DBMS > Google BigQuery vs. HEAVY.AI vs. Kyligence Enterprise vs. Lovefield vs. Teradata Aster

System Properties Comparison Google BigQuery vs. HEAVY.AI vs. Kyligence Enterprise vs. Lovefield vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A distributed analytics engine for big data, built on top of Apache Kylin

Embeddable relational database for web apps written in pure JavaScript

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

kyligence.io/kyligence-enterprise

google.github.io/lovefield

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Google

HEAVY.AI, Inc.

Kyligence, Inc.

Google

Teradata

Initial release

2010

2016

2014

2005

Current release

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache 2.0

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

C++ and CUDA

Java

JavaScript

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

R packages

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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