DBMS > Badger vs. Brytlyt vs. FileMaker vs. Google Cloud Datastore vs. SAP HANA

System Properties Comparison Badger vs. Brytlyt vs. FileMaker vs. Google Cloud Datastore vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

github.com/dgraph-io/badger

brytlyt.io

www.claris.com/filemaker

cloud.google.com/datastore

www.sap.com/products/hana.html

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.brytlyt.io

www.claris.com/resources/documentation

cloud.google.com/datastore/docs

help.sap.com/hana

Developer

DGraph Labs

Brytlyt

Claris

a subsidiary of Apple

Google

SAP

Initial release

2017

2016

1983

2008

2010

Current release

5.0, August 2023

19.4.1, November 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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

C, C++ and CUDA

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

iOS

client part only
Linux
OS X
Windows

hosted

Appliance or cloud-service

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

specific XML-type available, but no XML query functionality.

yes

A http query request can return the data in XML format

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

SQL-like query language (GQL)

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Filemaker WebDirect
JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

using Google App Engine

SQLScript, R

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Source-replica replication, warm standby

since Version 14

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

fine grained access rights according to SQL-standard

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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