Material Big Data

Lanzados ppts informativos de tecnologías BigData: Hadoop, Hbase, Hive, Zookeeper...

Te presentamos la mejor plataforma de Planificación y Presupuestacion BI

Forecasts, Web and excel-like interface, Mobile Apps, Qlikview, SAP and Salesforce Integration...

Pentaho Analytics. Un gran salto

Ya se ha lanzado Pentaho 7 y con grandes sorpresas. Descubre con nosotros las mejoras de la mejor suite Open BI

La mejor oferta de Cusos Open Source

Después de la gran acogida de nuestros Cursos Open Source, eminentemente prácticos, lanzamos las convocatorias de 2016

7 dic. 2016

Available new Open Source OLAP viewer, STPivot4




STPivot4 is based on the old Pivot4J project where functionality has been added, improved and extended. These technical features are mentioned below.



GitHub STPivot4
For additional information, you may visit STPivot4 Project page at http://bit.ly/2gdy09H

Main Features:
  • STPivot4 is Pentaho plugin for visualizing OLAP cubes.
  • Deploys as Pentaho Plugin
  • Supports Mondrian 4!
  • Improves Pentaho user experience.
  • Intuitive UI with Drag and Drop for Measures, Dimensions and Filters
  • Adds key features to Pentaho OLAP viewer replacing JPivot.
  • Easy multi-level member selection.
  • Advanced and function based member selection (Limit, Ranking, Filter, Order).
  • Let user create "on the fly" formulas and calculations using
  • Non MDX gran totals (min,max,avg and sum) per member, hierarchy or axis.
  • New user friendly Selector Area
  • and more…


6 dic. 2016

7 Ejemplos y Aplicaciones practicas de Big Data


En las siguientes Aplicaciones, Cuadros de Mando y ejemplos podéis ver el funcionamiento práctico del Big Data en diferentes casos y usando diferentes tecnologías: Kafka, Spark, Apache Kylin, Neo4J....

Acceder a los ejemplos

Si quieres saber más de Big Data, te pueden interesar estos enlaces:

OLAP for Big Data. It´s possible? 
Como empezar a aprender Big Data en 2 horas
List of Open Source Business Intelligence tools
Analysis Big Data OLAP sobre Hadoop con Apache Kylin (spanish)
Caso de uso de Apache Kafka en tiempo real, Big Data
 (spanish)


1 dic. 2016

Lanzamiento de Jedox 7 y Novedades


Se acaba de presentar la versión 7 de una de las mejores soluciones para Planificación y Presupuestación Financiera y de Ventas, Jedox 7

Apúntate al webinar gratuito en español para el próximo 20 de Diciembre de 15:30h a 17:30h



A continuación, te contamos las novedades, mejoras, etc... En este enlace tienes otros posts que hemos publicado sobre Jedox


Press Release oficial sobre el lanzamiento

Jedox 7:

- Web en inglés con las novedades en Jedox 7

Jedox 7 is a true game-changer: Download our free "What's New" whitepaper and get all the details on smart modeling tools that bring your planning quickly up to speed, new design capabilities, enhancements to our innovative GPU technology, and so much more.



Jedox Models: Planning Made Simple

We are proud to introduce four all-new Jedox Models for Profit & Loss, Cost Center, Sales and Human Resources.

In 2017, Jedox and their partners will continue to provide a growing portfolio of these predefined and configurable planning applications through the new Jedox Marketplace. 

Discover how you can kickstart and improve your planning processes with our new Jedox Models.

Jedox Models 






25 nov. 2016

Business Intelligence for Hadoop Benchmark


Quite interested this Benchmark you can download from atscale, where you can find insights about Business Intelligence on Hadoop

If you are interested, check also our posts:

OLAP for Big Data. It´s possible?
List of Open Source Business Intelligence tools
Analysis Big Data OLAP sobre Hadoop con Apache Kylin (spanish)
Caso de uso de Apache Kafka en tiempo real, Big Data (spanish)

About the Benchmark:

Key Findings:

  • SQL-on-Hadoop engines are well suited for Business Intelligence (BI): All tested engines – Hive, Impala, Presto,and Spark SQL – successfully executed all of the queries in our benchmark suite and are stable enough to support business intelligence workloads.

  • There is no single “best engine”: We continue to see the different engines shine in different areas. Depending on raw data size, query complexity, and the target number of end-users enterprises will find that each engine has its own ‘sweet spot’.

