DBMS > Amazon Redshift vs. BigObject vs. NSDb vs. Pinecone

System Properties Comparison Amazon Redshift vs. BigObject vs. NSDb vs. Pinecone

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A managed, cloud-native vector database

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Vector DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Website

aws.amazon.com/redshift

bigobject.io

nsdb.io

www.pinecone.io

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

nsdb.io/Architecture

docs.pinecone.io/docs/overview

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Pinecone Systems, Inc

Initial release

2012

2015

2017

2019

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 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

Java, Scala

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
macOS

hosted

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

String, Number, Boolean

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

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Scala

Python

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

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