DBMS > Apache Phoenix vs. Databricks vs. TigerGraph vs. Tkrzw

System Properties Comparison Apache Phoenix vs. Databricks vs. TigerGraph vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Document store
Relational DBMS

Graph DBMS

Key-value store

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

Technical documentation

phoenix.apache.org

docs.databricks.com

docs.tigergraph.com

Developer

Apache Software Foundation

Databricks

Mikio Hirabayashi

Initial release

2014

2013

2017

2020

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

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

C++

Server operating systems

Linux
Unix
Windows

hosted

Linux

Linux
macOS

Data scheme

yes

late-bound, schema-on-read capabilities

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

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

yes

with Databricks SQL

SQL-like query language (GSQL)

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Python
R
Scala

C++
Java

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or 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.

yes

using specific database classes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Role-based access control

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

show all

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

show all

Recent citations in the news

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix | Amazon Web Services
2 June 2016, AWS Blog

Bridge the SQL-NoSQL gap with Apache Phoenix
4 February 2016, InfoWorld

Apache Calcite, FreeMarker, Gora, Phoenix, and Solr updated
27 March 2017, SDTimes.com

Azure HDInsight Analytics Platform Now Supports Apache Hadoop 3.0
18 April 2019, eWeek

Deep dive into Azure HDInsight 4.0
25 September 2018, Microsoft

provided by Google News

Why Databricks' Tabular Play Has Put Snowflake On The Defensive
10 June 2024, Forbes

Informatica rolls out new integrations for Databricks’ cloud data platform
10 June 2024, SiliconANGLE News

Snowflake, DataBricks and the Fight for Apache Iceberg Tables
10 June 2024, The New Stack

Exclusive | Databricks to Buy Data-Management Startup Tabular in Bid for AI Clients
4 June 2024, The Wall Street Journal

Databricks acquires Tabular to build a common data lakehouse standard
4 June 2024, TechCrunch

provided by Google News

TigerGraph Unveils CoPilot, Version 4.0, and New CEO
30 April 2024, Datanami

TigerGraph unveils GenAI assistant, introduces new CEO
30 April 2024, TechTarget

How TigerGraph CoPilot enables graph-augmented AI
30 April 2024, InfoWorld

TigerGraph Bolsters DB for Enterprise Graph Workloads
1 November 2023, Datanami

Aerospike takes on Neo4j and TigerGraph with launch of graph database
20 June 2023, SiliconANGLE News

provided by Google News

Share this page