DBMS > Datastax Enterprise vs. Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

System Properties Comparison Datastax Enterprise vs. Lovefield vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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	5.80
Rank	#60	Overall
	#4	Wide column stores

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	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.datastax.com/products/datastax-enterprise

google.github.io/lovefield

azure.microsoft.com/services/data-explorer

azure.microsoft.com/services/synapse-analytics

www.stardog.com

Technical documentation

docs.datastax.com

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

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

docs.microsoft.com/azure/synapse-analytics

docs.stardog.com

Developer

DataStax

Google

Microsoft

Stardog-Union

Initial release

2011

2014

2019

2016

2010

Current release

6.8, April 2020

2.1.12, February 2017

cloud service with continuous releases

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

JavaScript

C++

Java

Server operating systems

Linux
OS X

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

tested with Chrome, Firefox, IE, Safari

hosted

Linux
macOS
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

SQL-like query language

via JavaScript builder pattern

Kusto Query Language (KQL), SQL subset

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

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

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript

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

C#
Java
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Using read-only observers

yes

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

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

none

yes

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

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

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

yes

relationships in graphs

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

User concepts

Access control

Access rights for users can be defined per object

Azure Active Directory Authentication

yes

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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