InfoQ Homepage Data Content on InfoQ
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Taming the Data Mess, How Not to Be Overwhelmed by the Data Landscape
Ismaël Mejía reviews the current data landscape and discusses both technical and organizational ideas to avoid being overwhelmed by the current lack of consolidation of the data engineering world.
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Data Versioning at Scale: Chaos and Chaos Management
Einat Orr discusses several technologies that version large data sets, the use cases they support and the technology developed to best support those use cases.
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Modern Data Pipelines in AdTech—Life in the Trenches
Roksolana Diachuk discusses how to use modern data pipelines for reporting and analytics as well as the case of historical data reprocessing in AdTech.
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Protecting User Data via Extensions on Metadata Management Tooling
Alyssa Ransbury overviews the current state of metadata management tooling, and details how Square implemented security on its data.
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Building & Operating High-Fidelity Data Streams
Sid Anand discusses building high-fidelity nearline data streams as a service within a lean team.
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Data-driven Development in the Automotive Field
Toshika Srivastava offers insight into how they in the automotive field are developing products with data and what their challenges are.
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Data Mesh Paradigm Shift in Data Platform Architecture
Zhamak Dehghani introduces Data Mesh, the next generation data platform, that shifts to a paradigm drawing from modern distributed architecture.
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Future of Data Engineering
Chris Riccomini talks about the current state-of-the-art in data pipelines & data warehousing, and shares some of the solutions to current problems dealing with data streaming & warehousing.
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Announcing Broadway
José Valim discusses how Broadway connects multiple stages and producers, how it leverages GenStage to provide back-pressure, and other features such as batching, rate-limiting, partitioning and more.
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A Dive into Streams @LinkedIn with Brooklin
Celia Kung talks about Brooklin, LinkedIn’s managed data streaming service, and dives deeper into its architecture and use cases, as well as their future plans.
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Panel: Predictive Architectures in Practice
The panelists discuss the unique challenges of building and running data architectures for predictions, recommendations and machine learning.
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PCF Data Collection for TBM
Chris Busch and Raj Sivaraj overview the foundation data collection needs of Mastercard, and how data is collected and provided back to product teams with critical expense data for show-back.
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