DBMS > Amazon CloudSearch vs. Badger vs. Google BigQuery vs. Teradata

System Properties Comparison Amazon CloudSearch vs. Badger vs. Google BigQuery vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

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

Large scale data warehouse service with append-only tables

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Search engine

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.86
Rank	#133	Overall
	#12	Search engines

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Website

aws.amazon.com/cloudsearch

github.com/dgraph-io/badger

cloud.google.com/bigquery

www.teradata.com

Technical documentation

docs.aws.amazon.com/cloudsearch

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

cloud.google.com/bigquery/docs

docs.teradata.com

Developer

Amazon

DGraph Labs

Google

Teradata

Initial release

2012

2017

2010

1984

Current release

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

hosted

hosted
Linux

Data scheme

yes

schema-free

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

all search fields are automatically indexed

yes

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

SQL

Support of SQL

yes

SQL 2016 + extensions

APIs and other access methods

HTTP API

RESTful HTTP/JSON API

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

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

none

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

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

In-memory capabilities

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

yes

User concepts

Access control

authentication via encrypted signatures

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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