Data Work Concepts

Under favour of ‘Hello Data’, I tried to define data as a phenomenon and explain in which context the data is used in the IT field. It was like a drop of a huge sea. I will continue with these concepts in the data field a little more. I will try to explain the workforce distribution in a few main headings, and thus we will have a look at the titles we will encounter in the business world and the technologies these titles use.

database administration

  • Database Administration

The majority of IT projects in our age are based on data. Database systems are also used to store data. Okay so far. But you need to be sure that this database will be active all the time. In addition, data should be kept intact, data backups should be kept, the database should be prevented from collapsing in the event of a disaster, its performance should be monitored, access rights to the database should be audited … For all of these, there is a need for someone who knows how the database system determined in the project works and will be able to master it. This person is DBA, fully responsible for the work I have written above. Usually a DBA specializes in a particular database technology and knows and dominates how the database management system works at all levels, visible or invisible. As the projects grow, the areas of responsibility of DBAs are distributed among them. Like the DBA responsible for performance or the DBA responsible for backup.

Examples of titles in this field; Such as Database Developer who writes programs running in the database management system, Database Administrator undertaking management and maintenance operations, Data Security Administrator responsible for security solutions..etc.

data architecture

  • Data Architecture

DBA is the responsible person in the management of the database in a project and it is not expected to have much command of the business logic in the job. Also, data is not something static. It constantly bends and moves. Moreover, it is necessary to have more information about the business logic in these movements. In software architecture, business rules are transformed into computer technology, and in data architecture, the relationship of these technologies with data is revealed. Here, a person should put forward an architectural structure in order for the technologies in the system to work with the data in the most ideal way and to ensure that the business rules are also operated in the data layer. This person who will reveal how the data will exist, how it will have harmony with different technologies, how it will move, where it will be transformed and its harmony with business logic with an architecture is called Data Architect.

Examples of other titles that can also be defined as Data Architect; Technical Information Specialist, such as Cloud (Data) Architect if it carries its work to cloud infrastructure, and Solution Architect if it has responsibility for software architectures..etc.

data engineering

  • Data Engineering

One issue that does not fall out of the responsibility area of Data Architecture is, of course, the implementation of the design. It has been demonstrated where and how data will flow, but someone has to lay these data pipelines. In other words, engineers need to work in order to handle the design’s development and operations level. This title, which is defined as Data Engineer because it includes both development and operation, includes tasks such as extracting data from certain sources, loading it to other targets, transformation of data and automation of works. The roles and responsibilities of Data Engineers can vary greatly depending on the richness of the architecture.

For this reason, the distribution of titles is also quite diverse; Data Engineer, Big Data Engineer for those working in the big data ecosystem, Cloud (Data) Engineer for those working in cloud infrastructure, Data warehouse Engineer / Developer for those who work on designing data warehouses, and ETL Developer for slightly more tool-oriented employees..etc.

business intelligence

  • Business Intelligence

Time itself is almost an investment for data generating businesses. As a functioning institution, you have developed your software for the tracking mechanism of your business and you produce data with this software. Your data has been growing exponentially over the years. Here, our data accumulated over these years, in fact, they have more than the meaning they instantly express to you. The first step in revealing these meanings is Business Intelligence. The more consolidated and organized form of classical reporting processes is called Business Intelligence. We need such a system because certain methodologies and technologies are needed to process this batch of data, which is stacked on a time-indexed basis. One of these is the Data warehouse you hear a lot. The concept that we call data warehouse actually emerged with the aim of creating Business Intelligence reports. By the way, it is not expected from anyone who will work in the field of Business Intelligence to have a technical skill set. It is important that people working as a BI Analyst or Data Analyst have skills such as reporting and strategic perspective rather than technical details. Of course, there are no sharp lines and boundaries between analysts and developers in this area.

data science

  • Data Science

I said that while defining Business Intelligence, it is the first step to reveal the meanings of data. Because BI is like a more organized version of classic reporting. However, the data contains many patterns and gems that even those who produce it cannot understand. The branches that need support here are mathematics and statistics. The journey to discover these meanings with the methods demonstrated by science is called Data Science. The concept, mostly referred to as Data Mining in its early years, has become a science with the enrichment of the methods used. Even now, using these methodologies, we can not only make sense, but also teach the computer itself, and even later enable the computer to generate new data independently. Data Scientist and Machine Learning Engineer are among the most common titles we encounter in this area, where we need a long article even for its definition.

Written by Muhammed Sezer

A Data Engineer held responsible positions in several projects as Architect, Developer and Team Lead. My strengths and qualifications are based on Data Modelling, Data Warehousing, Intelligent Systems and Data Pipelines.


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