InfoQ Homepage Database Content on InfoQ
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Azure Data Lake Analytics and U-SQL
In this article, the author shows how to use big data query and processing language U-SQL on Azure Data Lake Analytics platform. U-SQL combines the concepts and constructs both of SQL and C#. It combines the simplicity and declarative nature of SQL with the programmatic power of C# including rich types and expressions.
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Data Analytics in the World of Agility
Is it all about customer-centric business, or is there any data left? Can we integrate data analytics and customer empathy? This article explores how we can move towards a more customer-centric business and what information we require in order to understand the most valuable thing we have: our customer.
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Stream Processing Anomaly Detection Using Yurita Framework
In this article, author Guy Gerson discusses the stream processing anomaly detection framework they developed by PayPal, called Yurita. The framework is based on Spark Structured Streaming.
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Understanding Serverless: Tips and Resources for Building Servicefull Applications
There are still many misconceptions and concerns regarding serverless solutions. Vendor lock-in, tooling, cost management, cold starts, monitoring and the development lifecycle are all hot topics where serverless technologies are concerned. This article shares tips and resources to guide serverless newcomers towards building powerful, flexible and cost-effective serverless applications.
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How to Use Open Source Prometheus to Monitor Applications at Scale
In this article, the author discusses how to collect metrics and achieve anomaly detection from streaming data using Prometheus, Apache Kafka and Apache Cassandra technologies.
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How Do We Think about Transactions in (Cloud) Messaging Systems? An Interview with Udi Dahan.
Do today's cloud-based messaging services have different transactional support than those that preceded it? If so, what are the implications? In this interview with distributed systems expert Udi Dahan, we explores the question.
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Real-Time Data Processing Using Redis Streams and Apache Spark Structured Streaming
Structured Streaming, introduced with Apache Spark 2.0, delivers a SQL-like interface for streaming data. Redis Streams enables Redis to consume, hold and distribute streaming data between multiple producers and consumers. In this article, author Roshan Kumar walks us through how to process streaming data in real time using Redis and Apache Spark Streaming technologies.
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The Data Science Mindset: Six Principles to Build Healthy Data-Driven Organizations
In this article, business and technical leaders will learn methods to assess whether their organization is data-driven and benchmark its data science maturity. They will learn how to use the Healthy Data Science Organization Framework to nurture a data science mindset within the organization.
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Using TypeScript with the MySQL Database
TypeScript has emerged as a powerful environment for authoring web applications, providing significant improvements over standard JavaScript while remaining consistent with the language. In this article we'll explore in depth the details necessary to use TypeScript with Node.js, MySQL, and TypeORM to create a powerful solution for managing database access with server-side TypeScript.
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Sleeping Well at Night During a Live Cloud Migration in a VMware Environment
This article describes the challenges of live migration to the cloud and presents key concepts and requirements that enterprises and their service providers need to understand and adopt if they want to sleep well at night when migrating on-premises VMs and data to the cloud.
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Q&A on the Book Evidence-Based Management
The book Evidence-Based Management by Eric Barends and Denise Rousseau explores how to acquire evidence, appraise the quality of the data, apply it in your management decisions, and assess the impact of your decisions.
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Conquering the Challenges of Data Preparation for Predictive Maintenance
Predictive maintenance (PdM) applications aim to apply machine learning (ML) on IIoT datasets in order to reduce occupational hazards, machine downtime, and other costs. In this article, the author addresses some of the data preparation challenges faced by the industrial practitioners of ML and the solutions for data ingest and feature engineering related to PdM.