InfoQ Homepage Infrastructure Content on InfoQ
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‘Debt’ as a Guide on the Agile Journey: Technical Debt
In this article in a series on how ‘debt’ can be used to guide an agile journey, we will provide two examples of smells that are related to technical debt, explain the symptoms, the impact on the business and in our organization, outline the experiments (countermeasures) that we have introduced in an effort to try to remove the smell, and provide some specific advice for you to be inspired.
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Accelerating Deep Learning on the JVM with Apache Spark and NVIDIA GPUs
In this article, authors discuss how to use the combination of Deep Java Learning (DJL), Apache Spark v3, and NVIDIA GPU computing to simplify deep learning pipelines while improving performance and reducing costs. They also show the performance comparison of this solution with GPU vs CPU hardware, using Amazon EMR and NVIDIA RAPIDS Accelerator.
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Evolution of Azure Synapse: Apache Spark 3.0, GPU Acceleration, Delta Lake, Dataverse Support
At Microsoft Build 2021, Azure Synapse has announced significant improvements for its Apache Spark pool, its performance, and data querying and integration capabilities. This article outlines the improvements and provides the context.
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Implementing Microservicilities with Quarkus and MicroProfile
Microservicilities is a list of cross-cutting concerns that a service must implement apart from the business logic. These concerns include invocation, elasticity and resiliency, among others. This article describes how Quarkus and MicroProfile may be used to implement these concerns.
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Indestructible Storage in the Cloud with Apache Bookkeeper
At Salesforce, we required a storage system that could work with two kinds of streams, one stream for write-ahead logs and one for data. But we have competing requirements from both of the streams. Being the pioneers in cloud computing, we also required our storage system to be cloud-aware as the requirements of availability and durability are ever more increasing.
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Platform Engineering as a (Community) Service
Nicki Watt shares how successful platform engineering initiatives start by adjusting their thinking to centre around people and communities, and their experience consuming the platform, with examples.
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The Evolution of Precomputation Technology and its Role in Data Analytics
In this article, author Yang Li discusses the importance of precomputation techniques in databases, OLAP and data cubes, and some of the trends in using precomputation in big data analytics.
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Performance Tuning Techniques of Hive Big Data Table
In this article, author Sudhish Koloth discusses how to tackle performance problems when using Hive Big Data tables.
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The Brain is Neither a Neural Network Nor a Computer: Book Review of The Biological Mind
Underlying much of artificial intelligence research is the idea that the essence of an individual resides in the brain. This is contrary to neuroscience which has discovered that a brain cannot work independently from the body and its environment. Understanding this enables us see what is reasonable to expect from artificial intelligence, as well as technology designed to improve human life.
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The Kollected Kode Vicious Review and Author Q&A
Addison Wesley Professional The Kollected Kode Vicious by George V. Neville-Neil aims to provide thoughtful and pragmatic insight into programming to both experienced and younger software professionals on a variety of different topics related to programming. InfoQ has taken the chance to speak with author Neville-Neil about his book.
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Donkey: a Highly-Performant HTTP Stack for Clojure
Donkey is the product of the quest for a highly performant Clojure HTTP stack aimed to scale at the rapid pace of growth we have been experiencing at AppsFlyer, and save us computing costs. In this article, we’ll briefly outline the use-case for a library like Donkey and present our benchmarks. Finally, we will discuss Clojure and immutability, and some of our design decisions.
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Overcoming Data Scarcity and Privacy Challenges with Synthetic Data
In this article, the author discusses the importance of using synthetic data in data analytics projects, especially in financial institutions, to solve the problems of data scarcity and more importantly data privacy.