Big Data
Artificial Intelligence
Cloud
Cybersecurity
Agile Management
The digitalization and the use of massive data are producing a revolution with a huge impact on social life, which can be perceived daily. Nowadays, we have the ability to store and process more data than ever before in history and it can be transformed, augmented and processed in real-time in order to make decisions and digitally transform businesses.
The large-scale processing of mass data has a bit of data science, a lot of data arts and a bunch of technologies and tools that cooperate within the Big Data ecosystem to transform data into artificial intelligence and actionable knowledge.
Combining all the disciplines implicated in data processing, automatic learning, knowledge generation and the coordination of the actions derived from that knowledge, is the “magic” needed to be reached to make the Big Data and Artificial Intelligence projects succeed.
It is necessary a wide range of talent to transform data into actions, from professionals with the adequate technical, mathematical and statistical abilities to engineers with deep knowledge on technologies of Hadoop/ Spark (Hark) ecosystem.
Data Engineering
Data Engineers are the “plumbers” who keep the data pipeline connected, either on the cloud or on-premise, and are devoted to the logistics of data between systems, they deal with quality, cleansing and the organization of information.
They are dedicated to the art of building, developing and maintaining the processes of ingestion, transformation, normalization, modeling and design of data architecture in a large scale environment. They have to deal with all kinds of raw data, both structured and unstructured or in any format.
Data Science
Data processed and filtered by Data Engineers are delivered to Data Scientists who use them intensively in different analysis algorithms using statistical and machine learning methods to generate knowledge models to be used in the predictive, prescriptive and descriptive analysis as well as in other fields.
Data Analysis
Data Governance for Master Data Management
DevOps
We provide DevOps engineers with high skills coding or scripting, with the ability to make re-engineering in production processes.
Big Data Extreme to Extreme Projects
Our service proposal covers the complete cycle of development of Big Data & Advanced Analytics projects.
We use the latest AI tools and platforms that allow us to select, train and deploy the most suitable machine learning models and we develop the APIs for connecting this all together to create more intelligent, flexible and scalable solutions.
Service Offer
Advanced Analytics
Service Delivery
We provide the Data Engineer, Data Scientists and Data Analyst that perform the full Artificial Intelligence pipeline.
- What questions do we ask ourselves?
- Do we have the right data?
- What KPIs are the key to understanding model performance and results?
Data preparation
- Ingest data
- Refine the data
- Enrich the data
- Make sure the quality of the data is good enough
- Choose tools
- MLaaS or personalized
- Choose the model
- Choose the cloud or in-house
Model training
- Train the model with the data
- Iterate on the models to improve them
- Interpreting the results
- Manage discrepancies between the technical and business view of results
- Re-training the model
We help our clients to determine features like scalability, price, security, GDPR compliance, maintenance and operations requirements for each uses case before taking any decision, and we provide consulting and development services to deploy solutions in the cloud.
Private
It is a model of computing in the cloud that provides a secure and differentiated environment in which only a specific client can operate.
It is recommended when a client wants to store confidential data with the highest security requirements, GDPR compliance and at the same time if it is needed a scalable, secure and reliable solution in cloud.
Public
This is a model that provides virtualized environments, built on shared physical resources that can be accessed through Internet, these are the main differences with a private cloud that restrict, internal and private access to an organization.
Companies can benefit from the public cloud to make some processes more efficient and cheaper such as the storage of non-sensitive content, online services or event agile development environments.
Hybrid
It is a combination of private and public cloud services that is used in some organizations to offer different types and levels of services allowing secure storage of sensitive customer data in the private part of the Cloud, while offering a collaborative and online service accessible through the public cloud.
Service Offer
- Cost-benefit analysis
- Backup, storage, and data protection capabilities
- Flexibility to add resource
- Ability to scale
Consulting
- Designing cloud architectures
- Defining sensitive assets access
- Transitioning to the cloud planning
- Data & processes migration to the cloud
Development and operations
- Life cycle of application: development, customizing, evolution and maintenance
- Operational support to bring manageability and improve availability, and scalability of applications.
Much of the “rise” of cybercrime is due to the emergence of social networks, both personal and professional, group communication tools, etc. that expose society in a way that many people have not assimilated, leaving a way of entry to all kinds of malware.
Likewise, technologies such as Cloud Computing, Big Data or Quantum Computing, which are extremely useful for business purposes, allow cybercriminals to carry out attacks and develop much more powerful and effective malware, so that the complexity to detect, combat or mitigate risks is increasing as the technological revolution continues to advance.
The protection of personal equipment has usually been delegated to antivirus and personal firewalls, which have a good functioning except in cases in which the human misuses these tools.
We have developed and/or commercialize cybersecurity solutions aimed at controlling, managing and cataloging privileges in web applications, endpoints and mobile devices.
The Product Owner will be in charge of selecting the tasks with the highest priority and communicating it to the rest of the team, these tasks must be the ones that appear on the top of the Product Backlog. The Development Team asks everything needed to turn these user stories into more specific tasks.
What is perfomed in a Sprint?
How will the chosen job task be performed?
