DBMS > Apache Pinot vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. NSDb vs. WakandaDB

System Properties Comparison Apache Pinot vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. NSDb vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

pinot.apache.org

www.geomesa.org

azure.microsoft.com/services/synapse-analytics

nsdb.io

wakanda.github.io

Technical documentation

docs.pinot.apache.org

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

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

wakanda.github.io/doc

Developer

Apache Software Foundation and contributors

CCRi and others

Microsoft

Wakanda SAS

Initial release

2015

2014

2016

2017

2012

Current release

1.0.0, September 2023

4.0.5, February 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended commercial license 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

Scala

C++

Java, Scala

C++, JavaScript

Server operating systems

All OS with a Java JDK11 or higher

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

Go
Java
Python

C#
Java
PHP

Java
Scala

JavaScript

Server-side scripts

Stored procedures

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

depending on storage layer

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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.

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Recent citations in the news

Real-Time Analytics for Mobile App Crashes using Apache Pinot
2 November 2023, Uber

Speed of Apache Pinot at the Cost of Cloud Object Storage with Tiered Storage
16 August 2023, InfoQ.com

StarTree Announces Integration between Apache Pinot and Delta Lake with StarTree Cloud
20 June 2023, Datanami

StarTree brings Apache Pinot real-time database to the cloud
22 March 2022, TechTarget

Data analytics startup StarTree secures cash to expand its Apache Pinot-powered platform
29 August 2022, TechCrunch

provided by Google News

Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT | Amazon Web Services
18 October 2023, AWS Blog

Azure Synapse vs. Databricks: Data Platform Comparison 2024
26 March 2024, eWeek

Azure Synapse Runtime for Apache Spark 3.2 End of Support | Azure updates
22 March 2024, Microsoft

Azure Synapse Analytics: Everything you need to know about Microsoft's cloud analytics platform
24 September 2023, DataScientest

Azure Synapse Link for Cosmos DB: New Analytics Capabilities
10 November 2023, InfoQ.com

provided by Google News

Share this page