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Data engineering involves the development, implementation, and maintenance of systems that transform raw data into high-quality, actionable information. This field integrates several critical domains, including security, data management, DataOps, data architecture, orchestration, and software engineering. In essence, data engineering focuses on building robust platforms for collecting, processing, and utilizing digital information to drive organizational success. These platforms are designed to manage data flow efficiently and create comprehensive infrastructures that support business intelligence and decision-making processes.
As data engineering projects grow in popularity and complexity, teams often face numerous challenges. This white paper explores the four most common challenges in data engineering:
- Data Harmonization: Ensuring consistency and compatibility of data from various sources.
- Hybrid Data Integration: Seamlessly integrating on-premises and cloud-based data systems.
- Enterprise Governance at Scale: Implementing governance frameworks that scale with growing data volumes.
- Data Center Operations: Managing the operations and maintenance of data centers to ensure optimal performance.
