Data Engineering 101: A Deep Dive

Learn all about the game-changing power of data engineering for making businesses more efficient, innovative, and profitable in this special guide.

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by Matias Emiliano Alvarez Duran


In this digital age, data engineering stands as a critical asset for driving efficiency, innovation and allowing businesses to strategically predict what's coming and make more accurate decisions.

That's why we've asked our experts to prepare this comprehensive guide, which will take you through all the fundamentals of data engineering, with clear concept explanations and practical use case examples.

Whether you're running a fintech startup or a global cybersecurity company, this piece will help get a sense of what data engineering solutions entail and their game-changing potential for boosting your business growth.

Table of contents

Data can be complicated, but not with the right sidekick in your corner.
Our squads not only sort all your tech challenges, but extract every last drop of value from your data to boost your business decisions. Get in touch and let's explore the ideal data solutions to supercharge your growth! 

What is Data Engineering? 

In simple terms, data engineering is the art of building and maintaining the systems that handle data flow in an organization, making it easier to collect, store, and analyze.

In practice, it addresses several common business needs, such as managing large volumes of diverse and complex data, ensuring data quality for dependable analytics, guaranteeing data security, and ensuring regulatory compliance. 

Ultimately, the magic of data engineering is that it works as a leverage for business growth. It not only makes processes more efficient and scalable, but also extracts the strategic insights hiding within your data, leading to better business decisions, and opening new routes for innovation. 

Data Engineering vs. Data Science vs. Data Analytics

Before diving deeper into the world of data engineering, it's important to differentiate it from other related fields, such as data science and data analytics. These are three disciplines with significant differences between them, but they often get mixed up because, in practice, they do sometimes overlap. Have a look at the diagram below to understand what each of them entails. 

Data EngineeringData ScienceData Analytics
Main FocusTo architect and maintain a robust data infrastructure for efficient data collection, management, and storage.To extract knowledge from data through statistical and predictive analysis, and machine learning algorithms.To translate data into understandable and actionable insights. 
Tools & TechnologiesDatabases (SQL, NoSQL), ETL tools, Big Data technologies (Hadoop, Spark).Programming languages (Python, R) and data analysis and manipulation libraries (Pandas, Matplotlib).Data visualization tools (Tableau, PowerBI), statistical software.
OutputsData that is easily accessible, secure, reliable, and in the right format for analysis. Algorithms and statistical models to solve complex problems or predict future trends.Reports and dashboards with strategic insights to support decision-making.

Understanding Data Classification & Sources

Not all data is created equal, and since we're covering all the fundamentals of data engineering, let's also look at the types of data sets usually found within a business. Why? Well, different data sets require different strategies for efficient data collection, management, and analysis.

TypeDefinitionCharacteristicsCommon Sources
Structured Data that adheres to a predefined data model.Highly organized.Sales transactions, customer databases, and financial records.
Unstructured DataData with no predefined format.Not organized in a pre-defined manner Videos, images, audio files, and free format texts such as emails.
Semi-Structured DataData that lacks a predefined data model, but contains metadata, such as tags and other markers, which allow it to be analyzed.Contains tags or other markers to separate semantic elements.JSON files, XML files, and others.
Big DataData sets that are so large, diverse and complex, that traditional data management systems are unable to handle them. Characterized by being large and complex in Volume, Velocity, and Variety.Social media activity, Internet of Things (IoT) devices telemetry, large-scale e-commerce systems.

The Data Engineering Ecosystem: Processes, Frameworks & Tools

Frameworks, tools, and processes are the three essential elements that make up the data engineering ecosystem. They're the blocks used by data engineers to build that killer infrastructure you need for an efficient data flow within any organization, as you'll see below. 

ProcessesStandardized methods and practices for managing and manipulating data throughout its lifecycle.- Data Ingestion/Extraction
FrameworksSoftware solutions designed to support the development of data pipelines and architectures. They provide the scaffolding and a set of tools for performing specific tasks within the data engineering process.- Apache Hadoop, for distributed storage and processing
ToolsThe specific software applications used to execute the tasks defined by the processes and within the frameworks. - Relational databases (SQL-like), such as Amazon RDS and PostgreSQL

Data Engineering for Businesses: Key Applications

In reality, there are 1001 ways to apply data engineering within any company. Not only to solve challenges, but also to enhance business performance, innovation, and revenue growth. With that in mind, we've prepared a selection of use cases, based on our 10+ years of building data engineering solutions, to give you a taste of what they can do for your company.

