Data Engineering Strategies for Scalable Enterprise Systems

As enterprises become data-centric, data engineering has emerged as a key IT focus for ensuring that data is smoothly collected, stored, processed, and delivered across systems at scale. Building scalable data pipelines and systems that support a rapidly growing workforce, an increasingly diverse set of data types—both for structured and unstructured data—and a rising set of services that explore and analyze data requires new strategies. Data engineering considers methods, processes, and technologies able to ensure that the needs of business-critical use cases are followed by all of the following data engineering formal practices. Central data lakes help fulfill such needs, but architectural foundations also encompass well-defined layered data approaches aligned with best-of-breed storage systems and data models; data quality management, governance, and certification capabilities; development, deployment, and operational management solutions; and strategic support for cyber-physical systems.