If you are here it means that you want to learn about the world of Data Engineering.
At LoopStudio we are passionate about data, so we are going to talk about a role that is pivotal yet often shrouded in ambiguity in the data-driven landscape of today’s technology sector.
In the following article, we delve deep into what Data Engineering truly entails.
Like with any role in Data, can be hard to define with precision what Data Engineering is. The roles on this field tends to touch each other and have grey areas all the time, so when we read any definition we should be aware that probably what a Data Engineer is, and what a Data Engineer should do in an organization, are probably two different things.
Joe Reis and Matt Housley defined Data Engineering on its book “Fundamentals of Data Engineering” and it is a good start.
“Data engineering is the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engineering is the intersection of security, data management, DataOps, data architecture, orchestration and software engineering. A data engineer manages the data engineering lifecycle, beginning with getting data from source systems and ending with derving data for use cases, such as analysis or machine learning.”
We could add that Data Engineering is an essential field in the world of big data and analytics, focusing on the practical applications of data collection, data storage, and data management. It involves developing and maintaining the architecture and systems that allow for the efficient processing and analysis of large data sets.
Data engineers work to ensure that data flows smoothly from source to destination, making it accessible, understandable, and actionable for businesses and decision-makers. This discipline is vital for organizations looking to leverage data for strategic insights and operational efficiency.
What do Data Engineers do?
Data Engineers are the architects of data ecosystems in organizations. They design, construct, install, test, and maintain highly scalable data management systems. This involves integrating various software and hardware, and ensuring the secure and ethical handling of data.
Their work includes building data pipelines to collect, process, and distribute data, and they often collaborate with data scientists and analysts to provide them with structured data that is ready for analysis. Data engineers are also responsible for optimizing data flow and collection to improve data accuracy and efficiency.
We must not confuse Data Engineers with Data Architect though, as these last are the responsables to think about the data flow at organization level, with a vision at high level and not looking into the individuals projects itself. It is the responsibility of the Data Engineer to build the infrastructure following the guidelines of the Data Architects.
The Data Engineering Lifecycle
The Data Engineering Lifecycle is a concept developed by Reis and Housley on their book “Fundamentals of Data Engineering” and illustrates perfectly the logic of a Data Engineering project. The Data Engineering Lifecycle is a series of steps followed to manage and utilize data effectively.
It begins with data acquisition, where data is sourced from various inputs. This is followed by data storage, where it is securely and efficiently stored. Data processing and cleaning are next, ensuring data quality and usability. This leads to data aggregation and reporting, where data is compiled and made ready for analysis. Finally, data archiving and purging come into play, managing the lifecycle of data as it becomes less relevant over time.
How to become a Data Engineer?
Becoming a Data Engineer typically involves a mix of formal education and practical experience. Most professionals in this field have a degree in computer science, information technology, or a related field, but this is certainly not a limitation, anyone with passion for data and with some experience with databases and SQL can have a good starting point to its journey.
Foundational knowledge in programming languages like Python and SQL as well as an understanding of database management systems, is crucial. The life of a Data Engineer its on the cloud so the knowledge of Linux shell scripting and Docker is a need as well.
Gaining experience through internships, projects, bootcamps, and hands-on practice with data engineering tools and platforms is also key. Continual learning and staying updated with the latest trends in big data technologies is an essential part of a data engineer’s journey. Finally a data engineer will eventually need to be familiar with some of the top cloud providers such as AWS, Azure or GCP.
The Data Engineer career path
The Data Engineer career path can vary, but it often starts with a role as a junior or entry-level data engineer. From there, one can progress to a senior data engineer position, where responsibilities involve leading projects and designing complex data systems.
Some may choose to specialize in areas like big data, cloud computing, or data architecture. Eventually, a data engineer can advance to roles like lead data engineer, data engineering manager, or data architect, where they oversee entire data engineering departments and strategies.
How to make a career in Data Engineering
Making a career in Data Engineering involves a combination of education, skill development, and practical experience. Prospective data engineers should focus on mastering key programming languages, data modeling, and database management.
Building a portfolio of projects that demonstrate your skills is crucial. Networking within the industry, attending workshops and conferences, and possibly obtaining certifications can also be beneficial. Gaining experience through internships or entry-level positions is a critical step in establishing a career in this field.
Where I can learn Data Engineering?
Data Engineering can be learned through various channels:
- Many universities offer degrees in computer science or data science with courses focused on data engineering.
- Online platforms like Coursera, Udacity, and edX provide specialized courses and certifications.
- Bootcamps are another effective way to learn practical skills quickly.
- Additionally, many free resources are available online, including tutorials, forums, and open-source projects, which can be invaluable for hands-on learning and keeping up with the latest trends in the field.
Data Science vs Data Engineering
Its a common confusion to talk about Data Science and Data Engineering as they were the same thing. While data science and data engineering are closely related, they focus on different aspects of data management and analysis.
Data science is about extracting knowledge and insights from data, involving statistics, machine learning, and data visualization. In contrast, data engineering focuses on the practical aspects of data collection, storage, and data pipeline construction.
Data engineers create the infrastructure and tools that data scientists use to perform their analyses. Both roles are complementary, with data engineering laying the groundwork for effective data science.
Another approach to this comparison can be made with the logic of the image below, developed by Zach Wilson, in which we can see that a Data Engineer is 75% builder and 25% investigator, closer to an software developer, while the Data Scientist is closer to the data analyst, with a 25% of builder and 75% of investigator as a role.
In conclusion, Data Engineering is a vital and evolving field in the era of big data.
It plays a crucial role in enabling organizations to efficiently process and leverage large volumes of data for strategic decision-making.
As the data landscape continues to grow and diversify, the demand for skilled data engineers is set to increase, offering a wealth of opportunities for those interested in pursuing a career in this dynamic and impactful field.
Q: Do I need a degree in computer science to become a Data Engineer?
A: While a degree in computer science or a related field is common, it’s not the only path. Relevant skills can also be acquired through self-study, bootcamps, and online courses.
Q: What programming languages should I learn for Data Engineering?
A: Python, SQL, and Java are fundamental to most data engineering roles, but the requirements can vary depending on the specific job and industry.