DBMS > Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Realm

System Properties Comparison Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Realm

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Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

Fully managed big data interactive analytics platform

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

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Relational DBMS

column oriented

Relational DBMS

Document store

Secondary database models

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	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

google.github.io/lovefield

azure.microsoft.com/services/data-explorer

azure.microsoft.com/services/synapse-analytics

realm.io

Technical documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

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

docs.microsoft.com/azure/synapse-analytics

realm.io/docs

Developer

Google

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2014

2019

2016

2014

Current release

2.1.12, February 2017

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

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

JavaScript

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Android
Backend: server-less
iOS
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

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

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

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

ADO.NET
JDBC
ODBC

Supported programming languages

JavaScript

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

C#
Java
PHP

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Transact SQL

runs within the applications so server-side scripts are unnecessary

Triggers

Using read-only observers

yes

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

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

In-Memory realm

User concepts

Access control

Azure Active Directory Authentication

yes

More information provided by the system vendor

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More resources

DB-Engines blog posts

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