DBMS > Apache Druid vs. Hive vs. Snowflake vs. TigerGraph

System Properties Comparison Apache Druid vs. Hive vs. Snowflake vs. TigerGraph

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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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Graph DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

cwiki.apache.org/confluence/display/Hive/Home

docs.snowflake.net/manuals/index.html

docs.tigergraph.com

Developer

Apache Software Foundation and contributors

Apache Software Foundation

initially developed by Facebook

Snowflake Computing Inc.

Initial release

2012

2014

2017

Current release

29.0.1, April 2024

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

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

Java

C++

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

hosted

Linux

Data scheme

yes

schema-less columns are supported

yes

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

yes

Typing

predefined data types such as float or date

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 DML and DDL statements

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

CLI Client
JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Java
PHP
Python

JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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.

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

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

Role-based access control

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