DBMS > Apache Impala vs. Google BigQuery vs. Hypertable vs. Microsoft Azure Data Explorer vs. NuoDB

System Properties Comparison Apache Impala vs. Google BigQuery vs. Hypertable vs. Microsoft Azure Data Explorer vs. NuoDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

Fully managed big data interactive analytics platform

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

column oriented

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	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

impala.apache.org

cloud.google.com/bigquery

azure.microsoft.com/services/data-explorer

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

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

doc.nuodb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Hypertable Inc.

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2013

2010

2009

2019

2013

Current release

4.1.0, June 2022

0.9.8.11, March 2016

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GNU version 3. Commercial license available

commercial

limited edition free

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++

Server operating systems

Linux

hosted

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

yes

schema-free

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

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.

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

C++ API
Thrift

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

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

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

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

Yes, possible languages: KQL, Python, R

Java, SQL

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication factor on file system level

yes

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

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

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

Temporary table

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

Azure Active Directory Authentication

Standard SQL roles/ privileges, Administrative Users

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