DBMS > Apache Impala vs. Google BigQuery vs. Interbase vs. SAP Adaptive Server vs. TimesTen

System Properties Comparison Apache Impala vs. Google BigQuery vs. Interbase vs. SAP Adaptive Server vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Secondary database models

Document store

Spatial DBMS

with Boeing's Spatial Query Server

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Trend Chart

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	3.78
Rank	#77	Overall
	#41	Relational DBMS

Score	32.67
Rank	#25	Overall
	#17	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

impala.apache.org

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

www.sap.com/products/technology-platform/sybase-ase.html

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

help.sap.com/docs/SAP_ASE

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Embarcadero

SAP, Sybase

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2010

1984

1987

1998

Current release

4.1.0, June 2022

InterBase 2020, December 2019

16.0

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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++

C and C++

Server operating systems

Linux

hosted

Android
iOS
Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

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.

export as XML data possible

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

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

Java and Transact-SQL

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Interbase Change Views

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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