DBMS > Google BigQuery vs. KairosDB vs. Memgraph vs. Riak KV vs. Teradata

System Properties Comparison Google BigQuery vs. KairosDB vs. Memgraph vs. Riak KV vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Distributed Time Series DBMS based on Cassandra or H2

An open source graph database built for real-time streaming and compatible with Neo4j

Distributed, fault tolerant key-value store

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	3.02
Rank	#98	Overall
	#8	Graph DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

cloud.google.com/bigquery

github.com/kairosdb/kairosdb

memgraph.com

www.teradata.com

Technical documentation

cloud.google.com/bigquery/docs

kairosdb.github.io

memgraph.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

docs.teradata.com

Social network pages

Developer

Google

Memgraph Ltd

OpenSource, formerly Basho Technologies

Teradata

Initial release

2010

2013

2017

2009

1984

Current release

1.2.2, November 2018

3.2.0, December 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSL 1.1; commercial license for enterprise edition available

Open Source

Apache version 2, commercial enterprise edition

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

Java

C and C++

Erlang

Server operating systems

hosted

Linux
OS X
Windows

Linux

Linux
OS X

hosted
Linux

Data scheme

yes

schema-free

schema-free and schema-optional

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

restricted

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

RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

Bolt protocol
Cypher query language

HTTP API
Native Erlang Interface

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

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Erlang

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

dynamic graph partitioning

Sharding

no "single point of failure"

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication using RAFT

selectable replication factor

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

links between data sets can be stored

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with periodic snapshot and write-ahead logging (WAL) of changes

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

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

Users, roles and permissions

yes, using Riak Security

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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Licensing and pricing models

You can check out our pricing model and licenses on the company website .
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