DBMS > Amazon DynamoDB vs. Apache Druid vs. BigObject vs. GridGain vs. IBM Db2 Event Store

System Properties Comparison Amazon DynamoDB vs. Apache Druid vs. BigObject vs. GridGain vs. IBM Db2 Event Store

Editorial information provided by DB-Engines

Name

Description

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Analytic DBMS for real-time computations and queries

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

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store
Key-value store

Relational DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store
Relational DBMS

Event Store
Time Series 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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

aws.amazon.com/dynamodb

druid.apache.org

bigobject.io

www.gridgain.com

www.ibm.com/products/db2-event-store

Technical documentation

docs.aws.amazon.com/dynamodb

druid.apache.org/docs/latest/design

docs.bigobject.io

www.gridgain.com/docs/index.html

www.ibm.com/docs/en/db2-event-store

Developer

Amazon

Apache Software Foundation and contributors

BigObject, Inc.

GridGain Systems, Inc.

IBM

Initial release

2012

2015

2007

2017

Current release

29.0.1, April 2024

GridGain 8.5.1

2.0

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache license v2

commercial

free community edition available

commercial

free developer edition available

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

Java

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Unix

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

schema-less columns are supported

yes

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

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

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

yes

through the embedded Spark runtime

APIs and other access methods

RESTful HTTP API

JDBC
RESTful HTTP/JSON API

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

Lua

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

yes

Triggers

yes

by integration with AWS Lambda

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

none

yes (replicated cache)

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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)

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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