Data Engineering for Beginners: A Step - by - Step Guide.
Tags: datascience, dataengineering, python
Published: 2023-11-07 09:38:11

Data is the Future, and the future is NOW!- In the modern era, data engineering is more than just a profession; it's a pioneering journey. It empowers organizations to derive insights, predict trends, and make informed decisions. It's the key to unlocking the potential of data, making it the lifeblood that sustains business growth and innovation.

> "Data engineering is the art of turning data chaos into data clarity." - Lou Powell


## Who is a data engineer?
A data engineer essentially is anyone who serves as a gatekeeper and facilitator for the movement and storage of data. Data engineers are also often tasked with transforming big data into a useful form for analysis. In order to do this, they design, construct, install, test, and maintain highly scalable data management systems — basically, software needed to store and use this data.



## How to become a data engineer
A combination of certifications, hands-on practice and experience are powerful combos for landing a lucrative job. 
The following are ways u can venture into  your data engineering career:

1. University degrees. Useful degrees for aspiring data engineers include bachelor's degrees in applied mathematics, computer science, physics, or engineering. Also, master's degrees in computer science or computer engineering can help candidates set themselves apart.

2. Online courses. Inexpensive and free online courses are a good way to learn data engineering skills. There are many useful videos on YouTube, as well as free online courses and resources, such as the following six options:

- Codecademy's Learn Python. [https://www.codecademy.com/learn/learn-python](url) is essential for data engineers. This course requires no prior knowledge.

- Coursera's guide to Linux server management and security. This four-week course covers the[https://www.coursera.org/learn/linux-server-management-security](url)
 

- GitHub SQL Cheatsheet. This GitHub repository is consistently updated with [https://github.com/enochtangg/quick-SQL-cheatsheet](url)

- O'Reilly data engineering e-books. Titles in the [https://www.oreilly.com/data/free/](url) cover data engineering topics.

- Udacity Data Engineering Nanodegree. Udacity's online learning offerings include a [https://www.udacity.com/course/data-engineer-nanodegree--nd027](url)

3.You can gain more insights from[https://dataleum.com/academy/](url)



## Key Responsibilities for A Data Engineer

1. Create and maintain data pipelines which will involve data sourcing, extraction, transformation, profiling, storage, updating, indexing and maintenance of the advanced analytics data platform. 

2. Process raw, structured, and unstructured data at scale (including writing scripts, web scraping, calling APIs, writing SQL queries, etc.) into a form suitable for analysis then consolidate into a data platform for consumption by advanced analytics initiatives.

3. Ensuring that data storage and collection systems meet business requirements and acceptable industry standards.


## Services 
Below are data engineer's services.

1.Data ingestion: services and tooling around “scraping” databases, loading logs, fetching data from external stores or APIs, …

2.Metric computation: frameworks to compute and summarize engagement, growth or segmentation related metrics

3.Anomaly detection: automating data consumption to alert people anomalous events occur or when trends are changing significantly

4.Metadata management: tooling around allowing generation and consumption of metadata, making it easy to find information in and around the data warehouse.

5.Experimentation: A/B testing and experimentation frameworks is often a critical piece of company’s analytics with a significant data engineering component to it

6.Instrumentation: analytics starts with logging events and attributes related to those events, data engineers have vested interests in making sure that high quality data is captured upstream

7.Sessionization: pipelines that are specialized in understand series of actions in time, allowing analysts to understand user behaviors



## Skills Gained

- Strong programming skills; particularly in languages such as Python, Java, Scala, and SQL.

- Database systems, distributed computing systems, and big data technologies such as Hadoop, Spark, and Kafka. 

- Familiar with cloud platforms such as AWS, Google Cloud, and Azure.
## Tools covered
 
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/433dtl497lukb8jfhizt.PNG)

## All in All
 
Remember that data is the canvas, and you are the artist; the possibilities are endless, and the discoveries are waiting to be made.