DBMS > Apache Impala vs. Microsoft Azure Table Storage vs. TimesTen vs. Vertica vs. XTDB

System Properties Comparison Apache Impala vs. Microsoft Azure Table Storage vs. TimesTen vs. Vertica vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A Wide Column Store for rapid development using massive semi-structured datasets

In-Memory RDBMS compatible to Oracle

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Column oriented

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

impala.apache.org

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/timesten.html

www.vertica.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

impala.apache.org/impala-docs.html

docs.oracle.com/database/timesten-18.1

vertica.com/documentation

www.xtdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

OpenText

previously Micro Focus and Hewlett Packard

Juxt Ltd.

Initial release

2013

2012

1998

2005

2019

Current release

4.1.0, June 2022

11 Release 2 (11.2.2.8.0)

12.0.3, January 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Limited community edition free

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Clojure

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Java
PL/SQL

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

PL/SQL

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

horizontal partitioning, hierarchical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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