DBMS > Google Cloud Bigtable vs. Heroic vs. Microsoft Azure Synapse Analytics vs. YottaDB

System Properties Comparison Google Cloud Bigtable vs. Heroic vs. Microsoft Azure Synapse Analytics vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

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

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

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

A fast and solid embedded Key-value store

Primary database model

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

cloud.google.com/bigtable

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

yottadb.com

Technical documentation

cloud.google.com/bigtable/docs

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

yottadb.com/resources/documentation

Developer

Google

Spotify

Microsoft

YottaDB, LLC

Initial release

2015

2014

2016

2001

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0

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

hosted

Docker
Linux

Data 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

by using the Octo plugin

APIs and other access methods

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

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

ADO.NET
JDBC
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

C#
Java
PHP

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

yes

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)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Atomic single-row operations

ACID

optimistic locking

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

yes

Users and groups based on OS-security mechanisms

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