What is Data Engineering? A Beginner's Guide

Since everything is powered by data, the gathering, processing, and management of massive amounts of information efficiently have never been more strategic. Every organization, from tech start-ups to global enterprises, depends on data for better decision-making, accelerated innovation, and gaining a competitive advantage. This very transformation is enabled by data engineering: the discipline that builds the infrastructure and systems to convert raw, scattered data into meaningful, actionable insights.

This blog acts as a beginner’s guide that delves into the realm of data engineering, exploring its definition, what a data engineer does, ETL, ELT, and data transformation, SQL for data engineers, and ultimately, how to become one. If you're curious about what a data engineer does, eager to know what technologies they use, or considering starting a career in this field that changes rapidly, this guide will provide you with all the clarity necessary to orient yourself properly along this path.

What Is Data Engineering?

Therefore, data engineering can be defined as an underlying discipline that organizations use to gather, process, and maintain large volumes of data efficiently and reliably. In essence, data engineering is all about setting up the infrastructure and the systems that change raw data, often in a mess, from diverse sources such as databases, applications, sensors, etc., to clean, accessible, and actionable information. In fact, this conversion is very much required for any organization or team that wishes to make data-driven decisions, since it assures the availability and trustworthiness of data for analysis, reporting, and machine learning. It is important to mention that even though data engineering and data science are quite related, they differ in objectives: data engineering is leaning on building and maintaining the infrastructure that supports the processing of data, while data science concerns the analysis of data to extract insights.

What Does a Data Engineer Do?

The core responsibilities of a data engineer include: 

• Designing and Building Data Pipelines: Data engineers create automated workflows known as data pipelines, which extract data from various sources, transform it into a usable format, and load it into databases, data warehouses, or an analytics platform. They provide means to move data smoothly or with great availability, from daily reporting to analytics.
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• Integrating Data from Multiple Sources: Organizations are getting data from a plethora of places, for instance, websites, mobile apps, CRM systems, and third-party APIs. Data engineers link these sources to blend and join relevant data for analysis. 
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• Ensuring Data Quality and Consistency: Data engineers put checks on data to ensure cleaning, validating, and standardizing. This includes removing duplicates, correcting errors, and checking for compatibility and accuracy from cross-sourced data.
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• Managing and Optimizing Data Storage: Designing and maintaining the database and data warehouse is on data engineers. They will choose the best solution for storage needs in the organization. They will also optimize it concerning performance, so it is also ready for growth in data volumes. 
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• Collaborating with Other Teams: Data engineers work in close coordination with data scientists, analysts, and business stakeholders. They provide data that is ready and available for analysis. In addition, they help translate business requirements into technical solutions.
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• Automating and Monitoring Data Workflows: One of the key roles of a data engineer is automation. Using tools like TROCCO, Apache Airflow, and cloud tools, data engineers schedule, monitor, and debug data pipelines, thus preventing wrong data entry and ensuring the currency of data.

ETL, ELT, and Data Transformation

ETL: Extract, Transform, Load

• Extract: Pulling the data from various sources- databases, APIs, or files.
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• Transform: In this phase, the data is cleaned, standardized, and enriched before reaching its destination. Transformations ensure accurate and consistent data suitable for analysis.
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• Load: The actual loading of the transformed data into a designated system like a data warehouse or analytics platform, is done in this step.

ETL, being the conventional approach, works on the principle of ensuring data quality before it enters the hands of an analytic environment. In cases where organizations impose stringent governance on dataflow or the application processing the data holds limited processing power, the ETL model plays a pivotal role.

‍ELT: Extract, Load, Transform

• Extract: Data is pulled into the source systems just as in ETL.
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• Load: The raw data is loaded directly into the target system (typically a cloud data warehouse).
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• Transform: The data is transformed within the destination system, harnessing the power of its processing capabilities.

With the advent of cloud data warehouses and services that can execute data transformation jobs on a large scale efficiently, ELT is getting more and more traction. This method allows higher flexibility and faster data ingestion, as the transformation can be done on demand and modified easily.

Data Transformation Tools

Data engineers are entrusted with the responsibility of automating and streamlining the processes mentioned before by using specialized data transformation tools. These tools provide the following assistance:

• Data cleansing and standardization: Identifying and correcting duplicates and inconsistencies.
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• Data enrichment: Eventual addition of contextual information or other data to make data more useful for analysis.
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• Automation of workflows: Schedule and manage all data transformation jobs, saving time and labor.

Take a closer look into the same by trying TROCCO's Data Transformation Tool, that lets you seamlessly automate, integrate and transform data with the support of a no-code interface. 

