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Data Engineer’s Responsibilities: Shaping the Big Data Success

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Current and future business decisions rely heavily on tonnes of raw data that is generated in massive amounts daily. These are data engineer responsibilities to create infrastructures that will organise, classify, and store these piles of data for your company. Whether your business lies in tech, financial, e-commerce, or any other segment, hiring data engineers might be one of the first tasks before you start your business project.

With the sweeping development of artificial intelligence, machine learning activities, and immense automation in various businesses, the data engineering process helps companies find their own path in the digital world.

So, how exactly can data engineers assist your company to proceed with big data?

Data engineering process

Data engineers are the masters who transform the raw data from various sources into algorithms and data structures, store them in warehouses, and prepare for future use by data analysts and data scientists. The steps in the data engineering process depend on the usage of big data and on a company’s requirements. Big data is often described as various, voluminous, and velocious. The bigger the data, the more complex the process a data engineer will go through, especially if the data should be prepared for further application by AI. 

Defining appropriate tools and technologies

Since data for processing can come from various sources, one of data engineer’s responsibilities is to define the tools and technologies they will be working with. The data can be extracted from various CRM systems, from public sources, from SQL databases, or from websites in structured or unstructured form. Data engineers define the tools for design and data extraction. If a project includes big data, such tools as SQL, Kafka, or Hadoop will be common to use. They should also understand the business requirements and type of data analytics for their endpoint of the process. This is necessary as the approach will differ if the data is needed for machine learning, AI teaching, or business intelligence applications.

Data extraction and collection

During this stage, a data engineer collects all the necessary data from various sources and puts it into a structured and understandable form for further application. An important data engineering role here is to also integrate the raw data into a company’s infrastructure for future storage. Before the data becomes usable, it also goes through such stages as transformation, cleansing, annotation, and validation. 

The most common and widespread process is ETL, which signifies extract, transform, and load. The steps of the process can differ, but it’s a data engineer who decides its sequence. Extracting of the raw data starts from various servers, including databases, emails, files, etc. The step of data transformation involves all filtering, cleansing, calculation, or encrypting activities. The final loading step moves the extracted data to the repository. Usually, the loading phase is automated and happens in the background of standard activities. However, it is important that the data engineer test at the end to see if the data transferred didn’t cause any errors and is free from any discrepancies.

Data storage

Before the data reaches its repository, or storage, it should be transferred from the sources through created pipelines. It is one of the data engineer’s responsibilities to create pipelines that correspond to the business requirements. In the form of streaming or batch processing, data pipelines lead to storage. Depending on the amount and complexity of data, storage is usually represented by data warehouses, data lakes, or data marts. When the data engineering role deals with big data, the storage usually takes place in storage lakes, which can contain much more data than the warehouse, including structured and unstructured sources. 

Testing and deploying 

It’s not enough to create pipelines and collect the needed data. Data engineers collaborate with testers to maintain and test the system for errors, inconsistencies, and reliability. If the data needs to be updated on a regular basis, data engineers implement automations to reduce manual interventions. Before the actual deployment of the final result, the data engineering should also go through the process of compliance. At this stage, data engineers implement security and control checks and accesses in accordance with the project requirements.

What are the benefits of big data processing?

We hear of the necessity to work with big data, of companies leading automation projects, of the digitalisation era, and of the new opportunities technologies bring to our business. But what exactly happens when a data engineer finishes their work and the created pipelines and storage move to data scientists? Which results can a company receive after big data processing?

Data engineers help companies use big amounts of data smartly and gain from using it and implementing it into their systems. Processing big data is needed for analysing behaviours, building trends and forecasts, optimising work, modelling AI functions, or machine learning. 

Big data also contributes to such processes as decision-making, innovation, flexibility, and increased performance. No matter the sector your company operates in, data engineering helps to analyse the behaviour of your customers, personalise your product based on your target audience, adapt your strategies to become more competitive, stay resilient in times of market fluctuations, etc. – the list really can be endless.

To ensure that a company achieves its set goals, data engineers create a protected infrastructure that is structured and provides all the needed information for further analysis. However, it is important to remember the ethical part of data usage and take care of data’s security and privacy.

Since data engineers are in extremely high demand these days, you may spend much time finding a suitable professional for your project. Turning to outstaffing partners can be a smart action, which could save you costs, accelerate finding a fitting specialist from all over the world, and bring new ideas to your project.




David Radar, a psychology graduate from the University of Hertfordshire, has a keen interest in the fields of mental health, wellness, and lifestyle.

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