DBMS > Databend vs. eXtremeDB vs. Google BigQuery vs. SAP HANA

System Properties Comparison Databend vs. eXtremeDB vs. Google BigQuery vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Natively in-memory DBMS with options for persistency, high-availability and clustering

Large scale data warehouse service with append-only tables

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	0.79
Rank	#212	Overall
	#100	Relational DBMS
	#18	Time Series DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.mcobject.com

cloud.google.com/bigquery

www.sap.com/products/hana.html

Technical documentation

docs.databend.com

www.mcobject.com/docs/extremedb.htm

cloud.google.com/bigquery/docs

help.sap.com/hana

Developer

Databend Labs

McObject

Google

SAP

Initial release

2021

2001

2010

Current release

1.0.59, April 2023

8.2, 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

C and C++

Server operating systems

hosted
Linux
macOS

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Appliance or cloud-service

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.

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

SQLScript, R

Triggers

yes

by defining events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning / sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

Users with fine-grained authorization concept, user roles

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

Google Cloud Identity & Access Management (IAM)

yes

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