What is the specialty of data engineering? Duties and skills of a data engineer


We hear more and more about Data engineering. And for good reason, this major is now being offered as a full-fledged branch of data science. Data engineering focuses on developing and structuring data flows in order to allow for optimal exploitation. This step in the data processing process is crucial given the multiplication of streams and the amount of data.

What is data engineering?

Data engineering is a discipline that aims to organize, structure and select data in a way that allows for appropriate processing. The goal of data engineering is to select, sort, and arrange data in a way that ensures its quality and relevance. Therefore data engineering is an essential complement to data science. The two systems that were merging were now different from each other.

Consulting firm Gartner, an authority in the field, defines data architecture as follows: “Data architecture is the discipline that aims to make appropriate data accessible and available to different types of data consumers (including data scientists, business analysts, data analysts, and other actors). »

This specialty is growing in popularity and the numbers don’t lie. The demand for Data Engineers is growing at a growth rate of over 30% annually. Previously this specialty was part of Quests systems engineer Now, it is a large and independent specialty with many, many prospects.

What is the purpose of data engineering?

Without data engineering, companies risk quickly suffocating under the weight of useless data. Remember the phrase “finding a needle in a haystack”? This perfectly illustrates one of the basic functions of data engineering. The goal of a data engineer is to identify, consult and use relevant data.

Therefore, the foundation of data engineering lies in the creation of data pipelines. Like other types of engineers, data engineers imagine and build structures. Data architecture should allow for scalability as well as optimal security.

Another aspect of data engineering involves the production of data science models. In recent years, many tools have appeared that facilitate this aspect of the job.

Why is data engineering necessary?

In recent years, data has multiplied at lightning speed. Companies that previously struggled to collect data now have to work it out. Making the right decisions requires using the right data. This is the essence of the well-known expression in the industry: “garbage in, garbage out” or “garbage at the entrance, trash at the exit.”

Thus the role of data engineering is mainly at the level of the processes of loading, transforming, extracting and structuring databases (for example, creating data lakes). We can distinguish between the different main lines of action:

  • Collect data from different sources. Data Engineer works with existing software but can also develop their own tools;
  • Data structuring
  • identify and remove erroneous or irrelevant data; or
  • Standardize data so that it can be processed.

In addition, data engineering is essential for the development of machine learning and artificial intelligence. Indeed, to ensure proper operation, data quality, especially training data, makes a real difference. This is where data engineering comes into its own.

The role of data engineering in business development

Realizing the value available to them through their data, companies have recruited many data scientists. Today, the needs of companies have changed: it is not enough to keep and not apply proofs of concept. It is then necessary for a data engineer to make the work of a data scientist usable company-wide, by thinking about solving problems with the number of requests or memory used by an algorithm.

Without data engineering, companies risk crumbling quickly under the weight of useless data, as these have multiplied in recent years.

This is where the data engineer comes in, who authors, extends, arranges, and maintains a data warehouse. Between a technical background and specific skills, a data engineer helps companies translate data that is like raw material, driving decision making or for operational purposes, so that it is useful. Data engineering requires a rigorous infrastructure expertise and a technical relational aspect in a project management context.

Structured data is organized and formatted to make it easier to process and easier to analyze. For example, it can be names, addresses, ages, or data entered on forms.

A data engineer is then analogous to a computer scientist who takes care of all the information pipelines of an organization: he collects it, transforms it, and makes it available to the different service teams. Data is related to the real world and represents the visual and tactile interactions with the user, which requires computer development skills to decode. Thus, he must be able to create database structure, main modeling tools, code (python, C/C++, Java, etc.), master SQL or NoSQL techniques and explore extracted data.

Therefore, it has the role of the first link in the series Data processing. In effect, he brings the data to the data analyst, who will pass it on to the data scientist. For this, the data engineer will use the ETL (Extract, Transform, Load) mechanism:

