Big Data Engineering: It’s No Longer Just About “Big”

About a decade ago, big data was the big deal. Only big enterprises could handle it: they had the money, the tech, and the teams to make sense of it. With the rise of scalable cloud solutions, however, even smaller businesses got the opportunity to process massive amounts of data. What once seemed cutting-edge became a routine part of doing business.

And once AI entered the scene, data stopped being something to analyze. It became the fuel that powered AI models. And if AI starts spitting out nonsense, it’s not the model’s fault. Bad training data is usually behind the chaos. Having big data engineering as our core expertise, we at Flyaps know that if the data’s a mess, the AI will be too. 

We help businesses build data-driven solutions across industries like telecom, retail, media, and more. Behind these solutions are skilled data engineers who make your data ready for AI. Below, we’ll focus on what modern big data engineering is and why data experts are crucial for AI, ML, and real-time data apps.

How “big” is big data today

Big data is riding high in an age where nearly every software solution comes with AI-powered features. But this “big” no longer means what it used to, like when it referred to datasets so massive that conventional tools crumbled under their weight. 

With cloud computing, advanced storage solutions, and distributed systems storing and processing enormous amounts of data is not a problem anymore. Today, the real struggle isn’t just size. Modern data challenges include real-time data processing, data integration, data governance, finding the right balance between performance, storage, and cost, and more. To efficiently address these issues, companies should shift their focus to stream processing, data mesh architecture, and other data-driven tech. 

Here are the technologies that make data processing efficient and effective:  

Cost-effective cloud platforms

As data volumes skyrocket, it becomes difficult to juggle performance, storage, and cost. No one argues that cloud services like AWS, Google Cloud, and Azure provide scalable cloud solutions, but storing and processing petabytes of data isn’t cheap. Yet, companies can choose efficient storage formats, configure the infrastructure to handle data at scale, and use technologies like intelligent caching and serverless computing. Simply put, they need to optimize their data architectures to maximize efficiency and minimize expenses.

Stream data processing

Today’s businesses can’t afford to wait for data insights. They need them by the hand. Processing the flood of real-time information without delays requires powerful streaming architectures. That’s why businesses shift away from traditional batch processing, which can lead to delays, and implement streaming architectures. For a better understanding, here is the main difference between batch data processing and stream data processing.

Such platforms as Apache Kafka and Spark Streaming help build real-time data pipelines that can handle massive streams of data continuously. Kafka is widely used for handling high-throughput, low-latency messaging, while Spark Streaming processes data in micro-batches to analyze it in near-real-time.

To give you an idea, streaming architectures are used for scenarios when real-time insights are needed to act fast. For example, in banking, streaming can be used to detect fraud in real time. If a suspicious transaction occurs, the system can immediately flag it, send alerts, and even freeze accounts if necessary, all within seconds.

Data mesh architecture

Organizations grow, and managing data across multiple teams, departments, or regions becomes complex. Traditional data architectures, which rely on centralized data lakes or warehouses, lack the flexibility needed to support real-time data processing. So businesses choose data mesh architecture. It’s a decentralized approach to data management that treats data as a product and distributes ownership across different teams.

With data mesh, each team, like sales, inventory, or marketing, takes responsibility for managing its data. Instead of relying on a central team to handle everything, data mesh promotes a more collaborative, domain-driven approach to data management.

Edge computing

Edge computing is a way of processing data closer to where it is generated. In simpler terms, it allows devices like smartphones, wearables, or smart sensors to analyze data locally. It might be hard to grasp the idea at once, so let’s look at the example. 

Imagine you have a smart security camera outside your house. Without edge computing, the camera would send all its video footage to the cloud for processing, which could cause delays and high bandwidth costs. But it has a built-in AI processor that detects motion locally. It doesn’t stream 24/7 and only sends alerts or relevant clips when necessary. Here is how it works.

When shifting to edge computing, businesses get faster insights, lower cloud costs, and more efficient real-time operations.

AI and ML integration 

Storing data is one thing, while making it useful for AI and automation is another. Organizations are flooded with information from IoT devices, cloud applications, and internal systems, so manual management isn’t even an option. AI and ML step in to automate data integration and ensure consistency across disparate sources. It’s particularly important for industries like finance, healthcare, and logistics, where instant insights can mean the difference between success and costly delays.

Beyond integration, AI and ML play a critical role in balancing performance, storage, and cost. Intelligent algorithms dynamically allocate resources, which results in optimized cloud expenses. In turn, predictive analytics powered by machine learning helps businesses anticipate trends, detect anomalies, and make proactive decisions.

Now that you have a clear picture of what big data engineering looks like today, you won’t debate that data requires professional engineers to make sense of it. But what exactly does being a “professional” mean in this context? Let’s figure it out below.  

What it takes to be a good data engineer

Big data engineers are crucial for any company dealing with data. And these days, that’s nearly every business. Here’s what expert data engineers should be able to do.

• Build a solid data architecture

A big data software engineer designs the framework that holds the data system together so it’s strong, flexible, and can scale as the business grows.

