Material Big Data

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

Se convoca Curso Business Intelligence Open Source en Barcelona. No te lo pierdas!!

Quieres aprender sobre Pentaho, Mondrian, Saiku, CTools, Talend y más... de los expertos?

Pentaho 5. Un gran salto

Ya se ha lanzado Pentaho 5 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 2015

7 de febrero de 2016

Open Source Business Intelligence tips in January

1 de febrero de 2016

Who said that Business Intelligence can be boring?

Data, data, data... and visualization

31 de enero de 2016

Free Book, The Field Guide to Data Science


The Field Guide (download in previous link) to Data Science spells out what data science is, why it matters to organizations, as well as how to create data science teams. 

Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. 

Senior leaders will walk away with a deeper understanding of the concepts at the heart of data science, practitioners will add to their toolboxes, and beginners will find insights to help them start on their data science journey

Greenplum, Open Source Database for Data Warehouse


Greenplum, que hay tenido diferentes estrategias desde su creación, se convierte ahora en una de las mejores alternativas Open Source para entornos Data Warehouse, basada en PostgreSQL

Greenplum Database is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes.

Greenplum Database project is released under the Apache 2 license.

Greenplum on GitHub
Ver los Tutoriales
Documentacion

28 de enero de 2016

BI Reporting and Analytics on Apache Cassandra

Un paso más en el uso del Big Data para Análisis y Business Intelligence

26 de enero de 2016

Good web app for selecting between 400 Visualization Tools and Books



If you like, work and fun with data and visualization you should check this: DataViz Tools

25 de enero de 2016

Las 7 C del Internet of Things (IoT)


Vaya, se trataba de buscar otra letra para concretar puntos importantes de una tecnología de moda. Ahora tenemos las 7 Cs del Internet of Things, y lo dice Forbes:

1 — Consumption: The first stage of the IoT is always consumption. We could also use the word ‘ingestion’ here i.e. we need to build devices that are capable of producing operational data so that we can consume it into our IT structures.

2– Connection: The existence of smart connections (from sensors and other types of connection points) are essential avenues for IoT construction. Only when we have connectivity to the IoT can we start to build intelligence around the data that it produces.

3 — Conversion: This is the crucial stage that sees us take raw sensor data and convert it into contextualized meaning. Applying human reasoning to raw data is simply not possible, we need to expand the 1s and Os that the machines produce and start to know what information matters where, when and why — this after all is what context is all about.



4 — Centralization: The Internet of Things is everywhere, which is kind of why it got its name in the first place. Given the existence of so many different, disparate, disconnected and disaggregated data streams, the need to centralize that data and bring into one central location is essential if we are to perform big data analytics.

5 — Cognition: This is the part where we make sure that we understand what the data itself means. This is not the same as plain old data conversion, this is a more analytical process where we make sure we can apply context to the data in hand.

6 — Configuration: In this stage, to use Elrifai’s concept directly, we start to channel ‘feedback’ from the cyber world into the physical world i.e. This is where we start to send the data that we have crunched BACK INTO the Internet of Things so that the machines can work better and work smarter.

7 — Coordination: This is where we take the insight we have gained from IoT intelligence and start the process of better business logistics and scheduling i.e. we know what machines are about to fail, what transport networks are about to suffer outages and delays etc. and we can then use that insight to coordinate the logistics arm of the business function.

An eighth C?
If there were an eight C on this list it would be Creativity — this is because big data analytics with the Internet of Things is all about experimentation.