DBMS > Apache Doris vs. Datastax Enterprise vs. Google BigQuery vs. Prometheus

System Properties Comparison Apache Doris vs. Datastax Enterprise vs. Google BigQuery vs. Prometheus

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Large scale data warehouse service with append-only tables

Open-source Time Series DBMS and monitoring system

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	0.61
Rank	#235	Overall
	#110	Relational DBMS

Score	4.82
Rank	#65	Overall
	#4	Wide column stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	7.56
Rank	#49	Overall
	#3	Time Series DBMS

Website

doris.apache.org
github.com/apache/doris

www.datastax.com/products/datastax-enterprise

cloud.google.com/bigquery

prometheus.io

Technical documentation

github.com/apache/doris/wiki

docs.datastax.com

cloud.google.com/bigquery/docs

prometheus.io/docs

Developer

Apache Software Foundation, originally contributed from Baidu

DataStax

Google

Initial release

2017

2011

2010

2015

Current release

1.2.2, February 2023

6.8, April 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

Server operating systems

Linux

Linux
OS X

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL); Spark SQL

yes

APIs and other access methods

JDBC
MySQL client

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP/JSON API

Supported programming languages

Java

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

Since BigQuery is designed for querying data

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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