DBMS > Google BigQuery vs. Microsoft Azure Synapse Analytics vs. OpenMLDB vs. Snowflake vs. Solr

System Properties Comparison Google BigQuery vs. Microsoft Azure Synapse Analytics vs. OpenMLDB vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

An open-source machine learning database that provides a feature platform for training and inference

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

openmldb.ai

www.snowflake.com

solr.apache.org

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

openmldb.ai/docs/zh/main

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Google

Microsoft

4 Paradigm Inc.

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2010

2016

2020

2014

2006

Current release

2024-2 February 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

Apache Version 2

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++, Java, Scala

Java

Server operating systems

hosted

Linux

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Fixed schema

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

SQL

Support of SQL

yes

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
SQLAlchemy

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

C++
Go
Java
Python
Scala

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

user defined functions

Java plugins

Triggers

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

horizontal partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

optimistic locking

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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