DBMS > AnzoGraph DB vs. Apache Impala vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Apache Impala 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

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

Primary database model

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document 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	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

impala.apache.org

www.snowflake.com

Technical documentation

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

impala.apache.org/impala-docs.html

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Snowflake Computing Inc.

Initial release

2018

2013

2014

Current release

2.3, January 2021

4.1.0, June 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

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++

Server operating systems

Linux

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

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

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
JDBC
ODBC

Supported programming languages

C++
Java
Python

All languages supporting JDBC/ODBC

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

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

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

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