InfoQ Homepage Data Analytics Content on InfoQ
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Is Edge Computing a Thing?
Edge Computing is definitely a thing, but the computing need not occur at the edge. Instead what is needed is an ability to compute (anywhere) on streaming data from large numbers of dynamically changing devices, in the edge environment. This in turn demands an architectural pattern for stateful, distributed computing.
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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.
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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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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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Apache Kafka: Ten Best Practices to Optimize Your Deployment
Author Ben Bromhead discusses the latest Kafka best practices for developers to manage the data streaming platform more effectively. Best practices include log configuration, proper hardware usage, Zookeeper configuration, replication factor, and partition count.
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Natural Language Processing with Java - Second Edition: Book Review and Interview
Natural Language Processing with Java - Second Edition book covers the Natural Language Processing (NLP) topic and various tools developers can use in their applications. Technologies discussed in the book include Apache OpenNLP and Stanford NLP. InfoQ spoke with co-author Richard Reese about the book and how NLP can be used in enterprise applications.
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Democratizing Stream Processing with Apache Kafka® and KSQL - Part 2
In this article, author Robin Moffatt shows how to use Apache Kafka and KSQL to build data integration and processing applications with the help of an e-commerce sample application. Three use cases discussed: customer operations, operational dashboard, and ad-hoc analytics.
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How to Choose a Stream Processor for Your App
Choosing a stream processor for your app can be challenging with many options to choose from. The best choice depends on individual use cases. In this article, the authors discuss a stream processor reference architecture, key features required by most streaming applications and optional features that can be selected based on specific use cases.
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Democratizing Stream Processing with Apache Kafka and KSQL - Part 1
In this article, author Michael Noll discusses the stream processing with KSQL, the streaming SQL engine for Apache Kafka. Topics covered include challenges of stateful stream processing and how KSQL addresses them, and how KSQL helps to bridge the world of streams and databases through streams and tables.
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Pascal Desmarets on NoSQL Data Modeling Best Practices
NoSQL databases are specialized to store different types of data like Key Value, Documents, Column Family, Time Series, Graph, and IoT data. Pascal Desmarets talks about how to perform data modeling in NoSQL databases compared to the modeling in Relational databases.