DBMS > DuckDB vs. EXASOL vs. Google BigQuery vs. Microsoft SQL Server vs. RisingWave

System Properties Comparison DuckDB vs. EXASOL vs. Google BigQuery vs. Microsoft SQL Server vs. RisingWave

Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.58
Rank	#242	Overall
	#111	Relational DBMS

Website

duckdb.org

www.exasol.com

cloud.google.com/bigquery

www.microsoft.com/en-us/sql-server

www.risingwave.com/database

Technical documentation

duckdb.org/docs

www.exasol.com/resources

cloud.google.com/bigquery/docs

learn.microsoft.com/en-US/sql/sql-server

docs.risingwave.com/docs/current/intro

Developer

Exasol

Google

Microsoft

RisingWave Labs

Initial release

2018

2000

2010

1989

2022

Current release

0.10, February 2024

SQL Server 2022, November 2022

1.2, September 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

restricted free version is available

Open Source

Apache Version 2.0

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++

Rust

Server operating systems

server-less

hosted

Linux
Windows

Docker
Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

yes

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

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

JDBC
PostgreSQL wire protocol

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

UDFs in Python or Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles according to SQL-standard

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

Users and Roles

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