• Develop data pipeline

Data experts build efficient pipelines to integrate data from various sources. They handle the entire ETL process to ensure the data is cleansed, organized, and ready to be used for analysis or decision-making.

• Ensure data quality and security

A big part of a data engineer’s job is centered around data accuracy and security. They set up processes to catch errors, inconsistencies, or missing values. They also implement security measures like encryption and access controls to protect private information and prevent unauthorized access.

• Collaborate with stakeholders

Data engineers work closely with analysts, data scientists, and business leaders to turn their needs into data systems that work for everyone. 

• Manage databases

A big data cloud engineer is also responsible for databases. They design databases, set up storage solutions, and manage the overall structure of the database.

• Optimize the performance of data systems

Data engineers should constantly fine-tune data systems. They monitor performance, troubleshoot bottlenecks, and optimize queries and storage. The goal here is to ensure that data is processed and accessed without unnecessary delays.

These are the basic responsibilities of big data engineers, but when you add AI into the mix, engineering big data becomes a bit trickier. Data engineers working in AI need to focus on preparing the data for machine learning models. 

No data engineers, no AI

Big data engineering is a broad term by itself that covers everything related to collecting, processing, storing, and managing huge amounts of data. But when it comes to data processing for AI, it’s a different level. Here, we’re not only organizing data. We’re getting it ready to train models that can spot patterns and make predictions. If the data isn’t for AI, it’s usually structured for reporting, analytics, or making storage more efficient, not for predictive modeling. 

That’s why AI data engineering is more complex. It requires transformation, feature engineering, real-time processing, and more. Let’s imagine for a minute what happens without data engineers in AI.

❌ Garbage in, garbage out

AI models rely on clean, structured, and relevant data. Without engineers to prepare it, models would learn from noise and misinformation.

❌ Data chaos

No pipelines, no centralized data sources, just a tangled mess of files, missing records, and inconsistencies. Good luck training anything useful in that mess.

❌ Scalability nightmare

AI needs massive datasets. Without proper infrastructure, everything would be slow, expensive to maintain, and prone to crashing.

❌ Security and compliance issues

Data leaks, regulatory fines, and ethical disasters would be far more common without proper data governance in place.

❌ AI that can’t learn in real time

No efficient data pipelines means no real-time insights. AI models would be stuck analyzing outdated or incomplete information.

And once again, we reach the conclusion that data engineers are absolutely indispensable for AI data preparation. Without efficient data preprocessing, a solid data architecture, robust governance, and real-time data pipelines, an AI project simply can’t succeed. So if you plan to build an AI-driven app or platform, it’s time to think about data engineers.

Partner with professional data experts like Flyaps

Our team can help you design robust systems, ensure data quality, and build the infrastructure that lets you use data for insights, predictions, and smart decisions. And it’s not just talk. We’ve delivered numerous successful data-driven projects, including our collaboration with Airbyte, the leading solution for building ELT data pipelines. 

The client needed experienced Python engineers to build a platform that simplifies the creation of connectors and streamlines data movement from various sources. To meet this goal, we focused on making the platform as user-friendly as possible, so that new connectors could be developed in minutes.

Through this collaboration, our team helped Airbyte:

• Build a leading ELT platform 

Airbyte became one of the top ELT platforms with over 300 pre-built connectors, doubling its connector catalog annually.

• Create an active data community

The platform attracted over 10,000 active community members, contributing new connectors and improvements.

• Achieve unicorn status

In just two years, Airbyte received a $1.5 billion valuation and secured $181 million in funding.

Still got questions on engineering big data?

What is big data engineering?

Big Data Engineering refers to the process of designing and building systems that can handle, store, and process large and complex datasets. It focuses on creating the architecture and infrastructure necessary to support data operations that traditional systems can’t manage efficiently. It includes working with distributed systems, databases, big data technologies and cloud platforms to ensure scalable and reliable data processing.

What skills are required to become a big data software engineer?

To become a professional data engineer, you’ll need strong skills in software engineering and data science, with a deep understanding of distributed computing. Here are some key skills and tools that are essential for the role:

• Big data technologies

Proficiency in frameworks like Hadoop, Apache Spark, and Flink for data processing and NoSQL databases (for example, Cassandra, MongoDB) for scalable storage solutions.

• Cloud platforms

Familiarity with cloud services like AWS, Google Cloud, or Microsoft Azure, which offer tools for big data storage, processing, and data analytics.

• Data pipelines and ETL

Expertise in building and maintaining ETL (Extract, Transform, Load) pipelines to ensure smooth data flow from various sources into analytics systems.

• Programming languages

Strong coding skills in languages like Java, Scala, Python, or SQL to build efficient systems for data manipulation, processing, and querying.

• Data modeling

Experience in creating data models and schemas to structure big data for analysis and ensure consistency and integrity.

In addition to technical skills, a big data engineer should have the soft skills and a strong understanding of computer science specifics to work collaboratively with data scientists and data analysts.