DBMS > Apache Hive vs. Badger

System Properties Comparison Apache Hive vs. Badger

Please select another system to include it in the comparison.

Our visitors often compare Apache Hive and Badger with LevelDB, Amazon DynamoDB and PostgreSQL.

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Primary database model

Relational DBMS

Key-value store

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

Score	70.08
Rank	#18	Overall
	#12	Relational DBMS

Score	0.23
Rank	#302	Overall
	#45	Key-value stores

Website

hive.apache.org

github.com/dgraph-io/badger

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

godoc.org/github.com/dgraph-io/badger

Developer

Apache Software Foundation

initially developed by Facebook

DGraph Labs

Initial release

2012

2017

Current release

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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 VM

BSD
Linux
OS X
Solaris
Windows

Data scheme

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

Supported programming languages

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

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.

User concepts

Access control

Access rights for users, groups and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Design patterns for implementing Hive Metastore for Amazon EMR on EKS
28 February 2025, AWS Blog

Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster
14 March 2025, Towards Data Science

What Is Apache Iceberg?
18 December 2024, IBM

Pinot for Low-Latency Offline Table Analytics
29 August 2024, Uber

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2025
17 February 2025, Simplilearn

provided by Google News

Dgraph raises $11.5 million for scalable graph database solutions
31 July 2019, VentureBeat

provided by Google News

Share this page