DBMS > Apache Impala vs. GeoMesa vs. Google BigQuery vs. PostGIS

System Properties Comparison Apache Impala vs. GeoMesa vs. Google BigQuery vs. PostGIS

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Large scale data warehouse service with append-only tables

Spatial extension of PostgreSQL

Primary database model

Secondary database models

Document store

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Website

impala.apache.org

www.geomesa.org

cloud.google.com/bigquery

postgis.net

Technical documentation

impala.apache.org/impala-docs.html

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigquery/docs

postgis.net/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

CCRi and others

Google

Initial release

2013

2014

2010

2005

Current release

4.1.0, June 2022

5.0.0, May 2024

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache License 2.0

commercial

Open Source

GPL v2.0

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++

Scala

Server operating systems

Linux

hosted

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

none

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

depending on storage layer

yes

based on PostgreSQL

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

depending on storage layer

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.

depending on storage layer

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

depending on the DBMS used for storage

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

Google Cloud Identity & Access Management (IAM)

yes

based on PostgreSQL

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