DBMS > Google BigQuery vs. Kdb vs. Newts vs. ScyllaDB

System Properties Comparison Google BigQuery vs. Kdb vs. Newts vs. ScyllaDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

High performance Time Series DBMS

Time Series DBMS based on Cassandra

Cassandra and DynamoDB compatible wide column store

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Time Series DBMS

Wide column store

Secondary database models

Relational DBMS

Key-value store

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Website

cloud.google.com/bigquery

kx.com

opennms.github.io/newts

www.scylladb.com

Technical documentation

cloud.google.com/bigquery/docs

code.kx.com

github.com/OpenNMS/newts/wiki

docs.scylladb.com

Developer

Google

Kx Systems, a division of First Derivatives plc

OpenNMS Group

ScyllaDB

Initial release

2010

2000

kdb was released 2000, kdb+ in 2003

2014

2015

Current release

3.6, May 2018

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

commercial

free 32-bit version

Open Source

Apache 2.0

Open Source

Open Source (AGPL), commercial license available

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

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

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

table attribute 'grouped'

yes

cluster global secondary indices

SQL

Support of SQL

yes

SQL-like query language (q)

SQL-like DML and DDL statements (CQL)

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP REST
Java API

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Java

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

yes, Lua

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

based on Cassandra

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

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

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

upon request
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ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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