DBMS > Amazon DocumentDB vs. Apache Drill vs. Google BigQuery vs. ScyllaDB

System Properties Comparison Amazon DocumentDB vs. Apache Drill vs. Google BigQuery vs. ScyllaDB

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Large scale data warehouse service with append-only tables

Cassandra and DynamoDB compatible wide column store

Primary database model

Document store

Document store
Relational DBMS

Relational DBMS

Wide column store

Secondary database models

Key-value store

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.08
Rank	#76	Overall
	#5	Wide column stores

Website

aws.amazon.com/documentdb

drill.apache.org

cloud.google.com/bigquery

www.scylladb.com

Technical documentation

aws.amazon.com/documentdb/resources

drill.apache.org/docs

cloud.google.com/bigquery/docs

docs.scylladb.com

Developer

Apache Software Foundation

Google

ScyllaDB

Initial release

2019

2012

2010

2015

Current release

1.20.3, January 2023

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

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

C++

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux

Data scheme

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.

Secondary indexes

yes

cluster global secondary indices

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

SQL-like DML and DDL statements (CQL)

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Proprietary protocol (CQL)

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

yes, Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

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

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

in-memory tables

User concepts

Access control

Access rights for users and roles

Depending on the underlying data source

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

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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