DBMS > atoti vs. BigObject vs. Heroic

System Properties Comparison atoti vs. BigObject vs. Heroic

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Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for real-time computations and queries

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Object oriented DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

atoti.io

bigobject.io

github.com/spotify/heroic

Technical documentation

docs.atoti.io

docs.bigobject.io

spotify.github.io/heroic

Developer

ActiveViam

BigObject, Inc.

Spotify

Initial release

2015

2014

License

Commercial or Open Source

commercial

free versions available

commercial

free community edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Data scheme

yes

schema-free

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

via Elasticsearch

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like DML and DDL statements

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Server-side scripts

Stored procedures

Python

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Read/write lock on objects (tables, trees)

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

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More resources

Recent citations in the news

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28 November 2023, Risk.net

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