Data Engineering: What It Is and How It Drives Business Success

November 15, 2024

Many businesses struggle to turn their growing piles of data into something usable. In fact, research shows that up to 73% of enterprise data goes unanalyzed, leaving valuable information unused. Data engineering solves this problem by creating systems that organize raw data, making it easy to access and use. Building smart data pipelines can help drive long-term business success by turning data into practical insights.

What Is Data Engineering?

Data engineering is the practice of creating systems to collect, store, organize, and process data so businesses can use it effectively. It focuses on building pipelines that take raw data and transform it into a usable form. That allows for better insights and decision-making. Data engineers use tools like databases and cloud services to manage large amounts of data. By setting up this infrastructure, they help businesses make informed decisions and uncover new opportunities.

How Does Data Engineering Work?

Data engineering sets up systems to collect, organize, and prepare data so businesses can use it easily. At the core of this is the data pipeline, which moves data from its source to storage, is often a data warehouse or data lakehouse. For example, a small business might gather all customer data securely in one place, helping them understand buying trends over time.

To make this data useful, data engineers often use ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes. In ETL, for instance, a business extracts sales data, cleans and organizes it, and loads it into its data warehouse, preparing it for analysis. ELT is similar but loads raw data first, often to a cloud-based system, where it’s transformed as needed.

To keep information up to date, change data capture (CDC) tracks updates so stored data reflects the latest details, like current inventory levels. Data modeling structures the data to fit business needs, making it easier to find answers quickly, such as which products are top sellers on weekends.

Data observability tools monitor the data pipeline for reliability to keep everything running smoothly. And for businesses with large or fast-changing datasets, distributed computing platforms like Apache Spark process data quickly across multiple servers. Considering poor data management costs companies up to 12% of their revenue, data engineering has quickly become a valuable role.

Data Engineering vs. Data Science vs. Data Analysis: Key Differences

Understanding the roles of data engineering, data science, and data analysis helps show how each field supports a business in using data. Each one has a unique focus: building systems, finding trends, or answering questions.

Data Engineering: Building the Foundation

Data engineering is about creating and managing systems that collect, store, and organize data. They build the data infrastructure to make data more accessible. They set up ETL pipelines to move data from different sources into storage, like data warehouses, and keep everything connected (data integration). Data engineers also watch pipeline observability and manage data storage models to keep data reliable.

Data Science: Finding Insights and Predictions

A data scientist uses digital information organized by engineers to find insights and make predictions. They create algorithms and use statistical models to spot patterns in the data. Data scientists often work with machine learning to build models that predict future outcomes. This role uses programming and math skills, along with tools like Python or R, to analyze large datasets. They explore data to help businesses see patterns and make predictions.

Data Analysis: Turning Data into Answers

Data analysts use organized data to answer business questions. They use tools to create reports and visuals highlighting important insights. While they don’t need advanced programming skills, data analysts must understand data quality and integration to ensure accuracy. They often work with business teams to turn data into information that supports decisions.

In short, data engineers build systems that make data accessible, data scientists analyze data to create insights, and data analysts interpret this data to answer questions. Together, these roles help businesses use data effectively, with data engineering providing the foundation for analysis and insights.

Common Data Engineering Tools and Technology

Data engineering relies on a range of tools and technologies to store, organize, and process data efficiently. These help businesses manage data, making it accessible and ready for analysis. Here are some of the main tools and technologies used in data engineering:

Cloud Computing Platforms: Services like Amazon S3, Google Cloud, and Microsoft Azure Data Lake are commonly used. They offer scalable, flexible storage, making handling large volumes of data easier.
Relational Database Management Systems (RDBMS): Traditional databases, such as MySQL and PostgreSQL, organize data in structured tables. They’re ideal for applications needing consistent, structured data.
NoSQL Databases: NoSQL databases like MongoDB and Apache Cassandraare designed for unstructured data. They offer flexibility, making them great for modern applications with changing data formats.
Data Warehouses: Data warehouses, like Amazon Redshift and Google BigQuery, store structured data for complex analysis. They make it easy for businesses to create reports and draw insights.
Data Lakes: Data lakes, such as Amazon S3 and Azure Data Lake, store large amounts of raw data in its original form until it’s needed. They’re ideal for unstructured data and flexible analysis.
Data Lakehouses: Lakehouses combine data lakes and data warehouses. They store both structured and unstructured data, offering versatility for different types of analysis.
Data Pipelines: Tools like Apache Airflow and Precisely Software automate data movement and transformation. They keep data flowing smoothly from its source to storage.
Distributed Computing Platforms: These platforms allow data processing across multiple servers, speeding up the handling of large datasets.
Business Intelligence (BI) Software: BI tools like Tableau and Power BI turn data into charts and reports. They make it easy for businesses to understand and act on insights.

