DBMS > Apache IoTDB vs. Google BigQuery vs. InterSystems Caché vs. SAP HANA

System Properties Comparison Apache IoTDB vs. Google BigQuery vs. InterSystems Caché vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

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

Primary database model

Time Series DBMS

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/bigquery

www.intersystems.com/products/cache

www.sap.com/products/hana.html

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

cloud.google.com/bigquery/docs

docs.intersystems.com

help.sap.com/hana

Developer

Apache Software Foundation

Google

InterSystems

SAP

Initial release

2018

2010

1997

2010

Current release

1.1.0, April 2023

2018.1.4, May 2020

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Appliance or cloud-service

Data scheme

yes

depending on used data model

yes

Typing

predefined data types such as float or date

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

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

yes

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

yes

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