THE FUNDAMENTALS OF DATA ENGINEERING: KEY CONCEPTS AND TOOLS

At every 24 hours, about 402.7 million terabytes of data are created each day. Created in this context includes data that is newly generated, captured, copied or consume . In zettabytes, that equals around 147zettabytes per year.
However, as the demand and consumption of data surge, it is pertinent to have an understanding of how data is collected, processed, analyzed, Stored etc. Thus Data Engineering
In this article, we’ll dwell mainly on the basics or fundamentals of data engineering exploring the key-concepts an the tools which make data engineering possible..
Are you new in the tech world? Do you want to learn how data engineering works? Or maybe you desire a skill in data engineering; no worries have got you covered . Sit tight an have fun as I take you through this journey.
Data engineering has been in existence for years now an has been expounding over years.This have played a crucial role in our world today. Arguable, it have been one o the hottest role in the world of tech over the years an companies are scrambling to build the infrastructure to handle the ever growing flow of data generated.
To this end, let’s know what data engineering is. Data engineering is the process of building, maintaining an optimizing data. This involves collecting, processing, analyzing an storing data in repositories for further use. Simply puts, Data engineering involves collecting, transferring,processing , analyzing an storing data in large scale . Individuals who perform this roles are known as DATA ENGINEERS.
Data engineering is sometimes called information engineering. This is because it revolves around data which is transferred into a meaningful information for user consumption.
I guess after reading it’s meaning you might want to know as new learner why you should even choose data engineering after all. Here’s why:
1.There’s a high failure rate of big data projects (around 85-87 %)
2.There’s failure due to unreliable an quality data
3.There’s growing importance an demand of data engineering role an lots more.
However these (reasons for choosing data engineering) wasn’t really important in topic of discuss but it will help lighten the mood of an enthusiastic an potential data Engineer. Ok?
Howbeit, after having an understanding of what data engineering is an why to have a knowledge about it, let’s delve into how data engineering really works.
Data is collected from various sources such as the social media, the internet,newspapers, research etc This data is then processed transformed an stored for user accessibility. It is best to know the auntentication of data when fetching it from various sources.
Nobody will want to process fake or outdated data anyways .

KEY CONCEPTS IN DATA ENGINEERING
This takes us to key concept in data engineering. We’ll be looking at few concepts such as DATA PIPELINE, ETL( Extract, Transfer an Load) an Also DATA WAREHOUSING. Keep this in mind , it will be handful in understanding Data workflow later on in this article .
Data pipelines-: encompasses the ingestion of data from various sources which is followed by transforming an processing an then it is ported to data warehouse or repository for analyzing .This follows the ETL process.

ETL which is an acronym for Extract Transform Load.
Data is Extracted from disparate sources it is then Transformed by processing an Loading or ported to data warehouse for user consumption as earlier explained.
Data warehousing on the other hand, is the processing of storing data for for further use. Data is stored for accessibility, Availability an Security.
These process are complex an is of great details but for the purpose of our discussion well only look at it surface.
Links will be made available at the end of this article for further study to have a deeper understanding of some subject matter.

TOOLS AND TECHNOLOGIES
However this process are made possible an with ease . Thanks to Data Engineering Tools an Technologies .They are various tools an technologies which made this process effective. This includes Apache Hadoop, Apache spark Data Airflow an cloud platform such as Microsoft Azure, Goggle Cloud Service (GCS) an Amazon Web Service (AWS). Alright then, let’s hav a look on how these tools are applied.
Apache Hadoop: is an open source Java-based platform that manages data sets an distribute them across a node by a parallel process . In simply terms it helps to process an break data into simpler bit so that it can be easily transported. This make the process a lot easier.
Apache Spark on the other has the ability to manage an process big data set . It reads data from multiple data source, performs data transformation an distribute computing task efficiently .
However, Airflow aids in running complex data pipelines.
With the aid of these three tools . Data Engineering is made a whole lot easier . Cloud platforms such as Amazon Web service Goggle Cloud an Azure are also the technologies used in Data engineering for process , analyzing an storing data.
The Amazon Web service does the ETL service. It support both visual an. Coded based ETL job creation and it can automatically generate ETL code for data transformation.
Moreover, The Google Cloud Platform( GCP) is a fully managed stream and batch data proceeds services. It allows users as data engineers to build data pipelines fair processing an transforming data.. Most of the terms used here have been discussed earlier in the introductory part of this article. Do well to read in between the lines for a better comprehension.

To apply these tools an technology. There’s a pattern to fellow an that leads us to
Data Engineering Workflow .

Data Engineering workflow is a series of operations followed in sequence by data engineering teams to scaly and repeatedly execute data operations. Without this , the building maintaining an scaling of data product an pipeline would wreak havoc on modern data organizations.
Data workflow follows this pattern:
Data ingestion, Transformation, Storage an Orchestration .
Once data has been ingested from various sources it is then streamlined an edited stored prepared an presented to be used by either Data scientist or Data analyst .

The most interesting part of Data Engineering is it’s real life application; it cut across all areas of life ranging from health, Finance, marketing, commerce , politics etc . It’s importance can not be over emphasized.

In the health care system; through reliable an efficient data the health improvement of a patient is monitored. There data are made available by data Engineers.

marketing and Commerce: the act of decision making is whole influence by the availability of data. Business tracking; either profit or loss is monitored by data made available by data engineers.
Moreover, in marketing , production-consumption rare is also monitored by data which have marketers to know the consumption rate of their products an improvement to make in the product.
In Politics . You know how a candidate’s pedigree is very important .Candidates track record, pedigree an success rate is monitored through Data.
The list gels on an on as it’s application cut across all phase of life.

CONCLUSION
Dear friend I’m happy I’ve been able to take you through this journey an I’ll like to appreciate for following through. I believe by now you’ll understand the Fundamentals of Data Engineering, it’s key-concept , The tools an Technologies employed. The Data Engineering workflow and Most importantly it’s Real life application.

As a young an hungry learner which I believe you are, I thought letting you know the modern Trends wouldn’t be a bad idea . Below are a few modern trends in Data
Engineering you might love to look at:
1.Data Mesh
2.Real-time Data
3.Big Data
4.Data warehousing
5.Edge computing an
6.Arguments Analytics etc.

With those trends mentioned above you’ll have an updated knowledge on data Engineering.
Good luck in your Journey of becoming a Data Engineer.
HAVE FUN!!!
Written By:
Thompson God’swill

External source:
1.Data Engineering Fundamentals: A Complete Guide by Laerco de Sant’ Anna Filho(Data Engineering Fundamentals: A Complete Guide https://laerciosantanna.medium.com/data-engineering-fundamentals-a-complete-guide-bbe42292bd82)
2.Data Engineering for Everyone: edx app