DBMS > Apache Drill vs. Google BigQuery vs. Lovefield vs. SiteWhere

System Properties Comparison Apache Drill vs. Google BigQuery vs. Lovefield vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Large scale data warehouse service with append-only tables

Embeddable relational database for web apps written in pure JavaScript

M2M integration platform for persisting/querying time series data

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

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

Score	2.28
Rank	#119	Overall
	#23	Document stores
	#56	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Website

drill.apache.org

cloud.google.com/bigquery

google.github.io/lovefield

github.com/sitewhere/sitewhere

Technical documentation

drill.apache.org/docs

cloud.google.com/bigquery/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

Google

SiteWhere

Initial release

2012

2010

2014

2010

Current release

1.20.3, January 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

Java

Server operating systems

Linux
OS X
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

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 SELECT statement is SQL:2003 compliant

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST

Supported programming languages

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Depending on the underlying data source

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

using MemoryDB

User concepts

Access control

Depending on the underlying data source

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

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