DBMS > Google Cloud Bigtable vs. InfluxDB vs. JaguarDB vs. Snowflake vs. Teradata

System Properties Comparison Google Cloud Bigtable vs. InfluxDB vs. JaguarDB vs. Snowflake vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

DBMS for storing time series, events and metrics

Performant, highly scalable DBMS for AI and IoT applications

Cloud-based data warehousing service for structured and semi-structured data

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Key-value store
Wide column store

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

cloud.google.com/bigtable

www.influxdata.com/products/influxdb-overview

www.jaguardb.com

www.snowflake.com

www.teradata.com

Technical documentation

cloud.google.com/bigtable/docs

docs.influxdata.com/influxdb

www.jaguardb.com/support.html

docs.snowflake.net/manuals/index.html

docs.teradata.com

Developer

Google

DataJaguar, Inc.

Snowflake Computing Inc.

Teradata

Initial release

2015

2013

2015

2014

1984

Current release

2.7.6, April 2024

3.3 July 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

GPL V3.0

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++

the server part. Clients available in other languages

Server operating systems

hosted

Linux
OS X

through Homebrew

Linux

hosted

hosted
Linux

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL 2016 + extensions

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API
JSON over UDP

JDBC
ODBC

CLI Client
JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

in enterprise version only

Multi-source replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

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

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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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