DBMS > Google BigQuery vs. Ignite vs. Kyligence Enterprise vs. Spark SQL vs. Splice Machine

System Properties Comparison Google BigQuery vs. Ignite vs. Kyligence Enterprise vs. Spark SQL vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

cloud.google.com/bigquery

ignite.apache.org

kyligence.io/kyligence-enterprise

spark.apache.org/sql

splicemachine.com

Technical documentation

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

splicemachine.com/how-it-works

Developer

Google

Apache Software Foundation

Kyligence, Inc.

Apache Software Foundation

Splice Machine

Initial release

2010

2015

2016

2014

Current release

Apache Ignite 2.6

3.5.0 ( 2.13), September 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

Java

Scala

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

ANSI SQL for queries (using Apache Calcite)

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Java
Python
R
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

Java

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

yes, multi-version concurrency control (MVCC)

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

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