DBMS > Brytlyt vs. Databricks vs. Google BigQuery

System Properties Comparison Brytlyt vs. Databricks vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Large scale data warehouse service with append-only tables

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.27
Rank	#292	Overall
	#132	Relational DBMS

Score	84.24
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Website

brytlyt.io

www.databricks.com

cloud.google.com/bigquery

Technical documentation

docs.brytlyt.io

docs.databricks.com

cloud.google.com/bigquery/docs

Developer

Brytlyt

Databricks

Google

Initial release

2016

2013

2010

Current release

5.0, August 2023

License

Commercial or Open Source

commercial

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, C++ and CUDA

Server operating systems

Linux
OS X
Windows

hosted

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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.

yes

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with Databricks SQL

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions and aggregates

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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

Supported database models : In addition to the Document store and Relational DBMS...
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