DBMS > Google Cloud Firestore vs. Heroic vs. Kyligence Enterprise vs. Microsoft Azure Synapse Analytics

System Properties Comparison Google Cloud Firestore vs. Heroic vs. Kyligence Enterprise vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A distributed analytics engine for big data, built on top of Apache Kylin

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

Primary database model

Document store

Time Series DBMS

Relational DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

firebase.google.com/products/firestore

github.com/spotify/heroic

kyligence.io/kyligence-enterprise

azure.microsoft.com/services/synapse-analytics

Technical documentation

firebase.google.com/docs/firestore

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

Developer

Google

Spotify

Kyligence, Inc.

Microsoft

Initial release

2017

2014

2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Java

C++

Server operating systems

hosted

Linux

hosted

Data scheme

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

via Elasticsearch

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C#
Java
PHP

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Transact SQL

Triggers

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Transaction concepts

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

yes

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. Security Rules for 3rd party authentication using Firebase Auth.

yes

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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