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Use Analytics to Innovate and Lead in Today's Banking Environment
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Streamline hybrid cloud operations with unified management
As businesses shift to public clouds, managing hybrid and multicloud setups grows complex. Red Hat Insights simplifies this with services that streamline operations, bolster security, and manage costs for on-prem and cloud systems.
Insights features:
- Predictive analytics to preempt operational risks and security issues
- Configuration drift detection for system stability and compliance
- Automated patching for Red Hat Enterprise Linux
- Easy OS image provisioning across hybrid clouds
Red Hat Insights can reduce management tasks by 96%, enhancing IT efficiency and stability in cloud environments.
Learn how Red Hat Insights aids Google Cloud management in this white paper.
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Which SSD type deserves your data?
Flash storage has built up quite the reputation for being fast. And when it comes to data analytics, SSDs don’t disappoint because solid-state technology can perform analytics on big data more efficiently. However, there’s a bit of a catch.
In this guide, storage expert – Phil Goodwin – discusses how flash storage technology is only advantageous in certain types of environments. Discover which kind of SSD deployment is best designed for your environment’s big data needs.
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Spark Muscles Into Big Data Processing
It comes as no great surprise that the Apache Spark architecture has been horning in on the batch processing domain once controlled by Hadoop's MapReduce. But that's only part of the story. With data processing, streaming and machine learning capabilities on its résumé, the open source engine is learning to get along entirely without Hadoop in certain applications. In fact, one industry analyst cautiously sees a day when Spark could declare total independence, potentially bust up Hadoop cluster dominance and link separately with other Apache technologies.
In this three-part handbook, senior news writer Jack Vaughan examines the distinct advantages the Apache Spark architecture has over MapReduce. Also highlighted is how Spark's ability to process and analyze streaming data is helping detect fraudulent activities at a major banking and credit-card company. Next, Spark 2.0's upcoming upgrades to analytics speed, machine learning libraries, SQL support and stream processing are detailed. To close, Vaughan and senior news writer Ed Burns look at combining Spark and NoSQL databases in operational analytics applications, which could help broaden the use of both technologies.
