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Love in data times: best couples in the IT world


We know that the combination of technologies is key to provide solutions in AI, data science or big data as it allows to extract all the information from the data and obtain valuable information that helps to improve decision making.


In RocBird we celebreate Valentine's Day with this post about which are the best IT couples (or best combinations of technologies)


Data analytics + Machine learning

They complement each other because Data analytics allows us to see the picture of the day and the past of all our data, and machine learning helps us to predict what will happen in the future, or better understand the current situation.


Big Data + Advanced Analytics

Big Data allows us to capture and manipulate large volumes of data from multiple sources, and this data serves as an input source for the Advanced Analytics team to work its magic through different data models that leverage it and generate descriptive, predictive and prescriptive analytics.


Front-End + Back-End

The Front-End is the part of the website that interacts with the client while the Back-End interacts with the servers and databases. The interrelation of both structures allows the operation of a website or mobile application.


Cloud Computing + DevOps

DevOps are practices to place the code of an application in production, from our computer to a server on the Internet. By mastering the cloud you can prepare the application so that it can maintain a good performance with millions of users in parallel. DevOps allows you to define the best strategies to make your platform deployments fast, secure and effective.


Cloud services are a network of remote servers connected to the internet to store, manage and process data, servers, databases, networks and software currently most powerful are: Amazon Web Services, Microsoft Azure and Google Cloud Platform.


The relationship between the two allows to have an application deployment practice stored in the cloud, being this a practical and effective solution for the continuous delivery of a constantly evolving application.


Data Science + Business Intelligence

Data Science and Business Intelligence use programming in conjunction with mathematics to perform data analysis in order to make business decisions based on real information. This combination allows us to measure, examine and improve over time the evolution of our services.


Machine Learning + IoT

Identify patterns in data collected through multiple types of sensors in IoT devices and be able to make real-time predictions using Machine Learning, combined with IoT Hubs in major clouds, improving response time and preventing unwanted situations before they happen.


Data Engineering + Data Science

The data engineer with his expertise can make data available through ETL or ELT process automation, in a Data Lake, so that then a Data Scientist can consume it and can build analytical, machine learning models that provide valuable information to a company. This pairing works perfectly, as it allows each professional to work on what they are good at, and be focused on key parts of the process, thus generating a synergy of teamwork that guarantees the success of an Analytics project in most cases. In addition, the Data Scientist, who has skills in visualizations, can further enhance the delivery of value.


As we know technology never stops pairing with each other to create its best version. Let's be attentive to new pairs that are being formed so we get the best out of them.



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