluster

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

the nodes on which a table resides can be defined

SAP/Sybase Replication Server

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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)

Access rights for users and roles

fine grained access rights according to SQL-standard

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