DBMS > Apache Pinot vs. atoti vs. Google Cloud Bigtable vs. Yaacomo

System Properties Comparison Apache Pinot vs. atoti vs. Google Cloud Bigtable vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Website

pinot.apache.org

atoti.io

cloud.google.com/bigtable

yaacomo.com

Technical documentation

docs.pinot.apache.org

docs.atoti.io

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation and contributors

ActiveViam

Google

Q2WEB GmbH

Initial release

2015

2009

Current release

1.0.0, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free versions available

commercial

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

Server operating systems

All OS with a Java JDK11 or higher

hosted

Android
Linux
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Multidimensional Expressions (MDX)

yes

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC

Supported programming languages

Go
Java
Python

C#
C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding, horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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More resources

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27 January 2022, TechTarget

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6 May 2015, VentureBeat

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