Data Infrastructure Design

A well-architected data warehouse can streamline analytics, helping identify consumer trends and operational inefficiencies. A retail company could leverage real-time data processing to make immediate operational adjustments, such as optimizing its inventory in response to sales trends. In a finance setting, real-time transaction processing systems can be built to ensure high availability and fault tolerance.

Data Integration

Data integration essentially gathers all your business's data from different sources into one easy-to-access format. This gives businesses an overview of their operations and customer interactions. By combining sales data from online stores with social media feedback, an e-commerce company can get a clear picture of customer satisfaction and product performance levels, and use it to tweak their marketing strategies. 

Data Processing and Analysis

Data processing and analysis is, in a nutshell, what turns your business' raw data into actionable insights. By analyzing historical claims data, an insurance company can identify trends, assess risk, and optimize their pricing. On the other hand, a manufacturing business could process and analyze sensor data from production equipment to predict maintenance needs, significantly reducing downtime and operational costs. 

Business Intelligence Integration

Combining data from various sources into a unified Business Intelligence (BI) platform will boost any company's analysis and reporting capabilities, and take your decision-making to the next level. In cybersecurity, integrating BI tools with Security Incident and Event Management (SIEM) systems can be a highly strategic move. This kind of integration supports the analysis of large security log volumes in real-time so that cybersecurity teams can quickly identify even the most complex attack patterns, and swiftly implement protective measures.

Ongoing Maintenance and Optimization

Ongoing maintenance and optimization is the way to ensure that data keeps flowing as it should from source to destination, to efficiently support your decision-making processes.

If you run a Fintech business, for example, continuously monitoring and optimizing data pipelines helps to guarantee swift transaction and financial data processing, which is essential for real-time trading platforms and fraud detection systems. 

Artificial Intelligence & Machine Learning in Data Engineering

Artificial Intelligence (AI) and Machine Learning (ML) are essential assets in our data engineer's tool belts since the solutions we deliver for our clients wouldn't be so outstanding without these game-changing technologies. So let's find out a bit more about how these two technologies work and how they can be applied in data engineering.  

AspectArtificial IntelligenceMachine Learning
DefinitionMachines or software with the ability to perform tasks requiring human-like intelligence, including reasoning, learning, and adapting to environmental changes.ML is a branch of AI, focused on the development of algorithms that enable computers to learn and make decisions from data, without being explicitly programmed for each task.
Characteristics- Capable of a broad range of cognitive functions from simple automation to complex decision-making.- Involves automatic learning and improvement from data without explicit programming for specific tasks. 
Tools & TechnologiesIBM Watson,  Google AI Platform, Microsoft Azure AI and UiPath.TensorFlow, PyTorch, scikit-learn, and Keras.

Use Case Examples

Fraud Detection and Prevention

Worried about credit card fraud, money laundering, or other malicious activities? AI-based systems can be developed to monitor and analyze transaction data in real-time to identify potentially fraudulent activity. 

Recommendation Systems

Machine learning algorithms can be used in recommendation systems for fintech applications, for example, suggesting personalized financial products or investment opportunities based on the customer's transaction history, risk profile, and investment goals. This also works for creating a cart recommendation feature based on users' profiles, as we have done for several e-commerce clients.

Dynamic Pricing

Certain ML models are able to analyze market conditions, customer demographics, and risk factors in real-time, dynamically adjusting prices to maximize business profitability and competitiveness. 

Regulatory Compliance Monitoring

Using AI, it's possible to monitor and ensure compliance with changing regulations and standards within an industry, making it a great asset for highly regulated sectors, such as healthcare, finance, and insurance. 

Why Data Engineering is Critical for Modern Businesses

Solid data engineering is an essential foundation for efficiency and regulatory compliance within industries like cybersecurity, Fintech, and insurtech. But, in an era where data is generated and transferred in unprecedented volumes between a myriad of systems, it's also a strategic necessity for players from all sectors. 

Far beyond optimizing processes and preventing data incidents, data engineering allows companies to unlock the value within their data to get into their customer's minds, boost innovation, and gain a massive competitive advantage in a digitally-driven marketplace.

And if you're wondering about how exactly all of this works in practice, don't worry. You can check out what kind of magic we can deliver for your business on our data engineering services page, or explore some real-world examples from the NaNLABS squad, such as:

Data can be complicated, but not with the right sidekick in your corner.

Our squads not only sort all your tech challenges, but extract every last drop of value from your data to boost your business decisions.

Get in touch and let's explore the ideal data solutions to supercharge your growth!

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