Data Engineering

What is Data Engineering?

Today’s companies are collecting enormous amounts of data that needs to be analyzed, stored, and protected. Data engineering builds and maintains the infrastructure that holds and transforms this information to be reliably used for analysis and AI applications. It is the foundational bedrock on which an analytics, machine learning, and intelligent product development are built.

How does Data Engineering work?

Modern data engineering relies on cloud-native platforms, such as Apache Spark, dbt, and Airflow. Data engineers design pipelines that take data from sources like operational databases, APIs, event streams, and external feeds and store them in cloud data warehouses and lake house architectures. These pipelines rapidly move the data through stages that clean and restructure it for businesses to make the critical decisions to meet their goals. To ensure that the data remains clean and reliable, data quality controls are monitored. This control is especially important as the volume and complexity of data increases. GlobalLogic’s Data Engineering teams build the platforms and pipelines for enterprises that need to scale data into a strategic asset in production, with the governance that enterprise environments demand. 

What are the business benefits of Data Engineering?

• Provides a unified data infrastructure on which analytics, AI, and machine learning initiatives depend

• Eliminating manual data preparation and fragmented reporting processes, saving time and reducing errors
• Enables real-time decision-making by streamlining pipeline design and development of live data products
• Improves data quality and consistency enterprise-wide, increasing accuracy and reliability
• Creates a scalable foundation that grows with the business, lessening the requirement of a rebuild when decisions change