  • Version-to-version improvements are significant: The open source community continues to drive significant and rapid improvements across the board. All engines tested showed between 2x to 4x performance gains in the six months between the first and second edition of the benchmarks. This is great news for those enterprises deploying BI workloads to Hadoop.

  • Small vs. Big Data: Impala and Spark SQL continue to shine for small data queries (queries against the AtScale Adaptive Cache). New in this edition, the latest release of Hive LLAP (Live Long and Process) shows suitable “small data” query response times. Presto also shows promise on small, interactive queries.

  • Few vs. Many Users: While Impala continues to shine in terms of concurrent query performance, Hive and SparkSQL showed improvements in this category. Presto, new to this edition of the benchmarks, showed the best results in our user concurrency testing.


20 nov. 2016

Tipos de roles en Analytics (Business Intelligence, Big Data)



Conforme va creciendo la industria de Analytics, se hace más dificil conocer las descripción de cada uno de los roles y puestos. Es más, generalmente se usan de forma equivocada, mezclando tareas, descripciones de cometidos, etc...

Esto lleva a confusión tanto a los propios especialistas, como a las personas que están formandose y estudiando para realizar estos trabajos. En una industria tan cambiante es frecuente la aparición y especialización de diferentes puestos de trabajos. Aquí, os detallamos cada uno de ellos:


Business Analyst:




Data Analyst:



Data and Analytics Manager:


Data Architect:



Data Engineer:



Data Scientist:



Database Administrator:



Statistician:





Te puede interesar tambien:

Como pasar una entrevista con Pentaho BI Open Source?
Skills en Data Analysts y sus diferencias
Empezar a aprender Big Data en 2 horas?

Visto en Kdnuggets

17 nov. 2016

Cuadros de Mando y Business Intelligence para Ciudades Inteligentes


Cada vez son más las ciudades que están implementando soluciones de Ciudades Inteligentes, Smart Cities... en donde se abarcan una gran cantidad de aspectos, en cuando a tecnologías, dispositivos, analítica de datos, etc...

Lo principal en todos ellos es que son soluciones que deben integrar información e indicadores diversos de todo tipo de fuentes de datos: bases de datos relacionales tradicionales, redes sociales, aplicaciones móviles, sensores... en donde es fundamental que no haya islas o tecnologías cerradas, por lo que el Open Source es fundamental, pues se puede adaptar a todo tipo de soluciones

En base a nuestra experiencia en algunos de estos proyectos de ciudades inteligentes en los que hemos participado, queremos compartir unos cuantas tecnologías, recursos y demos que os pueden ser de ayuda:

1. List of Open Source solutions for Smart Cities - Internet of Things projects

2. List of Open Source Business Intelligence tool for Smart Cities 

3. 35 Open Source Tools para Internet of Things (IoT)



Demos:

Tecnologías Big Data

Demos Business Intelligence





Seguimiento del tráfico near real time en el Ayuntamiento de Madrid (Acceso)



Geoposicionamiento de rutas dinámicas (Acceso/Video)




Recomendación de Rutas (grafos) (Acceso/Video)



Como empezar a aprender Big Data en 2 horas



Big Data es uno de los hitos de estos últimos años. Son muchas las personas que quieren acercarse y conocer, primero lo más básico, para tener unas nociones generales. Pero resulta complicado encontrar una rápida guía, que en un par de horas, sirva para 'defendernos' en esto del Big Data, máxime si no se tienen altos skills técnicos

Por ello, hemos recopilado una serie de infografías, presentaciones, webinar, demos y documentación para que podáis tener una primera visión del Big Data en 2 horas!!


1. Infografías
     

















2. Webinar



Ver en formato Presentación



3. Demos




Ver Demos Online



4. Claves-Presentaciones








5. Libro Verde del Big Data

























Mas info? Escríbenos


12 nov. 2016

Pentaho 7 CE ya listo para descargar


Ya tenéis disponible la versión 7 de Pentaho Open Source, tanto de BI Server, como de PDI (Pentaho Data Integration)

A disfrutar!!

Si necesitas apoyo para una migración de versiones anteriores, echa un vistazo a este post

En este blog, puedes seguir lo contado en cada una de las charlas, más que interesantes, que se contaron en el Pentaho Community Meeting de Amberes (PCM16)







Una de las funcionalidades más interesantes presentadas es:

WebSpoon

A web browser based version of Spoon. WebSpoon is basically Spoon that runs in your brower, easy as that. 

