Maximise Your Business Potential with Expert Data Engineering

Many businesses need help turning vast amounts of data into usable information. Research shows up to 73% of business data needs to be analysed, leaving valuable insights untapped.

Data engineering helps solve this problem by developing systems that organise raw data, making it easily accessible and actionable. By building efficient data pipelines, businesses can transform data into valuable insights that drive long-term success.

What Is Data Engineering?

Data engineering is creating systems to collect, store, organise, and process data, enabling businesses to use it effectively. The focus is on building pipelines that transform raw data into a usable format, allowing for deeper insights and informed decision-making. Data engineers rely on tools like databases and cloud services to manage large volumes of data. By setting up this infrastructure, they empower businesses to make better decisions and identify new opportunities.

How Does Data Engineering Work?

Data engineering involves setting up systems to collect, organise, and prepare data for easy access and use. Central to this is the data pipeline, which transfers data from its source to storage, often a data warehouse or lakehouse. For example, a small business might consolidate all customer data in one place, enabling them to spot purchasing trends over time.

Data engineers typically use ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) to make this data actionable. In an ETL process, a business extracts sales data, cleans and organises it, and loads it into a data warehouse for analysis. ELT is similar but loads raw data first, often into a cloud-based system, where it’s then transformed as needed.

To keep the data current, Change Data Capture (CDC) tracks updates so the stored data reflects real-time information, such as current inventory levels. Data modelling structures the data to meet business needs, making it easier to answer questions like which products are the top weekend sellers.

Data observability tools monitor the data pipeline’s reliability, ensuring smooth operations. Distributed computing platforms such as Azure Synapse Analytics can quickly process data across multiple data sources for businesses with large or fast-evolving datasets. Data engineering has become essential since poor data management can cost companies up to 12% of their revenue.

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

Understanding the differences between data engineering, data science, and data analysis helps clarify how each discipline contributes to leveraging data in business. While all three roles are critical, each has its unique focus: building systems, identifying trends, or answering specific business questions.

Data Engineering: Building the Foundation

Data engineering creates and manages systems to collect, store, and organise data. Data engineers design and build the infrastructure that makes data more accessible, setting up ETL pipelines to move data from various sources into storage, like data warehouses, and ensuring seamless data integration. They also monitor the reliability of the pipeline and manage data storage models to maintain data consistency and accuracy.

Data Science: Finding Insights and Predictions

Data scientists analyse the data organised by engineers to uncover valuable insights and make predictions. They design algorithms and apply statistical models to identify patterns within the data. Often working with machine learning, data scientists build models that predict future outcomes based on past data. This role requires programming, mathematical expertise, and tools like Python or R to process and analyse large datasets. Data scientists help businesses recognise trends and forecast future developments.

Data Analysis: Turning Data into Answers

Data analysts focus on turning organised data into actionable insights. They use various tools to generate reports and visualisations that highlight key findings. While advanced programming skills are only sometimes necessary, data analysts must have a solid understanding of data quality and integration to ensure accurate results. They work closely with business teams to translate data into information that supports decision-making.

Data engineers build the systems that make data accessible, data scientists analyse this data to derive insights and predictions, and data analysts interpret the data to answer specific business questions. Together, these roles enable businesses to leverage data effectively, with data engineering providing the foundation for deeper analysis and insights.

Common Data Engineering Tools and Technology

Data engineering involves a variety of tools and technologies to store, organise, and process data efficiently. These tools help businesses manage data and ensure it’s ready for analysis. Here are some of the key tools and technologies used in data engineering:

Cloud Computing Platforms: Services like Amazon S3, Google Cloud, and Microsoft Azure Data Lake provide scalable, flexible storage, making it easier to handle large volumes of data.
Relational Database Management Systems (RDBMS): Traditional databases like MySQL and PostgreSQL organise data in structured tables. These are ideal for applications that require consistent, structured data.
NoSQL Databases: NoSQL databases, such as MongoDB and Apache Cassandra, are designed for unstructured data and offer flexibility, making them great for applications that need to handle evolving data formats.
Data Warehouses: Tools like Amazon Redshift and Google BigQuery store structured data for in-depth analysis. They enable businesses to create detailed reports and extract valuable insights.
Data Lakes: Data lakes, including Amazon S3 and Azure Data Lake, store large amounts of raw, unstructured data until needed. They’re perfect for handling unstructured data and enabling flexible analysis.
Data Lakehouses: Combining the features of data lakes and data warehouses, data lakehouses allow businesses to store and analyse structured and unstructured data.
Data Pipelines: Tools like Apache Airflow and Precisely Software automate data movement and transformation, ensuring it flows seamlessly from its source to storage.
Distributed Computing Platforms: Platforms like Apache Spark enable data processing across multiple servers, accelerating the handling of large datasets.
Business Intelligence (BI) Software: BI tools such as Power BI convert data into visual reports and charts, making it easier for businesses to understand and act on insights.

