DBMS > Google Cloud Firestore vs. Hazelcast vs. Microsoft Azure Data Explorer vs. RocksDB

System Properties Comparison Google Cloud Firestore vs. Hazelcast vs. Microsoft Azure Data Explorer vs. RocksDB

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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.

A widely adopted in-memory data grid

Fully managed big data interactive analytics platform

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

column oriented

Secondary database models

Document store

JSON support with IMDG 3.12

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

firebase.google.com/products/firestore

hazelcast.com

azure.microsoft.com/services/data-explorer

rocksdb.org

Technical documentation

firebase.google.com/docs/firestore

hazelcast.org/imdg/docs

docs.microsoft.com/en-us/azure/data-explorer

github.com/facebook/rocksdb/wiki

Developer

Google

Hazelcast

Microsoft

Facebook, Inc.

Initial release

2017

2008

2019

2013

Current release

5.3.6, November 2023

cloud service with continuous releases

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

BSD

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

Java

C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux

Data scheme

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

C++ API
Java API

Supported programming languages

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Event Listeners, Executor Services

Yes, possible languages: KQL, Python, R

Triggers

yes, with Cloud Functions

yes

Events

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Replicated Map

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

one or two-phase-commit; repeatable reads; read commited

yes

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.

yes

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.

Role-based access control

Azure Active Directory Authentication

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