DBMS > Apache Druid vs. Datomic vs. Google Cloud Datastore vs. Newts vs. Snowflake

System Properties Comparison Apache Druid vs. Datomic vs. Google Cloud Datastore vs. Newts vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

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

Time Series DBMS based on Cassandra

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

Primary database model

Relational DBMS
Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

druid.apache.org

www.datomic.com

cloud.google.com/datastore

opennms.github.io/newts

www.snowflake.com

Technical documentation

druid.apache.org/docs/latest/design

docs.datomic.com

cloud.google.com/datastore/docs

github.com/OpenNMS/newts/wiki

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

Cognitect

Google

OpenNMS Group

Snowflake Computing Inc.

Initial release

2012

2008

2014

Current release

29.0.1, April 2024

1.0.7075, December 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

limited edition free

commercial

Open Source

Apache 2.0

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

Java

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

hosted

Linux
OS X
Windows

hosted

Data scheme

yes

schema-less columns are supported

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

SQL for querying

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP API

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

HTTP REST
Java API

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Clojure
Java

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

Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

using Google App Engine

user defined functions

Triggers

By using transaction functions

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

But extensive use of caching in the application peers

Sharding

based on Cassandra

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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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