DBMS > Google BigQuery vs. jBASE vs. Kinetica vs. QuestDB

System Properties Comparison Google BigQuery vs. jBASE vs. Kinetica vs. QuestDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A robust multi-value DBMS comprising development tools and middleware

Fully vectorized database across both GPUs and CPUs

A high performance open source SQL database for time series data

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

cloud.google.com/bigquery

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.kinetica.com

questdb.io

Technical documentation

cloud.google.com/bigquery/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.kinetica.com

questdb.io/docs

Developer

Google

Rocket Software (formerly Zumasys)

Kinetica

QuestDB Technology Inc

Initial release

2010

1991

2012

2014

Current release

5.7

7.1, August 2021

License

Commercial or Open Source

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.

Implementation language

C, C++

Java (Zero-GC), C++, Rust

Server operating systems

hosted

AIX
Linux
Windows

Linux

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

Typing

predefined data types such as float or date

yes

optional

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

Embedded SQL for jBASE in BASIC

SQL-like DML and DDL statements

SQL with time-series extensions

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC
RESTful HTTP API

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Basic
Jabbascript
Java

C++
Java
JavaScript (Node.js)
Python

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Source-replica replication with eventual consistency

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID for single-table writes

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

GPU vRAM or System RAM

yes

through memory mapped files

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

News

QuestDB and Raspberry Pi 5 benchmark, a pocket-sized powerhouse
8 May 2024

Build your own resource monitor with QuestDB and Grafana
6 May 2024

Does "vpmovzxbd" Scare You? Here's Why it Doesn't Have To
12 April 2024

Create an ADS-B flight radar with QuestDB and a Raspberry Pi
8 April 2024

Build a temperature IoT sensor with Raspberry Pi Pico & QuestDB
5 April 2024

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.