Miguel Garcia

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.