What is a big data architect?

In an increasingly connected world, an immense amount of data is generated. However, to extract useful information that allows companies to make better decisions, such as anticipating the advance of epidemics, choose the most appropriate medical treatments, or predicting future sales patterns, it is necessary to have a professional capable of collecting and analysing this data: the big data architect.

Given the enormous potential of Big Data, it is no coincidence that a recent report published by Deloitte and Infoempleo concluded big data it is one of the most interesting and promising professions in the IT sector. Therefore, if you are looking for a challenging career in this industry with high employability rates, a degree in Computer Engineering could be your best option.

What is a big data architect?

Big data architecture focuses on the treatment and analysis of large volumes of data using software tools specifically designed to be able to store, manage and process this type of data. Therefore, the big data architect is the professional in charge of creating and defining the technological architecture that will collect, analyse, exploit and present this data.

He or she oversees designing and building the platform dedicated to the massive processing of data, so that it can be converted into useful and reliable information that facilitates decision-making in any company or organisation, no matter the sector. Its job is to translate the needs and requirements of companies and organisations into an appropriate Big Data solution that contributes to the achievement of business objectives.

The role of a big data architect

The exact role of the big data architect may vary depending on the size of organisation they work in or the sector. However, we’ll explain to you below some of the typical tasks a big data architect carries out:

• Identify data sources and manage data inputs and outputs: The role of the big data architect includes ensuring that data sources, whether internal or external, are reliable, lawful and aligned with organisational objectives. He or she also oversees data inputs and outputs, selecting the data to be processed and those to be cleansed to ensure quality standards.
• Implement the data strategy: The data architect oversees developing the strategy that determines how to work with the data, considering aspects such as its typology, velocity or veracity. He or she defines the models, rules and standards, as well as taking care of database development, database management and storage implementation.
• Planning Big Data solutions: One of the tasks of the Big Data architect is to find the best solutions for mass data processing, considering factors such as storage capacity or the application of the necessary filters. They should ensure the security, scalability and performance of the system always.
• Manage the data architecture: The data architect is in charge of choosing the management platform and managing the big data architecture and the day-to-day processing of the data. The data governance of the company or organisation is therefore in his or her hands.
• Auditing the database: The big data architect also has the role of auditing the data architecture to check that there are no irregularities in the protocols and/or to propose measures that can optimise the use of the organisation's data.

What skills does a data architect need?

Big data architects have a very technical profile, so they need to master the fundamentals of mathematics, statistics and advanced analysis techniques. They need to know programming languages such as Python and R, know how to design and structure SQL and NoSQL databases, understand Hadoop and Apache Spark technologies, and be familiar with cloud systems.

However, given that this is an area in full development, the most important thing is that the data architect has the ability and creativity to integrate and implement the different tools of the new technologies in their data architecture systems.

They must also develop their social and communication skills to be able to understand the needs of clients and explain the technical aspects of data architecture in an understandable way to people outside the industry.