DBMS > Google BigQuery vs. InfluxDB vs. Microsoft Azure Table Storage vs. Teradata Aster vs. TigerGraph

System Properties Comparison Google BigQuery vs. InfluxDB vs. Microsoft Azure Table Storage vs. Teradata Aster vs. TigerGraph

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

DBMS for storing time series, events and metrics

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

Platform for big data analytics on multistructured data sources and types

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

cloud.google.com/bigquery

www.influxdata.com/products/influxdb-overview

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

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

docs.influxdata.com/influxdb

docs.tigergraph.com

Developer

Google

Microsoft

Teradata

Initial release

2010

2013

2012

2005

2017

Current release

2.7.6, April 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Linux
OS X

through Homebrew

hosted

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
JSON over UDP

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

C
C#
C++
Java
Python
R

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

R packages

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

yes

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

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

In-memory capabilities

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

yes

Depending on used storage engine

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

Role-based access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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