Data Engineering for Beginners: A Step-by-Step Guide

What is Data Engineering?

Data engineering is the process of collecting, transforming, and storing data in a format that is accessible and usable for data analysis. It's the backbone of any data-centric organization, responsible for creating the infrastructure and pipelines that enable data scientists and analysts to derive insights from raw data. Data engineers bridge the gap between data sources and data consumers.

Why Data Engineering is important?

Data engineering helps make data more useful and accessible for consumers of data. To do so, Data engineer must source, transform and analyze data from each system. For example, data stored in a relational database is managed as tables, like a Microsoft Excel spreadsheet. Each table contains many rows, and all rows have the same columns. A given piece of information, such as a customer order, may be stored across dozens of tables.

Why should you opt for a Data Engineering career?

Data engineers must have specialized skills in creating software solutions around data. At the same time, it’s perhaps unrealistically expected that Data Engineers will be familiar with a breadth of tools and technologies – anywhere from 10 to 30 of them. And these tools are constantly changing.
So, supply for quality data engineers are extremely low at the moment and demand is astronomical. And as normal economics will tell you when supply can not match the demand the prices are bound to go up.
Data Engineer Salary in Kenya is average salary of $99,310. However the salary can range from $89,501 and $108,358.That is quite a good figure huh!

What are the skills needed to become a Data Engineer?

Just like Data Science or Full Stack Developer roles, Data Engineering role is also multi disciplinary. You need to learn a lot of dependent topics before becoming a great Data Engineer.
-However here are some of the skills you need to learn in order to break into the data engineering role;

1. Learn programming, Python, Scala, or Java.
   -Basic syntaxes, working with files, connecting to databases, building basic APIs, working with structured (database and tables)and unstructured(xml,json etc.) data.

2. Learn about Data Structures and Algorithms.

3. Learn SQL and the Core Data Base Management System, relational and non-relational.
   -Basic data extraction, joining tables, keys and constraints, window functions, aggregate functions etc. Data Definition and Data Modification queries.

4. Learn about the Hadoop ecosystem, spark, and other big data tools.
   
   -The Hadoop ecosystem and Apache Spark are fundamental in the big data realm. Hadoop includes components like HDFS and MapReduce for distributed data storage and processing, while Spark, prized for its speed and versatility, offers modules for SQL processing, streaming, machine learning, and graph analysis. Complementing these, tools like Kafka provide real-time data streaming, Flink excels in stream and batch processing, and databases like Cassandra and HBase cater to data storage needs. These tools collectively empower organizations to efficiently manage, process, and analyze extensive datasets in the age of big data.

5. Learn Cloud Computing and Services, AWS, Google GCP, and
   Azure
   AWS: Amazon Web Services offers a wide range of data engineering services.
   Azure: Microsoft's cloud platform includes various data engineering tools.
   GCP: Google Cloud Platform is known for its data analytics and storage services.

6. Learn System Design and Distributed System.

-Other Important Tools and Technology that you will need in your data engineering career includes.
1). Docker and Kubernetes
2). Power Bi, Matplotlib, Seaborn, kabana and other dashboarding tools
3). Kafka
4). Apache Airflow
5). Linux OS

Gain Practical Experience

Finally, the best way to learn data engineering is through hands-on experience. Work on real projects, whether they are personal projects or internships, to apply what you've learned and gain practical skills.

Data engineering is a dynamic and challenging field that is in high demand across various industries. As you follow these steps and gain experience, you'll be well on your way to becoming a proficient data engineer. Remember, data engineering is a journey, not a destination, so embrace the learning process and keep exploring the vast world of data.
In the world of data engineering, you're not just a data handler; you're a data architect. Your role is vital, and your journey is exciting. As you embark on this path, remember that with each line of code you write and every data pipeline you build, you're contributing to a smarter and more data-savvy world.

So, keep learning, keep coding, and keep shaping the future.Embrace it with enthusiasm, and let your passion for data drive you forward.