In today's data-driven world, the ability to collect, process, and analyze data is essential for businesses and individuals alike. Data engineering is the foundation that makes this possible, but it can seem like a complex and daunting field to newcomers.
Understanding what data engineering is and who a data engineer is will provide you with a comprehensive foundation for navigating the world of data.
What is Data Engineering and Who is a Data Engineer?
Data engineering is a crucial discipline that involves designing, building, and maintaining the infrastructure and systems necessary for data collection, storage, and processing. It serves as the backbone of data-driven decision-making, ensuring that data is reliable, accessible, and ready for analysis.
On the other hand, A Data Engineer is someone professional responsible for designing, constructing, installing, and maintaining the systems and infrastructure that enable the collection, storage, and processing of data. They work with various tools and technologies to make all these possible.
Data engineers are acknowledged as the most technically proficient experts in the realm of data science, acting as essential intermediaries between software and application developers and conventional data science positions.
Data Engineers are responsible for the first stage of the traditional data science workflow: the process of data collection and storage.
Step-By-Step Guide To Data Engineering For Beginners
Basics
Before diving into the world of data engineering, one should learn the basic concepts that builds the foundation of a data engineer. Concept such as database fundamentals, database management and programming languages.
Database fundamentals - Understanding what data is and how it's stored is crucial. Learn about databases, which are organized collections of structured data. Concepts to grasp include tables, rows, columns, and schemas.
Database Management - Learn about the various database management system(DBMS) such as MySQL, PostgreSQL, Oracle, MongoDB, and so on. Exploring these languages, their difference/similarities and when to use them is a crucial step for a data engineer.
Programming Languages - Coding is an important skill for data engineers. There are many languages data engineers use like Java, Scala, Ruby, etc. but the main language used is Python.
When deciding on a language to use as a beginner, start with Python and SQL. Python is versatile and commonly used for scripting and data manipulation, while SQL is essential for querying and manipulating databases.
Data Modelling and Data Warehousing
Data modeling is the process of creating a visual representation of data structures, including their relationships, constraints, and attributes. Data modelling involves techniques like:
Entity-Relationship Diagrams(ERD) - a visual representation that shows how different entities (objects or concepts) are related to each other.
Data Normalization - a process in which data is organized to minimize data redundancy and improve data integrity.
Denormalization - involves adding redundancy to the data model for the sake of optimizing data retrieval.
Data warehousing is a repository for storing and managing large volumes of data for reporting and analysis.
Familiarize yourself with data warehousing tools like Snowflake, Google BigQuery, Amazon RedShift, and so on.
ETL and ELT
ETL and ELT are two common data integration processes used in data engineering to move data from source systems to a data warehouse or data repository.
Extract, Transform, Load (ETL) is a traditional approach to data integration. It involves the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL is suitable for batch processing and is commonly used in scenarios where data needs to be cleansed and prepared before being made available for analysis.
Extract, Load, Transform (ELT) is a more modern approach to data integration. It is the process of extracting data from one or multiple sources and loading it into a target data warehouse. Instead of transforming the data before it's written, ELT takes advantage of the target system to do the data transformation.
Data Pipelines
After understanding your data, the next crucial step in data engineering is to design and implement data pipelines. Data pipelines are the backbone of your data integration and processing efforts, allowing you to collect, transform, and load data from various sources to a destination system, such as a data warehouse or data lake.
Conclusion
Learning the basics is essential, but it's only the first step in your quest for expertise. Continuous learning, practical experiences and collaboration are also important steps in becoming a data engineer.
So keep learning, stay determined and keep building your future of data.
Top comments (0)