DBMS > AnzoGraph DB vs. Apache Impala vs. Couchbase vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Apache Impala vs. Couchbase vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for Hadoop

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Primary database model

Secondary database models

Document store

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cambridgesemantics.com/anzograph

impala.apache.org

www.couchbase.com

www.snowflake.com

Technical documentation

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

impala.apache.org/impala-docs.html

docs.couchbase.com

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Couchbase, Inc.

Snowflake Computing Inc.

Initial release

2018

2013

2011

2014

Current release

2.3, January 2021

4.1.0, June 2022

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

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++

C, C++, Go and Erlang

Server operating systems

Linux

Linux
OS X
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

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

SQL

Support of SQL

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

SQL-like DML and DDL statements

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

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

JDBC
ODBC

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

All languages supporting JDBC/ODBC

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

user defined functions

Triggers

yes

via the TAP protocol

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Automatic Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

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.

yes

Ephemeral buckets

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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

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