DBMS > Apache Kylin vs. Datomic vs. EXASOL vs. Google Cloud Datastore vs. Snowflake

System Properties Comparison Apache Kylin vs. Datomic vs. EXASOL vs. Google Cloud Datastore vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

kylin.apache.org

www.datomic.com

www.exasol.com

cloud.google.com/datastore

www.snowflake.com

Technical documentation

kylin.apache.org/docs

docs.datomic.com

www.exasol.com/resources

cloud.google.com/datastore/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

Cognitect

Exasol

Google

Snowflake Computing Inc.

Initial release

2015

2012

2000

2008

2014

Current release

3.1.0, July 2020

1.0.6735, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

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

Java

Java, Clojure

Server operating systems

Linux

All OS with a Java VM

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

yes, details here

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
Java

Java
Lua
Python
R

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

using Google App Engine

user defined functions

Triggers

By using transaction functions

yes

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

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