DBMS > Apache Pinot vs. dBASE vs. Google BigQuery vs. LMDB vs. Splice Machine

System Properties Comparison Apache Pinot vs. dBASE vs. Google BigQuery vs. LMDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Large scale data warehouse service with append-only tables

A high performant, light-weight, embedded key-value database library

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

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

Score	0.35
Rank	#274	Overall
	#128	Relational DBMS

Score	9.16
Rank	#43	Overall
	#27	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.94
Rank	#120	Overall
	#20	Key-value stores

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

pinot.apache.org

www.dbase.com

cloud.google.com/bigquery

www.symas.com/symas-embedded-database-lmdb

splicemachine.com

Technical documentation

docs.pinot.apache.org

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

www.lmdb.tech/doc

splicemachine.com/how-it-works

Developer

Apache Software Foundation and contributors

Asthon Tate

Google

Symas

Splice Machine

Initial release

2015

1979

2010

2011

2014

Current release

1.0.0, September 2023

dBASE 2019, 2019

0.9.32, January 2024

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux
Unix
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

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-like query language

yes

APIs and other access methods

JDBC

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Java
Python

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

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

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

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, 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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

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 rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

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

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