Data Engineering 101: Introduction to Data Engineering
Outlines
What is Data Engineering?
Data Engineering Life Cycle
A case study of the role of a Data Engineer
Who is a good fit for the role of Data Engineering
Relevant value added by a Data Engineer.
What is Data Engineering?
In a layman’s conversion of the definition for data engineering, it will sound as the engineer is in charge of data.
Data engineering is a science that pays more attention to designing, collecting, processing, analyzing, and building data.
Most time, the data are LARGE and SCALABLE.
The process of engineering the data helps to maintain scale data for structured and unstructured data.
The data engineering Life Cycle
- Data collection
- Data sourcing
- Data storing
- Data Analysis
- Data Modelling
Most time, it evolves all the possible actions you can perform on your data from start to end.
The process is to make the best sense of data.
A case study of what a data engineer does?
with reference to www.interviewquery.com,
“You’re tasked with building a data pipeline for POS data from a store like Walmart. This data will be used by data scientists. How would you do it?”
With a case study question, the first step is to ask clarifying questions. You should gather as much information as you need. Then, you would propose your solution.
A few tips for a data engineer case study include:
- Problem-Solving Approach — When you’re presented with a problem, interviewers want to know the steps you take to solve it.
- Thoroughness — Before you jump into an answer, get clarification. You should understand exactly what they’re looking for. Then, you can jump into an answer.
- Ability to Communicate — Think out loud and walk the interviewer through the process. Say exactly why you would make a particular choice.
- Design Patterns — With architecture problems, you should have a strong grasp of design patterns, as well as the technologies and products that can be used to solve the problem.
- Forward Thinking — Every data engineering solution includes trade-offs. Interviewers want to see that you can assess a solution in terms of pros and cons, as well as potential weaknesses of a solution.
Ultimately, these questions focus on a range of subjects including database design, data warehousing, ETL pipelines, and data modeling.
Who is fit for a data engineering role?
A data engineer must have a programming background. The basic abilities are SQL, Python, R, and ETL approaches and practices. They additionally need to have an interest in the information, and in tracking down designs in information. Enormous information projects are more complicated than little information. Subsequently, you want to construct complex frameworks, and information pipelines to turn into a decent information engineer.
Relevant value added by a Data Engineer
Data literacy and data quality
People or teams working with data including data analysts, data engineers, and data scientists are always looking for effective ways of preparing and transforming data, generating efficient data models at any scale, and creating a self-service experience for themselves and their counterparts on the business side. Nonetheless, they are frequently tested with getting a handle on untidy information that prompts wasteful frameworks and unanswered inquiries in regards to business patterns.
Team encountering this aggravation alludes to it as information ignorance which can prompt undesirable business repercussions. The objective is to accomplish information proficiency by giving people and associations the capacity to comprehend information alongside the sources to acquire bits of knowledge.