DBMS > AnzoGraph DB vs. Google BigQuery vs. NSDb vs. Sqrrl vs. SwayDB

System Properties Comparison AnzoGraph DB vs. Google BigQuery vs. NSDb vs. Sqrrl vs. SwayDB

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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Large scale data warehouse service with append-only tables

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Adaptable, secure NoSQL built on Apache Accumulo

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Graph DBMS
RDF store

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

cambridgesemantics.com/anzograph

cloud.google.com/bigquery

nsdb.io

sqrrl.com

swaydb.simer.au

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

cloud.google.com/bigquery/docs

nsdb.io/Architecture

Developer

Cambridge Semantics

Google

Amazon

originally Sqrrl Data, Inc.

Simer Plaha

Initial release

2018

2010

2017

2012

2018

Current release

2.3, January 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache Version 2.0

commercial

Open Source

GNU Affero GPL V3.0

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

Java, Scala

Java

Scala

Server operating systems

Linux

hosted

Linux
macOS

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

SQL-like query language

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Scala

Actionscript
C

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

making use of Hadoop

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

making use of Hadoop

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic updates per row, document, or graph entity

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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 and roles

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

Google Cloud Identity & Access Management (IAM)

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.