InfoQ Homepage Data Content on InfoQ
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Designing Visualizations for Action
Chris Varosy discusses strategies for designing data visualizations and dashboards that bring the insight users need to make decisions.
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Data Consistency in Microservice Using Sagas
Chris Richardson discusses messaging, durability, and reliability in microservice architectures leveraging the Saga Pattern, explaining how sagas work and introduces a saga framework for Java.
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Systems That Learn
Stephen Buckley discusses the Systems That Learn initiative which aims to create systems that learn by combining expertise in Systems and Machine Learning.
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Homoiconicity: It Is What It Is
Stuart Sierra demonstrates the power that comes from having the same data representation at all layers: programming language, specification, database, inter-process communication, and user interface.
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Data-Driven Coaching - Safely Turning Team Data into Coaching Insights
Troy Magennis shows how to expose data to teams in order for them to retrospect productively, determine if a process experiment is panning out as expected, and to explore process change opportunities.
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Machine Learning in Academia and Industry
Deborah Hanus discusses some of the challenges that can arise when working with data.
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AI-Based Data Extraction
George Roth presents the challenges of data extraction from unstructured content in the context of preparing the data for Data Analytics.
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Data Preparation for Data Science: A Field Guide
Casey Stella presents a utility written with Apache Spark to automate data preparation, discovering missing values, values with skewed distributions and discovering likely errors within data.
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Straggler Free Data Processing in Cloud Dataflow
Eugene Kirpichov describes the theory and practice behind Cloud Dataflow's approach to straggler elimination, and the associated non-obvious challenges, benefits, and implications of the technique.
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Scaling up Near Real-Time Analytics @Uber &LinkedIn
Chinmay Soman and Yi Pan discuss how Uber and LinkedIn use Apache Samza, Calcite and Pinot along with the analytics platform AthenaX to transform data to make it available for querying in minutes.
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Effective Data Pipelines: Data Mngmt from Chaos
Katharine Jarmul discusses implementation decisions for those looking for a practical recommendation on the "what" and "how" of data automation workflows.
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Building Data Pipelines in Python
Marco Bonzanini discusses the process of building data pipelines and all the steps necessary to prepare data, focusing on data plumbing and going from prototype to production.