DBMS > Apache Drill vs. Google BigQuery vs. Microsoft Azure Table Storage

System Properties Comparison Apache Drill vs. Google BigQuery vs. Microsoft Azure Table Storage

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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

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Document store
Relational DBMS

Relational DBMS

Wide column store

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	4.92
Rank	#73	Overall
	#6	Wide column stores

Website

drill.apache.org

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

drill.apache.org/docs

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Google

Microsoft

Initial release

2012

2010

2012

Current release

1.20.3, January 2023

License

Commercial or Open Source

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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Server operating systems

Linux
OS X
Windows

hosted

Data scheme

schema-free

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

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

Transaction concepts

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

Since BigQuery is designed for querying data

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

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

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)

Access rights based on private key authentication or shared access signatures

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