DBMS > Apache Impala vs. Apache Phoenix vs. Microsoft Azure Data Explorer vs. OrigoDB

System Properties Comparison Apache Impala vs. Apache Phoenix vs. Microsoft Azure Data Explorer vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A scale-out RDBMS with evolutionary schema built on Apache HBase

Fully managed big data interactive analytics platform

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

column oriented

Document store
Object oriented DBMS

Secondary database models

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	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	3.28
Rank	#83	Overall
	#45	Relational DBMS

Score	0.00
Rank	#385	Overall
	#54	Document stores
	#21	Object oriented DBMS

Website

impala.apache.org

phoenix.apache.org

azure.microsoft.com/services/data-explorer

origodb.com

Technical documentation

impala.apache.org/impala-docs.html

phoenix.apache.org

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

origodb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Microsoft

Robert Friberg et al

Initial release

2013

2014

2019

2009

under the name LiveDB

Current release

4.1.0, June 2022

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

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

C++

Java

Server operating systems

Linux

Linux
Unix
Windows

hosted

Linux
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

Fixed schema with schema-less datatypes (dynamic)

yes

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

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

JDBC

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

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

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

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Hadoop integration

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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, groups and roles

based on Apache Sentry and Kerberos

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Azure Active Directory Authentication

Role based authorization

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix
2 June 2016, AWS Blog

Deep dive into Azure HDInsight 4.0
25 September 2018, azure.microsoft.com

Hortonworks Starts Hadoop Summit with Data Platform Update
28 June 2016, ADT Magazine

Apache Drill Adds New Data Formats
28 March 2022, iProgrammer

Amazon EMR 4.7.0 – Apache Tez & Phoenix, Updates to Existing Apps
2 June 2016, AWS Blog

provided by Google News

We’re retiring Azure Time Series Insights on 7 July 2024 – transition to Azure Data Explorer
31 May 2024, Microsoft

Update records in a Kusto Database (public preview)
20 February 2024, Microsoft

Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints
5 February 2024, Microsoft

Announcing General Availability of Graph Semantics in Kusto
27 May 2024, Microsoft

General availability: Azure Data Explorer adds new geospatial capabilities
23 January 2024, Microsoft

provided by Google News

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