DBMS > Cloudflare Workers KV vs. FileMaker vs. GridGain vs. Microsoft SQL Server vs. Teradata Aster

System Properties Comparison Cloudflare Workers KV vs. FileMaker vs. GridGain vs. Microsoft SQL Server vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.32
Rank	#283	Overall
	#40	Key-value stores

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

www.cloudflare.com/developer-platform/workers-kv

www.claris.com/filemaker

www.gridgain.com

www.microsoft.com/en-us/sql-server

Technical documentation

developers.cloudflare.com/kv/api

www.claris.com/resources/documentation

www.gridgain.com/docs/index.html

learn.microsoft.com/en-US/sql/sql-server

Developer

Cloudflare

Claris

a subsidiary of Apple

GridGain Systems, Inc.

Microsoft

Teradata

Initial release

2018

1983

2007

1989

2005

Current release

19.4.1, November 2021

GridGain 8.5.1

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

schema-free

yes

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

A http query request can return the data in XML format

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HTTP REST
Proprietary protocol

Filemaker WebDirect
JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

PHP

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

R packages

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

In-memory capabilities

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

yes

User concepts

Access control

simple rights management via user accounts and connection to external directory services

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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