DBMS > Badger vs. Google BigQuery vs. Snowflake vs. TimesTen

System Properties Comparison Badger vs. Google BigQuery vs. Snowflake vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Large scale data warehouse service with append-only tables

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

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store

Relational DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

github.com/dgraph-io/badger

cloud.google.com/bigquery

www.snowflake.com

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

cloud.google.com/bigquery/docs

docs.snowflake.net/manuals/index.html

docs.oracle.com/database/timesten-18.1

Developer

DGraph Labs

Google

Snowflake Computing Inc.

Oracle, TimesTen Performance Software, HP

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

Initial release

2017

2010

2014

1998

Current release

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache 2.0

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

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

Data scheme

schema-free

yes

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

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

PL/SQL

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

by means of logfiles and checkpoints

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

Google Cloud Identity & Access Management (IAM)

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

fine grained access rights according to SQL-standard

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