Miguel Garcia

Trend Report

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).

Trend Report

Data Engineering

Over a decade ago, DZone welcomed the arrival of its first ever data-centric publication. Since then, the trends surrounding the data movement have held many titles — big data, data science, advanced analytics, business intelligence, data analytics, and quite a few more. Despite its varying vernacular, the purpose has remained the same: to build intelligent, data-driven systems. The industry has come a long way from organizing unstructured data and driving cultural acceptance to adopting today's modern data pipelines and embracing business intelligence capabilities.This year's Data Engineering Trend Report draws all former terminology, advancements, and discoveries into the larger picture, illustrating where we stand today along our unique, evolving data journeys. Within these pages, readers will find the keys to successfully build a foundation for fast and vast data intelligence across their organization. Our goal is for the contents of this report to help guide individual contributors and businesses alike as they strive for mastery of their data environments.

Trend Report

Data Pipelines

Enter the modern data stack: a technology stack designed and equipped with cutting-edge tools and services to ingest, store, and process data. No longer are we using data only to drive business decisions; we are entering a new era where cloud-based systems and tools are at the heart of data processing and analytics. Data-centric tools and techniques — like warehouses and lakes, ETL/ELT, observability, and real-time analytics — are democratizing the data we collect. The proliferation of and growing emphasis on data democratization results in increased and nuanced ways in which data platforms can be used. And of course, by extension, they also empower users to make data-driven decisions with confidence.In our 2023 Data Pipelines Trend Report, we further explore these shifts and improved capabilities, featuring findings from DZone-original research and expert articles written by practitioners from the DZone Community. Our contributors cover hand-picked topics like data-driven design and architecture, data observability, and data integration models and techniques.

Trend Report

Data Pipelines

Data is at the center of everything we do. As each day passes, more and more of it is collected. With that, there’s a need to improve how we accept, store, and interpret it. What role do data pipelines play in the software profession? How are data pipelines designed? What are some common data pipeline challenges? These are just a few of the questions we address in our research.In DZone’s 2022 Trend Report, "Data Pipelines: Ingestion, Warehousing, and Processing," we review the key components of a data pipeline, explore the differences between ETL, ELT, and reverse ETL, propose solutions to common data pipeline design challenges, dive into engineered decision intelligence, and provide an assessment on the best way to modernize testing with data synthesis. The goal of this Trend Report is to provide insights into and recommendations for the best ways to accept, store, and interpret data.