DBMS > Apache Impala vs. Faircom EDGE vs. Google BigQuery vs. Hive

System Properties Comparison Apache Impala vs. Faircom EDGE vs. Google BigQuery vs. Hive

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

impala.apache.org

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

hive.apache.org

Technical documentation

impala.apache.org/impala-docs.html

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FairCom Corporation

Google

Apache Software Foundation

initially developed by Facebook

Initial release

2013

1979

2010

2012

Current release

4.1.0, June 2022

V3, October 2020

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

ANSI C, C++

Java

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

hosted

All OS with a Java VM

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL-like DML and DDL statements

yes

ANSI SQL queries

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

across SQL and NoSQL

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

based on Apache Sentry and Kerberos

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

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