DEV Community: God'swill Thompson

Understanding ETL pipeline

God'swill Thompson — Sat, 14 Sep 2024 16:15:46 +0000

“Understanding ETL Pipelines: Extract, Transform, Load in Data Engineering “

INTRO
Hey there,
In this article, we’ll be exploring the ETL Pipeline process in Data Engineering, it’s importance, working principles an real world Application.

ETL stands for Extraction, Transformation and Loading.
It encompasses the process of gathering Data for various sources, processing the Data an Storing it for User consumption; either a Data scientist Oran Analyst.

ETL is a very key concept in Data Engineering. As an aspiring Data Engineer, you wouldn’t want to miss this.
Stay with me!!.

Basically, ETL can be defined as a process which involves “fetching out” data from disparate sources such as relational Databases, APIs( Application Programming Interference), Web service, Web scraping etc, and transforming this Data into meaningful an an resourceful information and storing them in Data warehouse or repositories.

Data sources haven’t been so wide as now. Therefore this calls for a need to pay attention to Data an it’s processing. As Companies try to cope with large amount of Data they deal with form day to day business, they tend to look for a better way to improve the process of gathering this information , Processing them an Loading them up for storage .

However, that’s where ETL comes in. It is very essential for processing and managing large-scale data in Data Engineering Workflow.

We’ll be looking at this in clearer Details in the course of this article. Let’s jump right in !.

The Processes

EXTRACT; Mining:- This is the first step among others in the ETL process. The extraction stage has to do with collecting data from heterogeneous sources ranging from Flat files, APIs, websites etc.
Here, the Data is unprocessed an it’s gotten from identified source by the Data Engineer.
However, it is important to note that when extracting data, only useful data are extracted not just “some Data”.
Also, it’s pertinent to note that while extracting data the quality y an authentication of the Data is necessary.

TRANSFORM; refining :-

This is the next step after extraction. It involves the process of cleaning, moulding, merging an Enriching the Data extracted. This is the most vigorous part of the ETL pipeline process. It consumes a lot of time but it’s worth it. This is where the raw data is processed into meaning and useful information. It can also involves merging data from various sources.
Note that in the transformation stage of the data, compliance to the Data regulations policies is important. What this means is that Personal and sensitive Data are managed properly inline with the Data security an Privacy Policy.

LOAD; Use. :-
After a vigorous process of extracting an transforming data into useful information, the Data is now ready for storage or analyze.
This stage involves Data warehousing . The data warehouse stores clean and processed data which will be useful for BI (Business Intelligence) cases or for Data Analyst .
On the other hand, data lakes is use to store all type of raw data which is useful for data scientist.

TOOLS FOR ETL
The ETL pipeline process will not be possible without this tools employed.
The three main power ETL tools include Apache Airflow, Talend and AWS glue.

The Apache Airflow aids in running complex data pipelines. It is an open source platform that automated, process and manage Big Data.

AWS(Amazon web service) also automated data. It helps to manage an monitor data ensuring high quality across data lakes an Pipelines.

Talend on the other hand aids to fasten data mobility an arrangement. It uses Java programming language an it is also useful for visual representation of Data using Drag an Drop. It is flexible, accurate an efficient.

REAl LIFE APPLICATION
After understanding what ETL is, the processes an tools employed for the ETL process, let’s have a look at some of it real life application

ETL is used in several field for data processing such as Healthcare sector, marketing, Banking an finance etc.

Data of patients medical history diagnosis an treatment methods are gotten by health institutions on a daily basis . The ETL process helps them to collect , process an store this data which will in patients treatment, accessibility an Security.
Marketing firm get data of how do their product are going in the market. This helps them in competition an helps them to know what areas of their products to work on.

Banks gathers data daily such as debit,credit alert, customer complaints etc process them an store them for analysis, security an reference. All these employs the ETL pipeline process

CONCLUSION
The importance of understanding the ETL pipeline process as a potential data engineer cannot be over emphasized. It’s application helps to produce efficient, meaningful and accurate data for analysis. It is also important to pay attention to the tools employed to aid this process.
All these will be handful in your data engineering journey.

Catch ya next time!!!

Fundamentals of Data Engineering

God'swill Thompson — Mon, 09 Sep 2024 22:22:49 +0000

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