DBMS > Apache Impala vs. GeoSpock vs. Teradata Aster vs. ToroDB

System Properties Comparison Apache Impala vs. GeoSpock vs. Teradata Aster vs. ToroDB

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

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

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

Spatial and temporal data processing engine for extreme data scale

Platform for big data analytics on multistructured data sources and types

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Document store

Secondary database models

Document store

Time Series DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Website

impala.apache.org

geospock.com

github.com/torodb/server

Technical documentation

impala.apache.org/impala-docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GeoSpock

Teradata

8Kdata

Initial release

2013

2005

2016

Current release

4.1.0, June 2022

2.0, September 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Javascript

Java

Server operating systems

Linux

hosted

Linux

All OS with a Java 7 VM

Data scheme

yes

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

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

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

temporal, categorical

yes

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC
ODBC

JDBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

R packages

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Source-replica replication

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

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

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

Access rights for users can be defined per table

fine grained access rights according to SQL-standard

Access rights for users and roles

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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 brings Apache Iceberg data lake format to its Data Platform
30 June 2022, SiliconANGLE News

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

How GeoSpock is supercharging geospatial analytics
23 February 2021, ComputerWeekly.com

nChain leads investment round in extreme-scale data firm GeoSpock
2 October 2020, CoinGeek

Smart Cities, Autonomous Vehicles, Artificial General Intelligence Robotics: Q&A with Steve Marsh, GeoSpock
16 May 2018, ExchangeWire

GeoSpock’s extreme-scale data mission in $5.4m funding boost
8 October 2020, Cambridge Independent

CEO Richard Baker explores GeoSpock’s role in posting indexed data onto digital ledger
20 October 2020, CoinGeek

provided by Google News

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

Teradata Integrates Big Data Analytic Architecture
22 October 2012, PR Newswire

An American Dream Story, With A Silicon Valley Twist
14 August 2013, Forbes

Gartner, IBM, Teradata make Big Data announcements
17 October 2012, ZDNet

Big Data Use Case – What Is Teradata IntelliCloud?
24 May 2017, insideBIGDATA

provided by Google News

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