DBMS > BigchainDB vs. IRONdb vs. SAP HANA vs. Vertica vs. Yaacomo

System Properties Comparison BigchainDB vs. IRONdb vs. SAP HANA vs. Vertica vs. Yaacomo

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

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

Primary database model

Column oriented

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

www.bigchaindb.com

www.circonus.com/solutions/time-series-database/

www.sap.com/products/hana.html

www.vertica.com

yaacomo.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.circonus.com/irondb/category/getting-started

help.sap.com/hana

vertica.com/documentation

Developer

Circonus LLC.

SAP

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2016

2017

2010

2005

2009

Current release

V0.10.20, January 2018

2.0 SPS07 (April 4, 2023), April 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Python

C and C++

C++

Server operating systems

Linux

Appliance or cloud-service

Linux

Android
Linux
Windows

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

APIs and other access methods

CLI Client
RESTful HTTP API

HTTP API

JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Server-side scripts

Stored procedures

yes, in Lua

SQLScript, R

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

yes

horizontal partitioning, hierarchical partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

configurable replication factor, datacenter aware

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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