InfoQ Homepage Development Content on InfoQ
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Getting to Know Deep Java Library (DJL)
Amazon has announced DJL, an open source library to develop Deep Learning models in Java. This article details how to get started with the toolkit. The library aims to reduce number of software dependencies by enabling end-end Deep learning development in Java, rather than having to use additional technologies such as Python or R.
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Key Takeaway Points and Lessons Learned from QCon San Fransisco 2019
This year’s QCon San Fransisco featured 177 speakers, track hosts, workshop presenters, and committee members. These are people like one of the foremost thinkers in the DevOps movement John Willis, CEO/Co-Founder of DarkLang Ellen Chisa, and VP Cloud Architecture Strategy @AWSCloud Adrian Cockcroft.
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The Future of Spring Cloud's Hystrix Project
The Spring Cloud Hystrix Project was built on top of the similarly-named Netflix project. The latter has recently been placed into maintenance mode, leaving Java developers wondering where to migrate to. The Resilience4j project aims to fill that gap and provide continuity for cloud native Java projects.
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InfoQ Editors' Recommended Talks from 2019
As part of the 2019 end-of-year-summary content, this article collects together a list of recommended presentation recordings from the InfoQ editorial team.
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InfoQ's 2019, and Software Predictions for 2020
We take a look back at what we saw on InfoQ in 2019, and think about what the next year might bring.
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Quarkus, a Kubernetes Native Java Framework, Reaches Version 1.0: Q&A with Thomas Qvarnstrom
Quarkus, a Kubernetes native Java framework tailored for GraalVM and OpenJDK HotSpot, has reached version 1.0. Quarkus is an Open Source stack for writing Java applications, offering unparalleled startup time, memory footprint and developer experience. InfoQ spoke with Thomas Qvarnstrom, senior principal product manager at Red Hat, in order to learn about the Quarkus journey, extensions, and more.
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Adoption of Cloud-Native Architecture, Part 1: Architecture Evolution and Maturity
In this article, authors Srini Penchikala and Marcio Esteves discuss what organizations should assess when adopting cloud native architectures for hosting their applications on cloud. It focuses on architecture hosting models. They also discuss how architecture patterns like microservices, containers, serverless, and service mesh can help with organizational adoption of cloud native solutions.
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Lessons Learned in Performance Testing
Performance testing is a hard discipline to get right and many things can go wrong. The key is to pay attention to the details, understand the behavior, and avoid just producing fancy numbers. This article describes a few common problems seen frequently with performance testing and shares tips on how to make your performance testing routine better.
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Predicting Time to Cook, Arrive, and Deliver at Uber Eats
Time predictions are critical to Uber Eats' business as they determine when to dispatch delivery partners as well as ensure customer satisfaction. This article explains how their dispatch system evolved through time predictions powered by machine learning, followed by a deep dive on how to predict food preparation time without ground truth data. It goes over delivery and travel time predictions.
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13 Practices for Better Code Reviews
When done incorrectly, code review can be irritating, excessively time consuming, and have little or no impact on code quality. However, if done well, it can improve the quality of code and reduce the overall time spent delivering features. This article provides several good practices regarding both technical and cultural aspects of code review.
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Book Review: A Leader's Guide to Cybersecurity
A Leader's Guide to Cybersecurity educates readers about how to prevent a crisis and/or take leadership when one occurs. With a focus on clear communication, the book provides details, examples, and guidance of mapping security against what a business actually does. The book describes ways to align security with the motivation of others who may be security-agnostic against their own goals.
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/articles/redis-time-series-grafana-real-time-analytics/en/smallimage/use-redis-timeseries-with-grafana-for-real-time-analytics-logo-small-1573477936722.jpg)
How to Use Redis TimeSeries with Grafana for Real-Time Analytics
In this article, author Roshan Kumar discusses how a purpose-built database like RedisTimeSeries can be used to manage time-series data. He also shows how to visualize this data in a Grafana dashboard.