DBMS > Databricks vs. Google BigQuery vs. Interbase vs. Snowflake

System Properties Comparison Databricks vs. Google BigQuery vs. Interbase vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

Light-weight proven RDBMS

originally from Borland

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Relational DBMS

Relational DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.databricks.com

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

www.snowflake.com

Technical documentation

docs.databricks.com

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

docs.snowflake.net/manuals/index.html

Developer

Databricks

Google

Embarcadero

Snowflake Computing Inc.

Initial release

2013

2010

1984

2014

Current release

InterBase 2020, December 2019

License

Commercial or Open Source

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.

Implementation language

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

hosted

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

support of semi-structured data formats (JSON, XML, Avro)

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

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Interbase Change Views

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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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