Data Engineering

Across the globe, companies aren't just collecting data, they are rethinking how it's stored, accessed, processed, and trusted by both internal and external users and stakeholders. And with the growing adoption of generative and agentic AI tools, there is a renewed focus on data hygiene, security, and observability.

Engineering teams are also under constant pressure to streamline complexity, build scalable pipelines, and ensure that their data is high quality, AI ready, available, auditable, and actionable at every step. This means making a shift from fragmented tooling to more unified, automated tech stacks driven by open-source innovation and real-time capabilities.

In DZone's 2025 Data Engineering Trend Report, we explore how data engineers and adjacent teams are leveling up. Our original research and community-written articles cover topics including evolving data capabilities and modern use cases, data engineering for AI-native architectures, how to scale real-time data systems, and data quality techniques. Whether you're entrenched in CI/CD data workflows, wrangling schema drift, or scaling up real-time analytics, this report connects the dots between strategy, tooling, and velocity in a landscape that is only becoming more intelligent (and more demanding).

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