Cookies Policy

We use our own and third-party cookies to perform usage and measurement analyses on our website in order to improve our services. By continuing to browse you accept their use. To change the settings or find out more click. Find out more here.


Google Cloud Next in Madrid

On June 8th, our innovation team had the opportunity to spend a day listening to the Google professionals talking about new ways of working and introducing the new apps and technologies they have built.

The event was split in three sections focused in a variety of talks. The main topics discussed were related to Big Data, Machine Learning, Compute Engine and Business Security

The sessions started with a general view of the possibilities that Google Cloud Platforms offers.

The main advantage of the platform is the possibility to work with big data volumes that do not require setting up several servers, all of this at a low cost (you only pay for the information storage) and in few seconds. Its structure is based on their own Cloud Db called BigQuery and uses SQL as the main programming language.

The main advantage of the platform is the possibility to work with big data volumes that does not require setting up several servers, all of this at a low cost  and in few seconds

Cloud Spanner, the first Relational Database in Cloud

Later on, they presented Cloud Spanner. Undoubtedly, one of the most interesting products of Google Cloud for most companies as it is the first Relational Data Base (RDS) in Cloud that offers such technical stability and high availability.

In other words, this means that the organizations can use the SQL/Schema environment as always, but without the limitations that these tools imply. Therefore, this kind of platforms can be used in critical applications previously reserved only for NoSQL databases.

Big Data and machine learning

The Big Data session was focused on Cloud Dataproc and Cloud Datalab.

Cloud Dataproc is the add-in that will be used in conjunction with the Big Data solutions we have already mentioned (ie, BigQuery). It manages efficiently several clusters at the same time by taking into account the workload involved in each one. Additionally, it works with Spark, Hadoop, Pig and Hive.



Google Cloud Platform, cases of success

One of the biggest advantages of using the Google Cloud Platform (GPC) is that it provides a number of services and tools for developers to take build on top of Google’s infrastructure. Java, Python, Go, Node.js, Ruby, PHP and C # are some of the languages supported. Depending on the IT lifecycle of a given company there are several options to develop and deploy applications.


The use of Google Cloud infrastructure is suitable both for startups that are just starting their activities and for scientific environments that must use hundreds of cores to solve complex calculations, like Fluid Dynamics.

A good example of is CENER (Centro Nacional de Energias Renovables), that decided to move a significant part of its activities from HT Condor on premise to Google Cloud Platform, after comparing performance and budget issues between Amazon and Google cloud services. The Head of Computing and Software Development Service at CENER, gave a detailed speech about how GCP improved the daily operations at his company.

Another good example of the improvements that GPC provide us to the architectural world, is the implementation that BBVA made within its company. Google Cloud solutions are currently being used in BBVA to manage some internal applications as well as its Intranet.


Google Cloud Solutions save time and money

Lastly, one of the Google Cloud Solutions Engineer demonstrated how easily GKE (Google Container Engine) can be integrated with QA processes and integration pipelines and tools like Jenkins or GITHUB, thus reducing time & money related with SCM (Software Configuration Management) activities.

From a company point of view, the use of technologies Kubernetes and Docker containers can be of interest to deploy models and data products as they have already been developed and tested as proofs of concept or prototypes.

Taking models into production requires a professional workflow, high-quality standards, scalable code and infrastructure, and no doubt that Kubernetes and Docker containers, along with Google Container Engine (GKE) can help to make this process as straightforward as possible.

Redefining Consulting

We continue to explore the new opportunities that new technologies are creating to provide best solutions to our clients and become the engine of the innovation process in our business areas.

We are working very hard to be on the avant-garde of this new era.

Warning: count(): Parameter must be an array or an object that implements Countable in /home/austri/public_html/wp-content/themes/nfq/single-blog.php on line 334