DBMS > Coveo vs. Google BigQuery vs. Newts vs. ScyllaDB vs. TimesTen

System Properties Comparison Coveo vs. Google BigQuery vs. Newts vs. ScyllaDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Large scale data warehouse service with append-only tables

Time Series DBMS based on Cassandra

Cassandra and DynamoDB compatible wide column store

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Key-value store

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

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.coveo.com

cloud.google.com/bigquery

opennms.github.io/newts

www.scylladb.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.coveo.com

cloud.google.com/bigquery/docs

github.com/OpenNMS/newts/wiki

docs.scylladb.com

docs.oracle.com/database/timesten-18.1

Developer

Coveo

Google

OpenNMS Group

ScyllaDB

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2010

2014

2015

1998

Current release

ScyllaDB Open Source 5.4.1, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Open Source (AGPL), commercial license available

commercial

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.

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Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

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.

Secondary indexes

yes

cluster global secondary indices

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL)

yes

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST
Java API

Proprietary protocol (CQL)

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C#
Java
JavaScript
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, Lua

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Since BigQuery is designed for querying data

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

in-memory tables

yes

User concepts

Access control

granular access controls, API key management, content filters

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

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