Data engineering is a sub-area of data science projects whose true relevance has only been recognised in recent years. Data engineering plays a key role, especially when it comes to making data science use cases productive. In this basic article you will find relevant information on the topic of data engineering.
Data science projects are the result of teamwork. In contrast to classic IT tasks, which are clearly located in the IT department, there is the one data science department or the one Data Scientist not. Rather, employees from very different disciplines are needed who are jointly responsible for the success of a data project. One of the central sub-areas of any data science project is data engineering.
Link tip: In our blog articles about Data Roles you can get an overview of the most important roles in data science projects.
The basic tasks of data engineering
Unlike other professions in this field, such as data scientist, the data engineer does not receive the same amount of attention or fame. Nevertheless, data engineers are also rare and increasingly needed. Because without data engineering, an important Basis for analysis projects: the handling of Data.
Data engineering deals with the Collect, Processing and Validate of data and ensures that the infrastructure and applications needed for analysis are in place.
What is data engineering exactly?
The central work area of data engineering are databases, data warehouses and Data Lakes. The main task of the data engineer, in other words, is to Providing data. At Data Engineering Services is about modelling and scaling databases and thus ensuring the flow of data. Data engineering can thus encompass the following sub-areas:
- Conception and provision of the system architecture
- Programming of specific applications
- Database design and configuration
- Configuration of interfaces and sensors
Often, the data engineer's remit also includes the maintenance and administration of the IT infrastructureeven if this is not one of its core tasks. Often, the size and budget of the company in question determines whether or not there are separate persons in charge. However, at least in terms of professional training, a data engineer can take over these tasks partially or completely.
Link tip: In addition to system architecture, data engineering is also centrally concerned with Data pipelines - a concept that we describe in more detail here.
A whole range of tools and technologies are used in the field of data engineering
There is a wide variety of Tools and Technologieswhich are available for data engineering. The best-known tool in this context is Hadoop - an open source software solution from the Apache Software Foundation. Hadoop now has numerous innovations, extensions and in-house competitors. To name just the most important ones: Spark, Cassandra, Kafka or Tomcat. In addition, there are numerous other providers of databases and systems such as: MongoDB, cloudera, Oracle, Microsoft SQL Server, pentaho or talend.
To be able to select and set up the right tools for the right task, knowledge and a deep understanding of Data models as well as relational and non-relational Database design necessary. Especially in the Big Data-It is becoming increasingly clear in the IT environment that data engineering is gaining in importance, because this is where the possibilities of classic IT are reaching their limits.
Cloud solutions are becoming the standard in data engineering
More and more companies are relying on data science in their data science projects. Cloud solutions. The theme Cloud is becoming increasingly important for a variety of reasons. Above all, the aspects of security, access speed, scalability and economic considerations speak in favour of Cloud computing.
The Set up and the Configuration of the cloud is an important task area of data engineering. For this reason alone, it is becoming more and more important for companies to be aware of Recruiting also keep an eye on the profession of data engineer.
In our blog article about the Data Engineer as a profession, we have also listed an overview of training opportunities.
This is how data engineering and data science differ
It is not only that there is close cooperation between the fields of Data Science and data engineering must exist. In part, the areas of work can also overlap in terms of content. For this reason alone, it is important that a team has a distinctive, well-functioning Communication culture there are. However, there are also significant differences between the fields of data engineering and data science.
One difference is that the focus of the Data Scientist is on the Data analysis and exploration of the data with the help of mathematical and statistical models and methods, while the data engineer deals with the Software-, Hardware- and Database–Architectures that make this possible. Data engineering encompasses the aspects:
- Data security,
- Data protection (DSGVO),
- Data quality and
- IT security.
Data Engineering training opportunities
As the demand in the field of data engineering has increased rapidly in recent years, the important question arises: How do you become Data Engineer? In most cases, data engineers come from the fields of computer science, business informatics and computer technology. However, this does not exclude someone with a statistical Basic trainingwho at the same time has initial experience in the field of engineering, later specialises in data engineering.
In addition to personal preferences, this decision also depends heavily on the particular company in which someone wants to make a career, or on the specific data science projects - in short: Learning on the job. The framework conditions therefore strongly determine which specialisation or which exact knowledge is relevant and must be learned.
Our Data Engineering Trainee Programme
Since we ourselves can see more and more often in our projects how important the role of data engineering is for project success, we have developed a Data Engineering Trainee Programme was launched. This is a 12-month programme in which the most important aspects of the professional field are taught. It is important to us to have a balanced relationship between theory and practice, as this will also shape the later everyday professional life. Because of the multitude of tools and technologies, the acquisition of new knowledge and skills is a constant in everyday work.