By leveraging the right tools, data engineers can build reliable, scalable systems that ensure data is accessible, organised, and ready for analysis, enabling businesses to extract maximum value from their data.

The Role of a Data Engineer in Helping Organisations

A data engineer plays a crucial role in transforming raw data into something businesses can use by organising and processing it effectively. For example, consider a retail business aiming to track customer purchases and identify buying trends. A data engineer would set up data pipelines that extract sales data, organise it, and store it in a system ready for analysis. This allows the business to identify popular products and adjust strategies accordingly, such as tweaking inventory or refining marketing efforts.

Once the data is stored, an IT engineer ensures the system runs smoothly and is continually optimised. For instance, they might implement change data capture (CDC) to ensure customer data stays current, reflecting the latest purchase and inventory information. Data observability tools are used to monitor the integrity and accuracy of the data as it moves through the pipeline. Through their support, data engineers provide the solid foundation necessary for business analysts and leaders to make data-driven decisions.

Essential Skills and Qualifications for Data Engineers

To build a reliable data infrastructure that ensures data remains accessible, accurate, and secure, hiring the right data engineers is important. Here are the essential skills and qualifications to look for in a data engineering candidate.

Technical Skills

Programming Skills: Data engineers should be proficient in programming languages like Python, SQL, and Java to build data pipelines, automate processes, and manage data workflows.
DataOps and DevOps: These methodologies automate workflows and improve collaboration between IT and data teams, ensuring efficient data operations.
ETL (Extract, Transform, Load): Engineers should be skilled in ETL tools (e.g., Talend, Apache NiFi), which allow them to pull data from various sources, clean it, and load it into storage systems for analysis.
Cloud and Data Storage: With the rise of cloud technology, engineers must have experience with cloud platforms (like AWS Google Cloud) to manage scalable data storage.
Data Modeling and Database Management: Understanding SQL (structured data) and NoSQL (unstructured data) databases is critical for organising data efficiently, making it accessible and easier to manage.
Data Security: To secure sensitive information, data engineers must be well-versed in data protection practices, such as encryption and access control.
Batch and Stream Processing: Data engineers must be able to work with both batch processing (large chunks of data) and stream processing (real-time data) using tools like Apache Spark or Flink.
Automation and Scripting: Automation, using scripting languages like Python or Shell, helps data engineers streamline repetitive tasks, improving workflow efficiency.
Machine Learning: While not a core focus, basic knowledge of machine learning helps data engineers prepare data for data scientists, ensuring it’s ready for training models.

Data Handling Certifications

Certifications validate a candidate’s expertise and are an essential consideration during hiring. Notable certifications include:

Microsoft Certified: Azure Data Engineer Associate
AWS Certified Big Data – Specialty
Google Professional Data Engineer
Cloudera Certified Professional (CCP) Data Engineer

These certifications ensure candidates are trained in industry-standard tools and practices, helping businesses manage their data effectively.

Why Data Engineering Is Essential for Businesses

Data engineering has become vital as businesses increasingly rely on data to guide decision-making. With an estimated 463 exabytes of data being created every day globally, companies need robust systems to process and organise large amounts of data to uncover insights, predict trends, and remain competitive. Data engineering provides the structure to make this possible.

The 5 Vs of big data

Volume,
Variety,
Velocity,
veracity, and
Value

Illustrate businesses’ challenges when handling vast and complex datasets. Data engineers address these challenges by building pipelines that ensure smooth data flow from various sources to storage systems, often using data lakes or cloud platforms. This infrastructure supports processing massive datasets, enabling businesses to perform predictive analytics and gain real-time insights.

A strong data engineering strategy also allows businesses to integrate data from multiple sources, ensuring consistency and accuracy. This includes managing consistent identifiers across systems and establishing standards that make data accessible to technical and non-technical teams. Proper data integration is key when building machine learning models or predictive systems that drive business decisions.

Ultimately, data engineers turn raw data into actionable insights, helping businesses make faster, smarter decisions. By leveraging cloud-based big data processing, they enable advanced analytics and machine learning, transforming data into a valuable asset rather than an idle resource.

Let Intuitive IT Help You With Your Data Needs

Data is a business’s most valuable asset, but managing it effectively requires expertise and systems. Poor data quality costs organisations an average of $12.9 million annually. From setting up data pipelines to ensuring data accuracy, data engineering ensures your data is ready for meaningful insights and informed decision-making.

At IIT, we understand the complexities of managing big data, building reliable data infrastructure, and supporting data-driven growth.

Whether you need improved data integration, cloud storage solutions, or real-time analytics, our team is here to help. With services customised to your specific needs,IIT empowers your business to make confident, data-driven decisions.

Maximise Your Business Potential with Expert Data Engineering