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Geek Reading - Week of June 5, 2013

DZone

Simpler UI Testing with CasperJS ( Architects Zone – Architectural Design Patterns & Best Practices). Using MongoDB as a cache store ( Architects Zone – Architectural Design Patterns & Best Practices). Linux System Mining with Python ( Javalobby – The heart of the Java developer community). Java EE 7 is Final.

Java 244
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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 focused on technology coverage, building on the flexibility of JMX for Java and Python-based coded extensions for everything else. Reporting and analytics assets out-of-the-box Bundles offered by Extensions 2.0 Extensions 2.0

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Citrix monitoring with Dynatrace: Easily observe your entire Citrix ecosystem

Dynatrace

Many companies rely on Citrix as a critical component of their infrastructure that demands thorough observability and integrated analytics across the entire application landscape. Automated AI-powered analytics are necessary to match the scale of monitoring these enterprises require.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Java applications use a similar mechanism.). In-Memory Data Grids for Fast-Changing Data.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Java applications use a similar mechanism.). In-Memory Data Grids for Fast-Changing Data.

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Scalable Annotation Service?—?Marken

The Netflix TechBlog

All data should be also available for offline analytics in Hive/Iceberg. Unlike Java, we support multiple inheritance as well. Data ingestions from the ML data pipelines are generally in bulk specifically when a new algorithm is designed and annotations are generated for the full catalog. Currently the service has around 1.9

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Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

Production Use Cases Real-Time APIs (backed by the Cassandra database) for asset metadata access don’t fit analytics use cases by data science or machine learning teams. This feature support required a significant update in the data table design (which includes new tables and updating existing table columns).

Media 237