DBMS > Adabas vs. Apache Impala vs. GigaSpaces vs. Google BigQuery vs. Machbase Neo

System Properties Comparison Adabas vs. Apache Impala vs. GigaSpaces vs. Google BigQuery vs. Machbase Neo

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Analytic DBMS for Hadoop

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

TimeSeries DBMS for AIoT and BigData

Primary database model

Multivalue DBMS

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Graph DBMS
Search engine

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Trend Chart

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

impala.apache.org

www.gigaspaces.com

cloud.google.com/bigquery

machbase.com

Technical documentation

impala.apache.org/impala-docs.html

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

machbase.com/dbms

Developer

Software AG

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Gigaspaces Technologies

Google

Machbase

Initial release

1971

2013

2000

2010

2013

Current release

4.1.0, June 2022

15.5, September 2020

V8.0, August 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2; Commercial licenses available

commercial

free test version available

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++

Java, C++, .Net

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

Linux
macOS
Solaris
Windows

hosted

Linux
macOS
Windows

Data scheme

yes

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like DML and DDL statements

SQL-99 for query and DML statements

yes

SQL-like query language

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

Natural

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

in Natural

yes

user defined functions and integration of map-reduce

yes

user defined functions

in JavaScript

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

selectable replication factor

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

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

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

volatile and lookup table

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

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