DBMS > atoti vs. etcd vs. EXASOL vs. Google BigQuery

System Properties Comparison atoti vs. etcd vs. EXASOL vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A distributed reliable key-value store

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

Primary database model

Object oriented DBMS

Key-value store

Relational DBMS

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

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	7.05
Rank	#53	Overall
	#5	Key-value stores

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Website

atoti.io

etcd.io
github.com/etcd-io/etcd

www.exasol.com

cloud.google.com/bigquery

Technical documentation

docs.atoti.io

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.exasol.com/resources

cloud.google.com/bigquery/docs

Developer

ActiveViam

Exasol

Google

Initial release

2000

2010

Current release

3.4, August 2019

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 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

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

APIs and other access methods

gRPC
JSON over HTTP

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

Supported programming languages

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

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

Python

user defined functions

in JavaScript

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

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