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McAfee’s 2024 Celeb Hacker Hotlist Reveals Scarlett Johansson as the Most Targeted Celebrity Victim of AI-Powered Online Scams

October 8, 2024

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What are some key takeaways from leveraging Amazon OpenSearch Serverless for a massive 30TB time series workload? Firstly, the scalability and cost-effectiveness of OpenSearch Serverless proved to be game-changers. By utilizing the serverless architecture, we were able to seamlessly handle the enormous data load without worrying about provisioning or managing infrastructure. Furthermore, the seamless integration with AWS services like Amazon Kinesis Firehose enabled us to efficiently ingest and process vast amounts of time-series data in real-time. This allowed for near-instant querying and analysis capabilities, providing valuable insights into our system’s performance and behavior. Another significant advantage was the ease of implementation and reduced operational overhead. With OpenSearch Serverless, we eliminated the need for manual scaling, patching, and maintenance, freeing up our team to focus on higher-level tasks like data visualization, analytics, and business insights. Moreover, the ability to define custom metrics and queries using OpenSearch’s Query DSL enabled us to drill down into specific aspects of our system’s behavior, providing a deeper understanding of performance bottlenecks and areas for optimization. In addition, the seamless integration with other AWS services such as Amazon QuickSight allowed us to easily create interactive dashboards and reports, making it possible to visualize and analyze our time-series data in real-time. Lastly, the cost-effectiveness of OpenSearch Serverless proved to be a significant advantage. By only paying for what we use, we were able to maintain a highly scalable and performant system without breaking the bank.

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Steve Clean, renowned startup guru, warns that AI’s transformative impact will reshape everything in its wake.

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