DBMS > Apache Drill vs. Realm vs. Splice Machine

System Properties Comparison Apache Drill vs. Realm vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Primary database model

Document store
Relational DBMS

Document store

Relational DBMS

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

Score	1.82
Rank	#128	Overall
	#24	Document stores
	#60	Relational DBMS

Score	7.12
Rank	#48	Overall
	#8	Document stores

Score	0.54
Rank	#242	Overall
	#112	Relational DBMS

Website

drill.apache.org

realm.io

splicemachine.com

Technical documentation

drill.apache.org/docs

realm.io/docs

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Realm, acquired by MongoDB in May 2019

Splice Machine

Initial release

2012

2014

Current release

1.20.3, January 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Windows

Android
Backend: server-less
iOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C++

.Net
Java

with Android only
Objective-C
React Native
Swift

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

Server-side scripts

Stored procedures

user defined functions

runs within the applications so server-side scripts are unnecessary

yes

Java

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

In-Memory realm

yes

User concepts

Access control

Depending on the underlying data source

yes

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

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Recent citations in the news

Analyse Kafka messages with SQL queries using Apache Drill
27 September 2019, Towards Data Science

MapR Advances Support for Flexible and High Performance Analytics on JSON and S3 Data with Apache Drill
30 January 2019, Business Wire

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Update Apache Drill Eliminates ETL, Data Transformation for MapR Database
30 March 2022, InApps Technology

Interacting with MapR-DB
11 December 2018, hackernoon.com

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26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine Learning
19 January 2021, PR Newswire

Monte Zweben's Splice Machine Introduces New Hadoop RDBMS
24 June 2014, Forbes

Hadoop-based RDBMS Now Available from Splice
12 May 2014, Datanami

The Ultimate Tool for Data Science Feature Factories
23 August 2019, Towards Data Science

provided by Google News

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