DBMS > Apache Impala vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. Oracle

System Properties Comparison Apache Impala vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. Oracle

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Globally distributed, horizontally scalable, multi-model database service

Application development environment with integrated database management system

Widely used RDBMS

Primary database model

Relational DBMS

Key-value store
Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Website

impala.apache.org

cloud.google.com/bigtable

azure.microsoft.com/services/cosmos-db

www.progress.com/openedge

www.oracle.com/database

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigtable/docs

learn.microsoft.com/azure/cosmos-db

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/en/database

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Microsoft

Progress Software Corporation

Oracle

Initial release

2013

2015

2014

1984

1980

Current release

4.1.0, June 2022

OpenEdge 12.2, March 2020

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

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++

C and C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

yes

JSON 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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

close to SQL 92

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Progress proprietary ABL (Advanced Business Language)

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

yes

PL/SQL

also stored procedures in Java possible

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Implicit feature of the cloud service

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

isolation level can be parameterized

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.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

Users and groups

fine grained access rights according to SQL-standard

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