  • The first step, collecting technical data extracted from various sources such as sensors on objects connected to the Internet of Things, cookies on a website, a user’s shopping cart…
  • Then, it will write a data pipeline that defines a series of actions that will be performed as the company’s data is transmitted, while optimizing and securing it. The raw data is thus converted into usable data for analysis, which will be uploaded for future use.
  • Next, the Data Engineer will link up with the Data Analyst, whose task will be data visualization, and who will perform exploratory analysis and organize the information obtained in graphical form. It will have a more strategic role as it will help the managers to make their decisions for the benefit of the organization. It will then ask how long customers’ subscriptions will be before termination, for example.
  • Finally, the data scientist will get involved with the mechanics in order to model the data according to the pattern. For example, in the banking sector, a data scientist will seek to know which customers are likely to stay by making predictions after understanding the data collected.
  • Data engineering and machine learning fartificial intelligence
  • Data engineering is a branch of computer science and artificial intelligence. The information systems that surround us are becoming more and more complex, generating a lot of data that is difficult to interpret. Artificial intelligence makes it possible to use machine learning algorithms to make predictions. Thus, large amounts of data (big data) can be used in complex computer systems, subject to issues of efficiency, reliability, and ethics.

In addition, data engineering is essential for the development of machine learning and artificial intelligence. In fact, in order to ensure normal operation, the quality of the data, especially the training data, has a real impact. This is where data engineering comes into its own.

A data scientist relies on scientific, mathematical and technical tools to solve problems related to rotation. It will then create turnkey data solutions such as machine learning algorithms that can be used to detect fraudulent orders on a website or predict potential machine errors at a machine manufacturer in the industry.

As you understand, the initial intervention of data engineers is essential to the success of projects Big data Especially with the presence of technologies such as the Internet of Things and artificial intelligence. This explains the increase in job offers and the emergence of innovative applications such as self-driving vehicles, drones for smart agriculture, or the invention of new drugs.

Who is a data engineer?

Data engineer job
Data engineer job

Several professions specializing in data have emerged in recent years. The data engineer position is one of them.

However, unlike the data scientist position, which is the best known of them, the data engineer is the senior representative at Data processing.

The work of an engineer is first to the work of a data scientist, and his goal is to design platforms to process large amounts of data in the best conditions. To do this, it ensures that the published data pipelines are secure and clear enough to be analyzed by data analysts and then transformed by the data scientists who will apply the algorithms to them.

To do this, the data engineer has an in-depth experience that allows him to carry out his task of developing data flows. He specializes in structured languages ​​such as JavaScript, Scala, and Python. He also has skills in designing databases which he creates using SQL and NoSQL. The data engineer’s output should be readable and easily manipulated later.

A data engineer must master various technologies used in big data business such as Hadoop.

The Data Engineer works in a team and therefore must have excellent interpersonal skills. In fact, his work is ahead of that of fellow data scientists and data analysts and allows them to analyze data structure in the best conditions.

Data engineer tasks

  • Design solutions that allow processing of large volumes of data pipelines. These should be secure enough and readable for data analysts and data scientists.
  • Mobilize a team of data specialists at all stages of data processing.
  • Constant updating of technologies and languages ​​used in order to share knowledge and assist in the progress of the project.

Profile of the engineer

A data engineer comes from a higher education in a college of engineering, school of computer science, or a master’s specializing in data science or artificial intelligence. The first experience gained in training or work study is highly required to obtain the skills required for this job.

Data engineer skills

Proficiency in structured languages ​​(Javascript, Scala, Python, etc.),
Proficiency in various operating systems: UNIX, Linux, Solaris, and
Knowledge of database solutions (SQL, NoSQL, etc.),
Solid experience with data warehousing and ETL tools,
Mastery of big data technologies that allow data processing and processing (Hadoop, Spark, Kafka etc.),
Fluent in English.

Data engineer qualifications

  • the power of suggestion,
  • Precision,
  • interaction
  • analytical and synthetic spirit,
  • community spirit,
  • excellent relations,
  • sense of organization,
  • Feeling of quality.

Conclusion

In the early days of big data analytics, data scientists were often expected to set up the infrastructure and data pipelines needed for their work. It wasn’t necessarily part of their skills or expectations for the position. The result was that data modeling was not done properly.

There has been redundant work and inconsistent use of data among data scientists. These kinds of issues prevented companies from being able to extract optimal value from their data projects, so they failed. This also led to a high turnover of data scientists, which still exists today.

Today, with the onslaught of completed digital business transformations, the Internet of Things, and the race towards artificial intelligence, it is clear that companies need data engineers in abundance to lay the foundations for successful data science initiatives.

This is why we will continue to see the role of Data Engineers grow in importance and scale. Companies need teams of people whose sole purpose is to process data in a way that enables them to extract value from it.





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