DBMS > Apache Phoenix vs. Google BigQuery vs. Google Cloud Datastore vs. Hive vs. Immudb

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. Google Cloud Datastore vs. Hive vs. Immudb

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Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

data warehouse software for querying and managing large distributed datasets, built on Hadoop

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

Primary database model

Secondary database models

Relational DBMS

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Score	60.38
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Score	4.47
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	#12	Document stores

Score	61.17
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	#12	Relational DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Website

phoenix.apache.org

cloud.google.com/bigquery

cloud.google.com/datastore

hive.apache.org

github.com/codenotary/immudb
immudb.io

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.immudb.io

Developer

Apache Software Foundation

Google

Apache Software Foundation

initially developed by Facebook

Codenotary

Initial release

2014

2010

2008

2012

2020

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.1.3, April 2022

1.2.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
Unix
Windows

hosted

All OS with a Java VM

BSD
Linux
macOS
Solaris
Windows
z/OS

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

SQL-like DML and DDL statements

SQL-like syntax

APIs and other access methods

JDBC

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

gRPC protocol
PostgreSQL wire protocol

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
PHP
Python

.Net
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

yes

user defined functions and integration of map-reduce

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

using Google Cloud Dataflow

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

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