DBMS > Apache Impala vs. Google BigQuery vs. Microsoft Azure Table Storage vs. TimesTen vs. TinkerGraph

System Properties Comparison Apache Impala vs. Google BigQuery vs. Microsoft Azure Table Storage vs. TimesTen vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

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

In-Memory RDBMS compatible to Oracle

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Secondary database models

Document store

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Trend Chart

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Website

impala.apache.org

cloud.google.com/bigquery

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

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

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Microsoft

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2010

2012

1998

2009

Current release

4.1.0, June 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Java
PL/SQL

Groovy
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

optional

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

fine grained access rights according to SQL-standard

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