DBMS > Google BigQuery vs. Pinecone vs. SAP HANA vs. VictoriaMetrics

System Properties Comparison Google BigQuery vs. Pinecone vs. SAP HANA vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A managed, cloud-native vector database

In-memory, column based data store. Available as appliance or cloud service

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Website

cloud.google.com/bigquery

www.pinecone.io

www.sap.com/products/hana.html

victoriametrics.com

Technical documentation

cloud.google.com/bigquery/docs

docs.pinecone.io/docs/overview

help.sap.com/hana

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Google

Pinecone Systems, Inc

SAP

VictoriaMetrics

Initial release

2010

2019

2010

2018

Current release

2.0 SPS07 (April 4, 2023), April 2023

v1.91, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Server operating systems

hosted

Appliance or cloud-service

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

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