Data Engineering

In a nutshell: What is data engineering?

Data engineering is used to combine large amounts of data from different sources (e.g. software, machines). The data engineers responsible for this develop the IT infrastructure required to merge and store the data. They also program interfaces at the points where data is transferred.

 

The work of the data engineers is important preparatory work for further data processing, which is carried out by the data analysts, among others.

 

What does a data engineer do?

 

A data engineer is responsible for merging, summarizing, categorizing and visualizing large amounts of data (big data). All data sets generated in the various data sources are to be prepared by the data engineer in such a way that the data analysts and data scientists can analyze them efficiently.

 

The data engineer's work forms the indispensable basis for the application of data science. This in turn serves the purpose of correctly analyzing the generated big data and generating precise findings. The findings from data science are used to optimize company processes, making them relevant to the company's success.

 

In view of the high demands placed on data engineers and data engineering, we at innobit provide a power platform as a professional IT service provider. It makes numerous tasks in the big data environment easier for companies and IT specialists. Let us advise you individually on the Power Platform!

 

Competencies required of data engineers

Data engineers are IT specialists. They need experience in dealing with various programming languages and databases. They must also be able to customize an existing data infrastructure to the company's needs or introduce such a data infrastructure in the first place.

 

Setting up interfaces between the nodes where data is transferred and processed is also part of the work of data engineers. In addition to IT knowledge, logical skills are required to do their job. Data engineers must be able to set up a sensible clustering of data in order to provide the data scientists and data analysts with good groundwork for their work.

 

The most important skills of Data Engineers are:

• Knowledge of programming languages such as Python and SQL
• Ability to introduce and customize databases
• Setting up data pipelines for extracting and forwarding data
• Competencies in data processing
• Logical understanding of the clustering of big data
• Skills for visualization, reporting and further input for data scientists and data analysts
• Storing data in frameworks and in the cloud
• Continuous monitoring and constant optimization of the database

 

What is the difference between a data engineer and a data scientist?

 

The core task of data engineers is to provide the data volumes required by the company at the desired time. This provision takes place via databases. Ideally, the data in the databases is categorized, harmonized and visualized in an appealing way so that it can be easily processed further. The data scientist and data analyst are responsible for processing the data.

 

The data scientist analyzes the data provided and attempts to derive potential for the future from it. This includes, for example, the development of innovative products or production methods based on the big data provided.

 

In contrast to the data scientist, the data analyst is dedicated to analyzing the generated data volumes not only to derive future potential, but also to solve current problems. So if the aim is to reduce current production costs or the current consumption of a certain material, the data analyst is the right person to contact.

 

These are the main tasks of a data engineer, data analyst and data scientist at a glance:

 

• The Data Engineer takes care of the extraction of data, the data flow to the central storage location and the harmonization of data as well as its visualization.
• The data analyst analyzes the data in order to derive insights that contribute to solving current problems or achieving current goals.
• The data scientist analyzes the data with the intention of deriving opportunities and potential for the future of the company and its processes from the data volumes.

 

The quality of the analyses performed by data analysts and data scientists depends on the work done by the data engineers. With an attractively prepared database provided by data engineers, data analysts and data scientists can derive accurate insights from big data, which promotes high-quality decisions in companies and makes a decisive contribution to business success.

Data warehouse as the basis for the work of data engineers

Data engineers need an architecture to be able to collect and visualize the generated data. A fundamental component of this architecture is the data warehouse. This is a central storage system in which all the data from the various sources is linked together.

 

Examples of data sources include enterprise resource planning software (ERP), customer relationship management software (CRM) and devices (e.g. industrial machines, barcode scanners). The big data generated from these sources is fed into the central storage system, where it is sorted and categorized.

 

By connecting the data in a central warehouse, it is easier to harmonize, visualize, analyze , report and make decisions based on the data.

 

Without a central data warehouse, combining data from heterogeneous sources would require an enormous amount of effort. In addition to harmonization, data warehouses also contain the following functions for handling data:

 

• Carrying out ad hoc analyses
• Creation of user-defined reports
• Clustering based on logical or self-defined categories

 

Let's summarize for now: The generation of big data in ERP, CRM and other sources is followed by the consolidation of data volumes in the data warehouse.

 

This is followed by data mining. This refers to the derivation of findings from the data analyses. Data analysts draw conclusions from the data obtained and identify, for example, how business processes can be optimized, how the customer experience can be made more varied or how the waste of resources in certain stages of production can be reduced.

 

Data engineers could also do their work without a central storage system. However, a large part of the work would be spent on the isolated sorting, categorization and combination of data.

 

By using data warehouses, data engineers are spared these time-consuming processes and can provide data scientists and data analysts with better preparatory work so that data-driven decisions can be made more efficiently .

 

Data integrity: an elementary quality factor in the analysis and processing of data

In the age of big data, data integrity plays a central role. Data must be correct, complete and consistent. A negative example of this is contradictory information in companies' annual reports. If employees have manipulated or incorrectly maintained the data collected in the controlling tools, the supervisory authority could notice this when auditing the annual financial statements and initiate proceedings.

 

Data engineers, data scientists and data analysts have extensive obligations to maintain data integrity. If they fail to meet their responsibilities, or do so inadequately, this can have far-reaching consequences for companies. If problems do not arise with the authorities, the consequences could be wrong decisions and, for example, wasted resources in production.

 

Data integrity includes aspects such as data protection, protection against cyber attacks and the quality of the data. A rough distinction is made between physical and logical integrity.

 

Physical integrity describes the correctness of data during its storage and use. Logical integrity refers to the immutability of data, which includes protection against manipulation.

 

Data quality as a component of data integrity presupposes that the data meets the standards and requirements of the companies. The decisive factors here are that the data is up-to-date, correct, complete and reliable.

 

High-quality data can be obtained by using professional tools in as many work processes as possible. One example is process mining tools, which provide automated data on production processes and ensure a continuous and up-to-date data flow.

 

This information on data security and data quality makes it clear how extensive the field of data integrity is. Data integrity promotes the precision of data engineers' work and contributes to high-quality analysis results. This means that better conclusions and decisions can be drawn from big data.

 

Outlook for the future: focus on big data and data science

Most leading companies have understood the increasing importance of big data. Those that have not grasped it have had to cede market share to the competition or have disappeared from the scene completely.

 

In the age of digitalization, decisions based on thorough data analysis are fundamental to remaining competitive, satisfying customers and growing as a company.

 

Data engineers and the infrastructure they create are key to processing big data properly. Cloud technologies such as Azure Cloud make it even easier for companies to generate comprehensive data sets.

 

Given the fact that companies are increasingly using cloud offerings and the amount of data generated is growing exponentially, data engineering is one of the IT areas of the future. Consequently, all companies should invest in the development of data engineering technologies.

 

Conclusion: Use data engineering and solve problems better

High-quality data engineering is an integral part of the search for solutions in companies. It serves as the key to sorting and visualizing data and using it to develop innovations, satisfy customers, make production processes more efficient and achieve many other goals.

 

There is customized software, such as our Power Platform, that makes data engineers' work easier and increases the quality of data engineering.