DBMS > Apache Impala vs. Hazelcast vs. Interbase vs. Microsoft Azure Cosmos DB vs. Snowflake

System Properties Comparison Apache Impala vs. Hazelcast vs. Interbase vs. Microsoft Azure Cosmos DB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A widely adopted in-memory data grid

Light-weight proven RDBMS

originally from Borland

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	4.08
Rank	#75	Overall
	#41	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

impala.apache.org

hazelcast.com

www.embarcadero.com/products/interbase

azure.microsoft.com/services/cosmos-db

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

hazelcast.org/imdg/docs

docs.embarcadero.com/products/interbase

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hazelcast

Embarcadero

Microsoft

Snowflake Computing Inc.

Initial release

2013

2008

1984

2014

Current release

4.1.0, June 2022

5.3.6, November 2023

InterBase 2020, December 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

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

the object must implement a serialization strategy

export as XML data possible

yes

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

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

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

.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

yes

Event Listeners, Executor Services

yes

Interbase procedure and trigger language

JavaScript

user defined functions

Triggers

yes

Events

yes

JavaScript

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Replicated Map

Interbase Change Views

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate 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

one or two-phase-commit; repeatable reads; read commited

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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

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