DBMS > Hive vs. Kinetica vs. Microsoft Azure Data Explorer vs. RocksDB

System Properties Comparison Hive vs. Kinetica vs. Microsoft Azure Data Explorer vs. RocksDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Fully managed big data interactive analytics platform

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

column oriented

Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

hive.apache.org

www.kinetica.com

azure.microsoft.com/services/data-explorer

rocksdb.org

Technical documentation

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

docs.kinetica.com

docs.microsoft.com/en-us/azure/data-explorer

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

initially developed by Facebook

Kinetica

Microsoft

Facebook, Inc.

Initial release

2012

2019

2013

Current release

3.1.3, April 2022

7.1, August 2021

cloud service with continuous releases

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

BSD

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

C, C++

C++

Server operating systems

All OS with a Java VM

Linux

hosted

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

C++ API
Java API

Supported programming languages

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Yes, possible languages: KQL, Python, R

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users, groups and roles

Access rights for users and roles on table level

Azure Active Directory Authentication

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.