DBMS > Microsoft Azure Synapse Analytics vs. RavenDB vs. Tarantool vs. Transbase

System Properties Comparison Microsoft Azure Synapse Analytics vs. RavenDB vs. Tarantool vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Document store

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

azure.microsoft.com/services/synapse-analytics

ravendb.net

www.tarantool.io

www.transaction.de/en/products/transbase.html

Technical documentation

docs.microsoft.com/azure/synapse-analytics

ravendb.net/docs

www.tarantool.io/en/doc

www.transaction.de/en/products/transbase/features.html

Developer

Microsoft

Hibernating Rhinos

Transaction Software GmbH

Initial release

2016

2010

2008

1987

Current release

5.4, July 2022

2.10.0, May 2022

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

commercial

free development license

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 and C++

Server operating systems

hosted

Linux
macOS
Raspberry Pi
Windows

BSD
Linux
macOS

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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 (RQL)

Full-featured ANSI SQL support

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Open binary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C#
Java
PHP

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

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Transact SQL

yes

Lua, C and SQL stored procedures

yes

Triggers

yes

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate 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

ACID

ACID, Cluster-wide transaction available

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

Authorization levels configured per client per database

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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