DBMS > Faircom EDGE vs. SAP HANA vs. Splice Machine vs. Sqrrl vs. TDengine

System Properties Comparison Faircom EDGE vs. SAP HANA vs. Splice Machine vs. Sqrrl vs. TDengine

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

In-memory, column based data store. Available as appliance or cloud service

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Adaptable, secure NoSQL built on Apache Accumulo

Time Series DBMS and big data platform

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

www.faircom.com/products/faircom-edge

www.sap.com/products/hana.html

splicemachine.com

sqrrl.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

help.sap.com/hana

splicemachine.com/how-it-works

docs.tdengine.com

Developer

FairCom Corporation

SAP

Splice Machine

Amazon

originally Sqrrl Data, Inc.

TDEngine, previously Taos Data

Initial release

1979

2010

2014

2012

2019

Current release

V3, October 2020

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

3.0, August 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Open Source

AGPL V3, also commercial editions available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Appliance or cloud-service

Linux
OS X
Solaris
Windows

Linux

Linux
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL 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

SQL

Support of SQL

yes

ANSI SQL queries

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

JDBC
RESTful HTTP API

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

SQLScript, R

yes

Java

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

Multi-source replication
Source-replica replication

selectable replication factor

making use of Hadoop

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

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

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

yes

Access rights for users, groups and roles according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

yes

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Market metrics

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Licensing and pricing models

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