DBMS > Lovefield vs. Microsoft Azure Table Storage vs. SiteWhere vs. Vertica vs. Yaacomo

System Properties Comparison Lovefield vs. Microsoft Azure Table Storage vs. SiteWhere vs. Vertica vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Embeddable relational database for web apps written in pure JavaScript

A Wide Column Store for rapid development using massive semi-structured datasets

M2M integration platform for persisting/querying time series data

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	3.55
Rank	#80	Overall
	#6	Wide column stores

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

google.github.io/lovefield

azure.microsoft.com/en-us/services/storage/tables

github.com/sitewhere/sitewhere

www.vertica.com

yaacomo.com

Technical documentation

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

sitewhere1.sitewhere.io/index.html

vertica.com/documentation

Developer

Google

Microsoft

SiteWhere

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2014

2012

2010

2005

2009

Current release

2.1.12, February 2017

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Common Public Attribution License Version 1.0

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

Java

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

hosted

Linux
OS X
Windows

Linux

Android
Linux
Windows

Data scheme

yes

schema-free

predefined scheme

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

APIs and other access methods

RESTful HTTP API

HTTP REST

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

JavaScript

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

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

Using read-only observers

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

horizontal partitioning, hierarchical partitioning

horizontal partitioning

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.

selectable replication factor

based on HBase

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

User concepts

Access control

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

fine grained access rights according to SQL-standard

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