The ever-increasing amounts of data, which prevail in companies and based on which they align their future business development, are becoming increasingly important. But how are companies dealing with this development?
Big Data – A Conceptual Approximation
What does the term big data mean? Big data refers to storing, processing and analyzing enormous amounts of data. These data sets consist of structured and unstructured data. In addition, these are often so large that they can no longer be processed with conventional hardware and software. Therefore, special hardware and software are required to work in a combination of many computers that communicate and solve this problem. It goes Around dragnets, (inter)dependence analysis, environment and trend research, and system and production control. The global volume of data has grown so much that previously unknown possibilities are opening up. And what matters is what companies do with the data.
Why Is Big Data So Important?
According to a study from Bitkom, the data volume in nine out of ten companies increases by an average of 22 percent within a year. So the saying goes that data is the new oil for business. Due to the increasing interconnectedness of life, new data points for storage arise in many different places. Every purchase, every visit to a social media platform or every process in a production line leaves a lot of data behind. And since all companies with more or less complex business processes generate vast amounts of data, it is time to see data science in connection with data analytics as a great opportunity. Analyzing this data has several positive effects, such as more efficient market research, adjustment of online advertising measures, or the development of new business areas.
What Are The Tasks of Data Engineers and Data Scientists, And In Which Industries Are They Needed?
A data engineer is responsible for all processes related to data generation, storage, maintenance, preparation, enrichment and transfer. These activities form the basis for big data, data warehouse and analysis projects in the context of data science. Within this area of responsibility, databases are modeled and scaled to ensure data flow within a company.
On the other hand, a data scientist acts as an interface between the scientific field of mathematics and computer science expertise. He works on creating a structured database from unstructured raw data, analyzing it and ultimately creating a basis for decision-making for a company with his business know-how.
A data scientist is wanted wherever large amounts of data are generated. Companies have an interest in learning from existing data and optimizing existing processes. Central areas of application include e-commerce and the entire online industry.
What Skills Do a Data Engineer and a Data Scientist Need?
It is yet to be possible to study data engineering as a degree. For this reason, this is a profession for classic lateral entrants. Based on the area of responsibility, the following skills for a data engineer can be derived:
- relational databases
- ETL tools
- Big data technologies (Apache Spark, Hadoop or other no-SQL databases)
- Cloud technologies like AWS S3
On the other hand, a data scientist usually completes a degree in computer science, mathematics/statistics, physics or engineering. He usually starts as a generalist and has the opportunity to specialize in individual fields of application with increasing professional experience. The following skills are required to practice this profession:
- machine learning
- deep learning
- Programming languages (SQL, R, Python, Java, SPARK)
- communication skills
- Analytical skills