SQL for Data Engineers

The SQL (Structured Query Language) is the foundation of data engineering, and it is the universal language of interactions with databases and warehouses. For data engineers, learning SQL skillfully is not only advantageous, but also very necessary. It helps engineers do efficient extraction, manipulation, and analysis of data, thus becoming an important tool for building and maintaining a robust data pipeline and supporting modern data stacks.

Why SQL Matters for Data Engineers

• Data Extraction and Manipulation: With SQL, data engineers can fetch the required data from databases, filter the results, join the tables, and aggregate them. This is fundamental in preparing data for analysis, reporting, and running machine learning tasks.
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• Data Transformation: Most data transformation jobs like cleanup, standardization, and enrichment can be achieved straight away with SQL. SQL is the core technology which simplifies and automates complex transformation workflows.
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• Integration with Data Pipelines: SQL works well with data pipelines in staging, transforming, and validating data before they're actually put in the analytics platform. Hence, at this stage, data have to be precise and ready for consumption by data scientists and analysts.

How to Become a Data Engineer

The key steps to succeed in this field are the following:

• Master programming languages such as Python and SQL, familiarize yourself with data structures and algorithms, and understand database management in order to build a strong technical foundation. 

• Learn about data pipelines and data engineering tools by exploring ETL/ELT platforms, such as TROCCO, Apache Airflow, and by working with cloud platforms such as AWS, Google Cloud, or Azure. 
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• Get practical experience by doing personal projects; for example, building data pipelines and databases, contributing to open source projects, or looking for internship or entry-level positions in data engineering. 
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• To be informed of emerging trends in the industry, read the blogs and news resources from the industry, engage in online communities, and participate in webinars or conferences. 
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• Network and seek mentorship by attending meetups and conferences, getting to know experienced data engineers, and finding mentors who can provide guidance and advice in career development.

FAQs

• What is data engineering in simple words?
  
  Data engineering is building and designing systems that will gather, store, and process great quantities of data so as to conduct analysis and make decisions. Data engineers ensure that data is trustworthy, accessible, and ready for any business insights.
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• What does a data engineer do?
  
  A data engineer builds and maintains systems that collect, process, and store data. They design data pipelines, ensure data quality, and prepare datasets for analysis by data scientists and analysts.
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• Q: Is data engineering just ETL?
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  No, data engineering goes beyond ETL. While ETL is a key component, data engineers also manage data architecture, performance optimization, real-time processing, and data infrastructure at scale.
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• Q: What is data engineering with an example?
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  Data engineering is the practice of building data pipelines and infrastructure. For example, a data engineer might create a pipeline that extracts user data from an app, cleans it, and loads it into a cloud warehouse for analytics.
  
  

• What are the basics of data engineering?
  
  Basics include an understanding of data pipelines, knowledge of different databases (like SQL and NoSQL), a knowledge of data modeling, and skills in ETL (Extract, Transform, Load) processes. The data engineers also ensures and looks into data quality while building scalable solutions from data storage to analysis.
  
  
• How do I start learning data engineering?
  
  Start with the core concepts around SQL and one programming language like Python. Take some online courses on the likes of Coursera or Udemy, read some documentation and books, and see if you can get a mentor or join the data engineering community for extra help and support.
  
  
• Can I learn data engineering in 3 months?
  
  It is challenging to become a fully fledged data engineer in three months, but if you are willing to put in effort with a meticulous plan, in three months you will have gone quite far. Concentrate on programming, SQL, databases, ETL techniques, and cloud platforms to build primary skills with some real-world projects.
  
  
• What is ETL in data engineering?
  
  ETL means Extract, Transform, Load. It is one of the most important processes whereby data is collected from various sources, subjected to transformation (cleaning and standardizing), and is loaded into a target system, namely a data warehouse, for analytical and reporting purposes.
  
  
• Who is called data engineer?
  
  A data engineer is someone who designs, builds, and manages systems and infrastructure for collecting, storing, and processing data at any type of scale. Working in coordination with data scientists and analysts, they take extra steps to ensure accuracy, reliability, and readiness of data for analytics and machine learning purposes.

Conclusion

This blog delved into the fundamentals of data engineering, unfolding the definition of data engineering, what does a data engineer do, primary processes involved, SQL for data engineers, and ultimately how to become a data engineer. No matter where your journey takes you, data engineering offers a rewarding path filled with challenges, growth, and the satisfaction of helping organizations harness the true power of their data.

Take control of your future in the data-driven world! Start your free trial with TROCCO today to transform raw data into actionable insights that drive you and your company’s growth.