Data Engineering 101

Data engineering is a very distinct role in the field of IT and is at the top of the ranks in the data roles stack since it lays the foundation for the rest of the professionals like; business analysts, data analysts and data scientists. Precisely data engineering is the process of preparing data for data analysis and general consumption. This done through complex processes that ensure that the needed datasets are collected, transported and stored in the right place and can be accessed by consumers in the right format. Therefore, a data engineer is responsible for deciding the data to be collected, how to collect it, how to transfer and store it securely in the right format.

Tools used in data engineering**

In the recent past big data has continued to grow immensely and it is projected to grow even further, recent projections show that by the year 2025 the world will be producing around 463 exabytes of data those are huge amounts of data, literally 18-digit amounts of data! This amount of data needs special skills and tools to be handled correctly, the commonly used tools and techniques include:

1. Programing using languages such as Python, Java, R, Scala etc. these languages are used to come up with tools such the data pipelines and for writing automation scripts.
2. Cloud computing technologies help in storing the data in large amounts as they can handle growing data. Examples of the providers include Microsoft Azure, Amazon web services and Google cloud platform.
3. Database management systems that include SQL and NOSQL systems like MySQL, mongo DB etc. these systems are used to store and manipulate data.
4. Machine learning – though machine learning is mostly used by data scientists, data engineers need the skills so that they understand the needs of the data scientists so that they can serve them better.

Path to becoming a data engineer

*A university degree *
A university degree in computer science, mathematics and statistics, physics or any other related field is important but not mandatory employers look at the skill set and the ability to solve problems.

*Certifications *
There are a number of certifications that test specific skills and give certificates, most of these certificates are taught by industry experts and they are an added advantage in the job market. Some examples of platforms offering certifications include:

1. Udacity
2. Coursera has a number of them
3. Edx
4. Udemy
5. Google
6. Microsoft
7. Aws

*Bootcamps and communities *
These add value since they are made of people practicing data engineering and other related skills.

After acquiring the necessary skills what’s next? One needs to brand as data engineer in order to secure a job this can be done by creating an online portfolio and include projects that one has undertaken, host them on personal websites or on GitHub.