DBMS > Apache Impala vs. etcd vs. Google BigQuery vs. IBM Cloudant

System Properties Comparison Apache Impala vs. etcd vs. Google BigQuery vs. IBM Cloudant

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

Primary database model

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	2.85
Rank	#95	Overall
	#18	Document stores

Website

impala.apache.org

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

cloud.google.com/bigquery

www.ibm.com/products/cloudant

Technical documentation

impala.apache.org/impala-docs.html

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

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2013

2010

Current release

4.1.0, June 2022

3.4, August 2019

License

Commercial or Open Source

Open Source

Apache Version 2

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

C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

hosted

Data scheme

yes

schema-free

yes

schema-free

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

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

gRPC
JSON over HTTP

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

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