By accessing a server URL, you can create, preview, save and run transformations and jobs in your browser. WebSpoon works on server side, so all your transformations are stored and run in your browser.




If you want to deploy webspoon for yourself, you can download the .war file from the repository, copy it to the tomcat webserver folder and restart your server. After doing so, webspoon will be accessible through the url of your running server.

Different usecases can be thought of for a browser based spoon:
  • PDI on the go: run pdi on your smartphone or tablet.
  • Security: transformations and jobs run on the server so the data remains within the server.
  • No installation required.
  • No difference in UI between BI server and DI server.
In order to get developing yourself and contribute to the project, clone the repository, install RAP and eclipse and import the cloned UI folder as an eclipse project.





A disfu

10 nov. 2016

OLAP for Big Data. It´s possible?

Hadoop is a great platform for storing a lot of data, but running OLAP is usually done on smaller datasets in legacy and traditional proprietary platforms.   OLAP workloads are beginning to migrate to the one data lake that is running Hadoop and Spark.
Fortunately, there are a number of Apache projects that are starting to make OLAP possible on Hadoop. 

Apache Kylin

Image title
For an introduction to this interesting Hadoop project, check out this article.   Apache Kylin originally from eBay, is a Distributed Analytics Engine that provides SQL and OLAP access to Hadoop datasets utilizing Hive and HBase.   It can use called through SparkSQL as well making for a very useful project.   This project let's you work with PowerBI, Tableau and Excel with more tool support coming soon.    You can do MOLAP cubes and support many users with fast queries over billions of rows.   Apache Kylin provides JDBC and ODBC drivers.

Check our Post with demo online and detailed information

Image title
An interesting talk on Mondrian, MDX and Apache Kylin, points to big things in OLAP.    Yet another project using the excellent Apache Calcite.
I would recommend giving this project a try and see if it meets your needs.  It is one of the best options out there.   It is currently not part of the Big Hadoop Three's supported stacks.                 

Druid

Image title

Druid is another very strong offering in fast SQL OLAP solutions on Hadoop with support growing rapidly.  The documentation for this project is excellent and makes it easy for OLAP-oriented DBAs, data architects, data engineers and data focused programmers to get started with this interesting Big Data project. Druid provides sub-second OLAP Queries with column orientation and inverted indexes enabling multi-dimensional filtering and scanning to allow for aggregating and filtering data.   Again, not officially part of the Big Hadoop Three's supported stacks.   I recommend downloading and installing this project and giving it a test run.  Airbnb and Alibaba are users of Druid.
And the secret word for Druid; Apache Calcite.  This project seems to be everywhere and you will find it here as well.      
                         

Apache Lens

Image title

Apache Lens provides a unified analytics interface to Hadoop. It is pretty quick to install, works with Hive, JDBC and OLAP Cubes.  There is an Apache Zeppelin interface for Apache Lens which is good.   I don't hear a lot about this one, but again it seems interesting.

Other Options To Investigate:
  • SnappyData  (Strong SQL, In-Memory Speed, and GemfireXD history)
  • Apache HAWQ (Strong SQL support and Greenplum history)
  • Splice Machine (Now Open Source)
  •  Hive LLAP is moving into OLAP, SQL 2011 support is growing and so is performance.
  • Apache Phoenix may be able to do basic OLAP with some help from Saiku or STPivot.   I really like Phoenix and it has the performance and power to back up a lot of data through queries and concurrency.  It is lacking a lot of the OLAP specific queries that some tools and users will most likely need.  I am thinking that Apache Calcite and Phoenix will eventually make this a great OLAP tools.

Source: Dzone

6 nov. 2016

List of Open Source Business Intelligence tools




Here you can find an updated list of main business intelligence open source tools. If you know any other, don´t hesitate to write us

- Talend, including ETL, Data quality and MDM. Versions OS y Enterprise

- Pentaho, including Kettle, Mondrian, JFreeReport and Weka. Versions OS y Enterprise

- BIRT, for reporting

- Seal Report, for reporting

- LinceBI, including Kettle, Mondrian, STDashboard, STCard and STPivot

- Jasper Reports, including iReport. Versions OS y Enterprise

- Jedox Base, Palo core and Jedox Base. Versions OS y Enterprise

- Saiku, for OLAP Analysis, including Mondrian. Versions OS y Enterprise

- SpagoBI, including Talend, Mondrian, JPivot and Palo

- Knime, including Knime connectors

- Kibana, for elasticsearch data