Data Engineering: What Is It And Why Is It Important?

on

|

views

and

comments

Data engineering is a part of data science projects whose actual importance has only been recognized in recent years. Data engineering plays a crucial role, especially when it comes to putting data science use cases into production. In this article, we will consider what data engineering is and why is it so important?

Data science projects are the result of teamwork. Contrary to classical IT tasks, which are clearly located in the IT department, there is no such thing as a data science department or a data scientist. Instead, employees with different skill sets and from different specialist areas are required, who are jointly responsible for the success of a data project. One of the most important areas of every data science project is data engineering. 

The basic tasks of data engineering

Contrary to other professionals in this field, such as the data scientist, the data engineer does not enjoy the same degree of attention or fame. Nevertheless, data engineers are very few and far from the actual requirement. Since, without data engineering, an essential criterion for project analysis is missing: the handling of data. 

Data engineering deals with the collection, preparation, and validation of data, as well as ensuring that the infrastructure and application required for analysis are in place. 

What is data engineering exactly?

The primary work area of data engineering are databases, data warehouses, and data leaks. Simply put, the primary task of the data engineer is to provide data. Data engineering services are regarding modeling and scaling databases and thus ensuring data flow. Thus, data engineering can  include the following sub-areas:

  • Database design and configuration
  • Programming of specific applications
  • Configuration of interfaces and sensors
  • Conception and provision of the system architecture

The data engineer’s responsibilities also include the maintenance and administration of IT infrastructure, even if it is not included in one of his primary tasks. The size and budget of the respective organization usually decide whether there are responsible persons for such specific tasks are here or not. However, at least in terms of technical training, a data engineer can take over these responsibilities in part or in full. 

Data Engineering has been crucial for various leading organizations to outperform their competitors. In several businesses, new entrants and established competitors use data-driven strategies to compete, capture and innovate. As a matter of fact, one can find several examples of data engineering usage in almost every sector, from IT to healthcare.

When we consider healthcare businesses, data engineering has been used to analyze the outcomes of pharmaceuticals. Businesses are actively using data engineering to discover the risks and benefits that were not clear during the initial clinical trials. In addition, big data can also provide a better analysis of the trials, as well as help predict reliable outcomes. 

Moreover, data engineering can also create several new growth opportunities. It can also give rise to a new business category, such as the ones that analyze and aggregate industry data. Most of such organizations will be sitting in the middle of large information flows regarding products and services, buyers and suppliers, consumer intent and preferences, and more. Businesses across industries must start developing their data engineering capabilities aggressively. 

Data Engineering Case Studies: 

  1. By applying Responsible Testing, 3PL realizes the full potential of Big Data.

3PL is a two decades old asset-based third-party logistics service provider company of both brokerage & freight management and asset-based services of dry van, refrigerated, and dedicated/private fleets.

3PL used various off-the-shelf systems to help run their business. As the legacy systems collected a massive amount of transactional data, collating it and extracting meaningful insights was a challenging task for the business. Because of data silos, stakeholders received various out-of-sync and unverified data, developing inaccurate insights. Such a thing led to unreliable data on various key parameters across different aspects of the revenue cycles. The business urgently required a reliable QA & Testing partner who had an in-depth understanding of Big data and BI services. 

How data engineering helped solve 3PL business challenges:

Firstly, we assessed the eight primary applications and primary data sources:

  • On-premises systems ( McLeod Imaging ShoreTel VoIP, McLeod TMS Systems)
  • Third-party cloud systems ( SalesForce, BlueGrace, EFS, PeopleNet) and
  • Flat File (Budget Data)

Designed and developed a business intelligence system that included:

  • A data extraction source system. To assist in loading the Flat Files to the data mart server, the Extract, Transform, and Load (ETL) procedure was implemented.
  • A scalable and adaptable data mart layer that acts as a ‘Single Source of Truth/Data’ for all updates.
  • Users may see and access reports through browser or mobile apps on the BI Server, which serves as a repository for all reports and dashboards.

The QA team carried out end-to-end system and integration testing, as well as analysing and creating different test data sets, which is critical for big data testing. Data quality was checked at every level of the BI application. The team tested the data stream and assured correct report production across operating systems (OS), browsers, and devices using our Responsible testing strategy.

  • Healthcare Industry

There are several heterogeneous data sources in the health industry that provide a vast quantity of information about individuals, diseases, and health centers. This data, when correctly examined, is extremely beneficial to health experts.

The use of so-called Big Data methods enables the inference of a layer of intelligence, with predictive models that help anticipate health demands and provide more effective medical care being particularly significant.

Leverage the power of intelligent data analytics with Trigent

The spectacular growth in Big Data and analytics emphasizes the importance of these technologies in navigating through the business landscape. At Trigent, We can help businesses leverage the full potential of data analytics. Our technology experts can guide you to ensure you make the right AI investments, eliminate data silos, and create cutting-edge business solutions.

Share this
Tags

Must-read

What kind of silicone surgery should I use?

This question is a popular question. In surgery, many people suspect that the face. What kind of silicone is suitable for mice? Because as...

Simple Link Building Strategies For Success in 2023 and Beyond

Recent SEO statistics show that most internet pages do not have any backlinks. There is an apparent need for more SEO campaigns to build...

Most Common Reasons Your Sump Pump Is Running When It Isn’t Raining

The sump pump is a vital addition to any home in a flood zone. It kicks in as soon as water is detected and...

Recent articles

All Categories

More like this