DBMS > Databricks vs. Google Cloud Spanner vs. Microsoft Azure Synapse Analytics vs. SAP IQ vs. Snowflake

System Properties Comparison Databricks vs. Google Cloud Spanner vs. Microsoft Azure Synapse Analytics vs. SAP IQ vs. Snowflake

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.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

Document store
Relational DBMS

Relational DBMS

column-oriented

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	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.databricks.com

cloud.google.com/spanner

azure.microsoft.com/services/synapse-analytics

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

docs.databricks.com

cloud.google.com/spanner/docs

docs.microsoft.com/azure/synapse-analytics

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

Databricks

Google

Microsoft

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2013

2017

2016

1994

2014

Current release

16.1 SPS04, April 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

C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Python
R
Scala

Go
Java
JavaScript (Node.js)
Python

C#
Java
PHP

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication with 3 replicas for regional instances.

yes

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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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