How Data Engineering Facilitates Company Work
How Data Engineering Facilitates Company Work
Value of Data Engineering
When we look at data engineering as a field of study, the ultimate goal is to make the data accessible, so that organizations can use it to evaluate and optimize their performance. The need for proper data management in any company is a particularly complex but generally solvable issue. If we look at the amounts of data that companies generate on a daily basis, which is continuously increasing every day since the digital revolution, we can get a general feel about the grandeur of the task. Overall, the generated data is of great importance to the companies, so they can conduct analysis and gain great business benefits.
The global data growth trends predict that by 2025 there will be 463 exabytes of data generated on a daily level. These substantial quantities of data are fundamental for the scope of artificial intelligence, including machine learning, deep learning, reinforcement learning, and many others. It is important to note that the predictions would not give accurate results if the data isn’t previously properly cleansed. As a result, in the past 10 years, as data exponentially grows, data engineering is a field that is one of the key drivers of company growth. The main goal of data engineering is to design and build systems for extracting, transforming, and storing data at scale.
The main goal of data engineers is to enable the full availability of data so that organizations can evaluate and optimize their performance. More importantly, to contribute to creating additional value for their companies. The advantages provided by this field include better company decision-making, greater precision in data analysis, as well as identification of the potential risks and business opportunities.
Generally, we can distinguish the area of business intelligence which has a role to generate and execute on-premise ETL (extract-transform-load) processes. On the other hand, the role of data scientists mainly includes analyzing and modeling data on cloud environments. In this context, the data engineer creates and executes local and cloud ETL processes for data science purposes. This is an advanced administration process that includes extracting, transforming, and storing data that allows for discovering optimal solutions for better business processes.
To further understand the complete need and benefit of data engineering in the companies, we’ll take a look at two perspectives.
As we mentioned before, companies today with their complex business processes, on a daily basis generate large volumes of data that is necessary to be stored for further analysis. Companies always have an option to locally store data, which requires a local server structure and physical location servers. The main question that is brought up here is: can these on-premise hardware infrastructures support the data that is going to be generated in the future? Can the same environment load data even after 5 years with no data losses?
Looking from another point of view, the current on-premise environments may be great at handling the current business processes, but whether the whole infrastructure of the database will need to change if we have new types of input data in the system is a question that needs to be asked. Maybe we need to extract data from different types of databases along with their proper integration, or we want to generate an additional database on the same server.
In this perspective, the main question is – do the current employees from different sectors in the company need to have prior knowledge about these data migrations? Do data scientists need to focus their time on their own analysis or do they need to care about storing the data?
The answer to all of these questions is the cause for the recognizability and need for data engineering. The basis of this area of study is based on three aspects: extracting data from all relevant sources, transforming the extracted data into the correct form needed for further analysis, and loading the databases with the appropriate architecture. These processes are summarized in the ETL process. Additionally, data engineers have to optimize on-premise environments as they are previously configured, and adding more data and processing power can jeopardize the built solution. On another hand, the use of cloud environments is rising as their optimization and adding resources is done with a click of a button.
Raw data is collected from various sources such as:
Oracle SQL Server, flat file, Teradata, etc
Data is stored in the staging area for transformation
Processed data is stored into the data warehouse for use
To understand the challenges the companies face today, we’ll consider one scenario from a company consisting of three departments that run their own independent business processes (production, external communications & delivery department). Every department works with its own database which is stored locally on a computer. According to their needs, each department generates different types of reports stored in different formats (.txt, .xslx, .parquet) and generated at different time intervals (daily, monthly, quarterly).
Our job, in this scenario as a data engineer, is to analyze if the revenue of the delivery department is greater than the costs of the production department and what impact customer satisfaction has on the realized revenue.
To start the process, it is necessary for the production and delivery departments to generate and send transaction reports from the previous year (because we don’t have access to their local computers, we ask the reports to be delivered by email). The external communications additionally send the client messages from a web form. When we gather the necessary data, we start the calculation process and discover that the unit of measurement is different in the two different reports and we must transform it; the reports are generated in different time intervals and it’s necessary to be summarized and we detect inequality in the services listed in the various reports. To fulfill our final task, we must manually correct the differences and calculate the reason which delays our process deadline. Once we manage to complete the entire process (with double checking due to miscalculations) we need a location to export the final report and we can not choose which database will it be uploaded to. The next time we want to repeat the given analysis, we will need to go through all these same steps.
Even though this process may seem imaginary, it is in fact common in certain companies that do not have a specific way of administering their data. Even more important are the potential losses of the company, which would be due to these inconsistencies and delays. With this example, we can notice that data administration is an unavoidable process, but it’s up to the company to decide how to deal with the issue – either manually do the calculations or hire a data engineer to standardize the data and incorporate it into a flexible database for wider use. Obviously, the latter is a far better and cheaper option for the business.
Benefits of Data Engineering
Even though data engineering isn’t always the most necessary part of the company structure (depending on the company), it can present a great benefit. If the company generates and works with vast amounts of data, if the data is variable or it includes new data for a certain time period, data management is a top priority. For all the business processes to function continuously, a data engineer can work on standardizing and structuring the whole data flow and the data itself. Data engineers ordinarily have the task of identifying and gathering all of the needed data, transforming it, and standardizing it, with one main goal – to create a database with appropriate architecture.
If your company or the company you’re working for is dealing with issues similar to our examples, hiring a data engineer can be of great help. Otherwise, there is a real chance the company will have to deal with unusable, unstructured, and redundant data.
Given that most companies generally store their data in the cloud for easy access, the cloud platforms offer all of the required services for end-to-end data engineering. The most common cloud platforms are Amazon Web Services, Azure Cloud Services and Google Cloud Platform. More about that in the next blog, where we’ll be giving an in-depth look at data engineering on a cloud.
Benefits from Data Engineering = https://www.devlane.com/blog/benefits-of-data-engineering