DBMS > Databend vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics

System Properties Comparison Databend vs. Google Cloud Datastore vs. Microsoft Azure Synapse Analytics

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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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	0.28
Rank	#287	Overall
	#131	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

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

cloud.google.com/datastore

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.databend.com

cloud.google.com/datastore/docs

docs.microsoft.com/azure/synapse-analytics

Developer

Databend Labs

Google

Microsoft

Initial release

2021

2008

2016

Current release

1.0.59, April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Rust

C++

Server operating systems

hosted
Linux
macOS

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
PHP

Server-side scripts

Stored procedures

using Google App Engine

Transact SQL

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Transaction concepts

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

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.

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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