La amo con locura

La amo con locura, pero eso no evita que siempre me rechace. Yo insisto e insisto, ya sabéis que no soy de rendirme, pero parece que no quiere que mi vida sea con ella.

Creo que puedo decir sin equivocarme que la quiero desde que nos conocemos. Vale, puede que alguna vez haya renegado un poco, puede. Puede que fuese ese puntito de rebeldía adolescente, puede. Pero lo cierto, es que no me visualizo viviendo sin ella, no entiendo mi futuro lejos de ella, no lo veo.

Una vez más me ha dicho que me vaya, y una vez más, cansado de aguantar, voy a darla su tiempo, a esperar que me eche de menos aunque no sepa si alguna vez lo hizo o sólo me echa siempre de más.

Así que me voy, ¡ahí la dejo!

Estoy seguro de que volveré a rondarla, en algún momento volveré a insistir, volveré a intentar que mi vida esté ligada a la suya, o que al menos sea a su vera. Ya sabéis: no soy de rendirme. Volveré, pero de momento la dejo para otros mejores que yo, otros que la llenen más, o que la entiendan, o que sepan adaptarse a ella mejor de lo que yo he sabido.

¡Ahí la dejo! ¡Ahí os la dejo! Ahí la tenéis vosotros que sois mejores. No es que yo no la quiera, la quiero y la querré siempre, pero no la quiero a cualquier precio y yo también necesito sentirme querido, útil, necesitado ¿y quién no?

Así que adiós. O mejor aún: hasta luego Tierruca; y no adiós.

Big Data talks

I was attending a talk about Big Data past week. The organizer was Ascentic and the speakers were IT workers from Cantabria with high responsibilities in their companies. I think that it is interesting know about different use cases on different sectors.

[Leelo en español en CantabriaTIC]

Celestino Güemes works at Atos Worldgrid and he is a member of its wise committee. He was talking about “new” types of analytic and the issues. He explained us that analytics of historic data is something easy nowadays. However, systems are evolving with predictive analytics and prescriptive analytics, providing to an operator possible actions to take, and what is the recommended one.

He also remarked the use of deep learning and multi-sided market analytic platforms to develop new products and services.

He exposed some interesting and real cases as example of the different types of use of Big Data:

Operational excellence: An oil company uses drilling heads with 120 sensors. They can analyze the data in real time and compare them with the historic  data to know when a head will break avoiding problems.

User experience: They work for a telecommunications provider in the relation between mobile network configuration and use of the clients. For example, they can detect where a user lives or works based on the network elements the user uses. They also can improve the quality of service for a specific VIP user when she is using Youtube during a trip or notify a client with information or an offer when he walk into a street (this is very similar to what I did with my team at GPMESS, my last company).

Bussiness re-invention: A seller of electricity is putting their data with other data sources to look for new possible services and business models. They are exploring things like detect the different machines in a house and offer discounts in new machines when they detect a problem in one of them based on the use of electricity.

Confidence and compliance: he is working in a solution to detect non-technical economic losses (frauds and errors) for electricity companies. These losses represent 1% of the business (3.7M€ per year).

Miguel Sierra is a manager at CIC. He leads a product called IDbox that is a software for Operational Intelligence. It integrates all available information sources, processes that captured signals and offers the tools for analysis to assist in operational decision making. This product is used by companies from different sectors: nuclear plants, electricity companies, private parking companies, water companies, and also high performance sports training.

He was talking about their history and how they became a company with high expertise on Big Data.

He said that the size is important but the frequency is more important. They process 1.5M signals from Iberdrola each second and 80K signals from a nuclear plant each 20 milliseconds (it is almost the same that 4M signals each second).

They help business that are not scalable at first sight providing them ways to become scalable and more profitable companies. He used the example of a clinic that work with professional athletes. They needed a doctor attending a single athlete inside their installations. Now they can provide a service to other clinics and gyms monitoring trainings from a control center operated by a group of doctors. A single doctor can work now with more than 20 athletes that are training at anyplace.

Raul Uría, CEO at Zzircon Business Intelligence. He did a basic presentation thinking in non-technical attendees. He explained what is and for what is the data mining. He showed a complete example with a single product (a slide for kids) talking how data mining helps to know to what users you have to offer this product, and how you should impact them and what message you should use.

