DBMS > Amazon DynamoDB vs. EXASOL vs. GreptimeDB vs. Snowflake vs. Solr

System Properties Comparison Amazon DynamoDB vs. EXASOL vs. GreptimeDB vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Secondary database models

Spatial DBMS

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	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

www.exasol.com/resources

docs.greptime.com

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Amazon

Exasol

Greptime Inc.

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2000

2022

2014

2006

Current release

9.6.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

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

Rust

Java

Server operating systems

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

gRPC
HTTP API
JDBC

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Java
Lua
Python
R

C++
Erlang
Go
Java
JavaScript

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

Python

user defined functions

Java plugins

Triggers

yes

by integration with AWS Lambda

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Hadoop integration

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

optimistic locking

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

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

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

Simple rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

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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Key customers

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Market metrics

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Licensing and pricing models

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