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

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

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

A managed, cloud-native vector database

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Vector DBMS

Relational DBMS

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	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

cloud.google.com/bigquery

www.gridgain.com

www.pinecone.io

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

docs.pinecone.io/docs/overview

help.sap.com/hana

Developer

Google

GridGain Systems, Inc.

Pinecone Systems, Inc

SAP

Initial release

2010

2007

2019

2010

Current release

GridGain 8.5.1

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

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

Java, C++, .Net

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

Appliance or cloud-service

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

yes

APIs and other access methods

RESTful HTTP/JSON API

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

RESTful HTTP API

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

SQLScript, R

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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)

Security Hooks for custom implementations

yes

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