What is data engineering?

What is data engineering?

Data engineering is the practice of designing and building systems for the aggregation, storage and analysis of data at scale. Data engineers empower organizations to get insights in real time from large datasets.

Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists and executives, to reliably, quickly and securely inspect all of the data available.

Data analysis is challenging because the data is managed by different technologies and stored in various structures. Yet, the tools used for analysis assume the data is managed by the same technology and stored in the same structure. This rift can cause headaches for anybody trying to answer questions about business performance.

• One system contains information about billing and shipping
• Another system maintains order history
• And other systems store customer support, behavioral information and third-party data Together, this data provides a comprehensive view of the customer. However, these different datasets are independent, which makes answering certain questions — like what types of orders result in the highest customer support costs — very difficult.

Data engineering unifies these data sets and lets you find answers to your questions quickly and efficiently.

What Do Data Engineers Do?

Data engineering is a skill that is in increasing demand. Data engineers are the people who design the system that unifies data and can help you navigate it. Data engineers perform many different tasks including:

• Acquisition: Finding all the different data sets around the business
• Cleansing: Finding and cleaning any errors in the data
• Conversion: Giving all the data a common format
• Disambiguation: Interpreting data that could be interpreted in multiple ways
• Deduplication: Removing duplicate copies of data

Once this is done, data may be stored in a central repository such as a data lake or data lakehouse.

Data engineers play a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics. Historically, data engineers have carefully crafted data warehouse schemas, with table structures and indexes designed to process queries quickly to ensure adequate performance. With the rise of data lakes, data engineers have more data to manage and deliver to downstream data consumers for analytics. Data that is stored in data lakes may be unstructured and unformatted – it needs attention from data engineers before the business can derive value from it.

Fortunately, once a data set has been fully cleaned and formatted through data engineering, it’s easier and faster to read and understand. Since businesses are creating data constantly, it’s important to find software that will automate some of these processes.

The right software stack will extract a huge amount of information and value from your data, which creates end-to-end journeys for the data known as “data pipelines.” As the information travels through the pipeline, it may be transformed, enriched and summarized several times.

What is the difference between data engineering, data analysis and data science?

Data engineering, data science, and data analytics are closely related fields. However, each is a focused discipline filling a unique role within a larger enterprise. These three roles work together to ensure that organizations can make the most of their data.

• Data scientists use machine learning, data exploration and other academic fields to predict future outcomes. Data science is an interdisciplinary field focused on making accurate predictions through algorithms and statistical models. Like data engineering, data science is a code-heavy role requiring an extensive programming background.
• Data analysts examine large datasets to identify trends and extract insights to help organizations make data-driven decisions today. While data scientists apply advanced computational techniques to manipulate data, data analysts work with predefined datasets to uncover critical information and draw meaningful conclusions.
• Data engineers are software engineers who build and maintain an enterprise’s data infrastructure—automating data integration, creating efficient data storage models and enhancing data quality via pipeline observability. Data scientists and analysts rely on data engineers to provide them with the reliable, high-quality data they need for their work.