DBMS > Apache Pinot vs. Bangdb vs. EventStoreDB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Pinot vs. Bangdb vs. EventStoreDB vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Converged and high performance database for device data, events, time series, document and graph

Industrial-strength, open-source database solution built from the ground up for event sourcing.

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Event Store

Wide column store

Secondary database models

Spatial DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Website

pinot.apache.org

bangdb.com

www.eventstore.com

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

docs.pinot.apache.org

docs.bangdb.com

developers.eventstore.com

Developer

Apache Software Foundation and contributors

Sachin Sinha, BangDB

Event Store Limited

Microsoft

Initial release

2015

2012

Current release

1.0.0, September 2023

BangDB 2.0, October 2021

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

Open Source

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, C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

Linux
Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

RESTful HTTP API

Supported programming languages

Go
Java
Python

C
C#
C++
Java
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

Access rights based on private key authentication or shared access signatures

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More resources

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