DBMS > AgensGraph vs. Apache Impala vs. FeatureBase vs. Microsoft Azure Cosmos DB vs. Snowflake

System Properties Comparison AgensGraph vs. Apache Impala vs. FeatureBase vs. Microsoft Azure Cosmos DB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Analytic DBMS for Hadoop

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

bitnine.net/agensgraph

impala.apache.org

www.featurebase.com

azure.microsoft.com/services/cosmos-db

www.snowflake.com

Technical documentation

bitnine.net/documentation

impala.apache.org/impala-docs.html

docs.featurebase.com

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Bitnine Global Inc.

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Molecula and Pilosa Open Source Contributors

Microsoft

Snowflake Computing Inc.

Initial release

2016

2013

2017

2014

Current release

2.1, December 2018

4.1.0, June 2022

2022, May 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

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
OS X
Windows

Linux

Linux
macOS

hosted

Data scheme

depending on used data model

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL queries

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC

gRPC
JDBC
Kafka Connector
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

CLI Client
JDBC
ODBC

Supported programming languages

C
Java
JavaScript
Python

All languages supporting JDBC/ODBC

Java
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

user defined functions

Triggers

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights can be defined down to the item level

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

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.