DBMS > Blueflood vs. Heroic vs. MaxDB vs. Microsoft Azure Synapse Analytics vs. TerarkDB

System Properties Comparison Blueflood vs. Heroic vs. MaxDB vs. Microsoft Azure Synapse Analytics vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

A robust and reliable RDBMS optimized to run all major SAP solutions

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

blueflood.io

github.com/spotify/heroic

maxdb.sap.com

azure.microsoft.com/services/synapse-analytics

github.com/bytedance/terarkdb

Technical documentation

github.com/rax-maas/blueflood/wiki

spotify.github.io/heroic

maxdb.sap.com/documentation

docs.microsoft.com/azure/synapse-analytics

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Rackspace

Spotify

SAP, acquired from Software AG (Adabas-D) in 1997

Microsoft

ByteDance, originally Terark

Initial release

2013

2014

1984

2016

Current release

7.9.10.12, February 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Limited community edition free

commercial

restricted open source version available

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

C++

Server operating systems

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

predefined scheme

schema-free

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

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

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

ADO.NET
JDBC
ODBC
WebDAV

ADO.NET
JDBC
ODBC

C++ API
Java API

Supported programming languages

.Net
C#
Java
Perl
PHP
Python

C#
Java
PHP

C++
Java

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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