DBMS > Amazon DynamoDB vs. GeoMesa vs. Google Cloud Datastore vs. Heroic vs. SAP Advantage Database Server

System Properties Comparison Amazon DynamoDB vs. GeoMesa vs. Google Cloud Datastore vs. Heroic vs. SAP Advantage Database Server

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Low-cost RDBMS with access to ISAM and FoxPro data structures

Primary database model

Document store
Key-value store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

aws.amazon.com/dynamodb

www.geomesa.org

cloud.google.com/datastore

github.com/spotify/heroic

www.sap.com/products/advantage-database-server.html

Technical documentation

docs.aws.amazon.com/dynamodb

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/datastore/docs

spotify.github.io/heroic

Developer

Amazon

CCRi and others

Google

Spotify

Sybase, SAP

Initial release

2012

2014

2008

2014

1993

Current release

4.0.5, February 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache License 2.0

commercial

Open Source

Apache 2.0

commercial

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.

Implementation language

Scala

Java

Server operating systems

hosted

Linux
Windows

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

RESTful HTTP API

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

Delphi
Perl
PHP

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

Multi-source replication using Paxos

yes

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

depending on storage layer

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.

Eventual Consistency
Immediate Consistency

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

ACID across one or more tables within a single AWS account and region

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.

depending on storage layer

caching of (temporary) tables can be configured

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

depending on the DBMS used for storage

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

fine grained access rights according to SQL-standard

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