DBMS > Apache Impala vs. BigObject vs. Microsoft Azure Cosmos DB vs. SAP IQ vs. Snowflake

System Properties Comparison Apache Impala vs. BigObject vs. Microsoft Azure Cosmos DB vs. SAP IQ vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Analytic DBMS for real-time computations and queries

Globally distributed, horizontally scalable, multi-model database service

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column-oriented

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

impala.apache.org

bigobject.io

azure.microsoft.com/services/cosmos-db

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

docs.bigobject.io

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

BigObject, Inc.

Microsoft

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2013

2015

2014

1994

2014

Current release

4.1.0, June 2022

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community edition 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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

JavaScript

yes

user defined functions

Triggers

JavaScript

yes

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

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Implicit feature of the cloud service

SAP/Sybase Replication Server

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*

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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

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