I am sure that it was a great explanation for people that are not involved on IT everyday.

¿Qué hace a un emprendedor emprendedor?

Os traigo una traducción más o menos precisa del artículo «What makes entrepreneurs entrepreneurial?», de la profesora Saras D. Sarasvathy.

El artículo original me lo descubrió y recomendó Ana Borrego durante el Yuzzday y me pareció una verbalización muy adecuada a algo que he planteado varias veces, y que he visto defender a otros emprendedores como por ejemplo Pedro Serrahima.

No puedo resumir varias páginas en un párrafo, pero muy básicamente habla del modo de razonar que usan muchos emprendedores de éxito y de que es contrario al modo de pensar de otros perfiles como el de los directivos.

Me pareció tan necesario que le he echado un ratito a traducir todas las metáforas que usa la autora, para que sea accesible a quien no se lleve bien con el inglés. Creo que es algo que puede ayudar, no sólo a emprendedores, si no a cualquiera que esté implicado en la creación de productos, que se mueva en mercados volubles, o que simplemente desayune incertidumbre en su día a día.

Espero que os parezca interesante y os resulte de utilidad.

WSO2 vs Azure API Management

Next values are estimations and all of them depend on the final production needs.

  WSO2 Azure API Management
Deployment effort 24-40 hours 1 hour
New API effort 1-2 hours 1-2 hours
Scale effort 4-8 hours 1 hour
Distribute in other location effort 16-24 hours 1 hour
Min dev. environment costs 1 machine = 587 euros yearly 486 euros yearly
Min pro. environment costs 12 machines + 8,500 euros (yearly) for production support = 15,544 28,288 euros yearly + 1,404 euros for VPN connection to private endpoints = 29,692
Pros –          It is more flexible thinking about configuration and deployment options –          Its management is easier and faster.
Cons –          Hard maintenance –          Its more expensive

–          At this moment you can only use this over Azure infrastructure.

Reverse Geocoding: Bing Maps REST Services

Provider: Microsoft Corp.
Provider Client Libraries: Javascript, .Net
Multiple Languages: Yes
Limitations: Developer account: 10,000 transactions within 30 days period


Batch processing: 1,000,000 geocode entities non-billable transactions in any 12 month period


Windows Apps: 50,000 transactions per day


No Windows mobile apps: 125,000 per year


Information wizard:


If Enterprise prices do not need to be considered, base prices can be seen as a component of Azure subscriptions:

Price: Different prices that depends on the use.


Example request:,-122.12934?o=json&key=BingMapsKey


Example response:

   "copyright":"Copyright © 2011 Microsoft and its suppliers. All rights reserved. This API cannot be accessed and the content and any results may not be used, reproduced or transmitted in any manner without express written permission from Microsoft Corporation.",
               "name":"1 Microsoft Way, Redmond, WA 98052",
                  "addressLine":"1 Microsoft Way",
                  "adminDistrict2":"King Co.",
                  "countryRegion":"United States",
                  "formattedAddress":"1 Microsoft Way, Redmond, WA 98052",


Azure API Management

It is a cloud API Manager that can connect to public and private endpoints. It is provided by Microsoft and cannot be installed as on premise service (but they are open to suggestions about this point and it is under review: “There is no on-premises deployment option available at this time but you can vote on uservoice if you’d like this capability. However, you can certainly use Azure-based API management with on-premises systems and data.”).

At this moment it does not provide a monetization standard (but it is under review: ”We’ll monitor continued feedback on this item”).

It is planned to provide a standard way to have testing and live environments (Sandbox Environment – SBE).

Set Up

Follow a two steps wizard is the only need to set up an Azure API Management instance.

Azure APIM 1

Azure APIM 2

Advanced configuration can be provided in order to personalize security, rate limits or monitoring capabilities.


A publisher can add a new API manually or import it using WADL or Swagger definition files.

Azure APIM 3

A subscriber can discover and subscribe to an API throw developer portal.

Soy un Yuzz

“I’m a Yuzz” grita la camiseta que llevo ahora mismo puesta. Soy un Yuzz. Es seguro que lo fuí y realmente espero seguir siéndolo.

Seguir leyendo en El Faradio.