DBMS > Amazon Neptune vs. Databricks vs. Microsoft Azure Synapse Analytics vs. Riak TS vs. Sqrrl

System Properties Comparison Amazon Neptune vs. Databricks vs. Microsoft Azure Synapse Analytics vs. Riak TS vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Graph DBMS
RDF store

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

aws.amazon.com/neptune

www.databricks.com

azure.microsoft.com/services/synapse-analytics

sqrrl.com

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.databricks.com

docs.microsoft.com/azure/synapse-analytics

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Amazon

Databricks

Microsoft

Open Source, formerly Basho Technologies

Amazon

originally Sqrrl Data, Inc.

Initial release

2017

2013

2016

2015

2012

Current release

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

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

C++

Erlang

Java

Server operating systems

hosted

Linux
OS X

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

with Databricks SQL

yes

yes, limited

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Python
R
Scala

C#
Java
PHP

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Relationships in graphs

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

links between datasets can be stored

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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