DBMS > Apache Impala vs. Heroic vs. searchxml vs. TempoIQ vs. Teradata Aster

System Properties Comparison Apache Impala vs. Heroic vs. searchxml vs. TempoIQ vs. Teradata Aster

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

DBMS for structured and unstructured content wrapped with an application server

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

Time Series DBMS

Native XML DBMS
Search engine

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

impala.apache.org

github.com/spotify/heroic

www.searchxml.net/category/products

tempoiq.com (offline)

Technical documentation

impala.apache.org/impala-docs.html

spotify.github.io/heroic

www.searchxml.net/support/handouts

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Spotify

informationpartners gmbh

TempoIQ

Teradata

Initial release

2013

2014

2015

2012

2005

Current release

4.1.0, June 2022

1.0

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

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

C++

Server operating systems

Linux

Windows

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

C++

most other programming languages supported via APIs

C#
Java
JavaScript

Node.js
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

on the application server

R packages

Triggers

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

sychronisation to multiple collections

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

ACID

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

based on Apache Sentry and Kerberos

Domain, group and role-based access control at the document level and for application services

simple authentication-based access control

fine grained access rights according to SQL-standard

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More resources

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

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

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

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23 May 2014, Northwestern Engineering

Teradata Aster gets graph database, HDFS-compatible file store
8 October 2013, ZDNet

Teradata Provides the Simplest Way to Bring the Science of Data to the Art of Business
22 September 2011, PR Newswire

Teradata's Aster shows how the flowers of fraud bloom
23 April 2015, The Register

Case study: Siemens reduces train failures with Teradata Aster
12 September 2016, RCR Wireless News

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