By using the right tools, data engineers can create reliable, scalable systems that make data easy to work with and understand.

The Role of a Data Engineer in Helping Organizations

A data engineer helps businesses make sense of their data by organizing and processing it, allowing it to provide new insights. For example, imagine a retail business that wants to track customer purchases to understand buying habits better. A data engineer would set up pipelines to pull data from the company’s sales system, organize it, and store it, preparing it for the analytics team. This setup allows the business to view and analyze trends, like which products are most popular, so that they can adjust inventory or marketing strategies.

Once the data is stored, an IT engineer keeps the system running smoothly and improves it as needed. In this example, they might set up change data capture (CDC) to keep customer data current. That would ensure inventory levels and purchase details always reflect the latest updates. They also use data observability tools to check that the data stays accurate and complete as it moves through the pipeline. Through their support, data engineers provide a solid data foundation for analysts and business leaders to make informed decisions.

Essential Skills and Qualifications for Data Engineers

Hiring the right data engineers is key to building a reliable data infrastructure that keeps business data accessible, accurate, and secure. Here are the top skills and qualifications to look for in a data engineering candidate.

Technical Skills

Programming Skills: Engineers benefit from knowing programming languages like Python, SQL, and Java to build data pipelines, automate tasks, and manage data flows. These allow them to handle different tasks efficiently.
DataOps and DevOps: These practices keep data processes running smoothly by automating workflows and improving collaboration between IT and data teams. Engineers with these skills can ensure reliable and efficient data operations.
ETL (Extract, Transform, Load) is the process of pulling data from various sources, cleaning it, and loading it into data storage systems. Proficiency in ETL tools (like Talend or Apache NiFi) means data engineers can handle large data sets and prepare them for analysis.
Cloud and Data Storage: With the shift to cloud storage, data engineers need experience with data warehousing and cloud storage platforms (like AWS or Google Cloud) to ensure that data is accessible and scalable.
Data Modeling and Database Management: Engineers should understand both SQL (for structured data) and NoSQL (for unstructured data) databases. Determining and organizing data efficiently saves time and keeps data accessible.
Data Security: Cybersecurity is essential, especially when handling sensitive information. Data engineers should know best practices for securing data, such as encryption and access control.
Batch and Stream Processing: Data engineers work with both batch processing (handling data in large chunks) and stream processing (handling real-time data). Tools like Apache Spark or Flink help them process data quickly and reliably.
Automation and Scripting: Automation, using scripting languages like Python or Shell, helps engineers avoid repetitive tasks. That, in turn, improves workflow efficiency.
Machine Learning: While not a primary focus, basic machine learning skills help data engineers prepare data for data scientists, ensuring data is ready for model training.

Data Handling Certifications

Certifications can show a candidate’s expertise. Some valuable ones include:

Credentials like these assure businesses that candidates are trained in industry-standard tools and best practices for data management. Hiring engineers with the right skills ensures businesses can get the most out of their digital information.

Why Data Engineering Is Essential for Businesses

Data engineering has become essential as businesses increasingly rely on data to make informed decisions. With an estimated 463 exabytes of data created each day globally, companies need ways to process and organize huge amounts of information to gain insights, predict trends, and stay competitive. Data engineering provides the structure that makes this possible.

The 5 Vs of big data (volume, variety, velocity, veracity, and value) highlight the challenges businesses face in handling large and complex data. Data engineers design data pipelines to manage these challenges, ensuring data flows smoothly from sources to storage, often in data lake houses or cloud systems. This setup allows companies to store and process massive structured and unstructured datasets, which supports predictive models and real-time insights.

A strong data engineering strategy helps businesses integrate data from different sources, keeping it consistent and usable. That includes managing consistent IDs across systems and creating standards so that SQL-literate employees and others can access and use data easily. Effective data integration also ensures data accuracy, which is critical when building machine learning models or predictive models that guide business decisions.

Engineers can turn raw information into insights that companies can use. It enables faster, smarter decisions, supports big data processing in the cloud. That in turn provides a foundation for advanced analytics and machine learning. This setup empowers data as an asset, rather than something to be stored away and never used again.

Let ITonDemand Help You With Your Data Needs

Data is one of the most valuable assets a business can have, but managing it well requires the right expertise and systems. Poor data quality costs organizations an average of $12.9 million. From setting up data pipelines to ensuring data quality, data engineering makes sure your data is ready for insights and decision-making. At ITonDemand, we understand the challenges of managing big data, building reliable data systems, and supporting data-driven growth.

Whether you need better data integration, cloud storage solutions, or real-time analytics, our team can help. With services tailored to your needs, ITonDemand empowers your business to make confident, informed decisions. Contact us to learn how we can support your data goals and help you get the most out of your data.

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