When would you choose Pig over Flink or Storm?
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what is apache pig
- What are some common geospatial analysis tasks that can be done with Pig? How does Apache Pig handle time series data?
- How do you perform aggregations in Pig? What are the different aggregation functions in Pig?
- What are some common patterns for deploying machine learning models trained on Pig data? How does Apache Pig handle the optimization of data pipelines for specific use cases or workloads, tailoring configurations and processing logic for maximum efficiency?
- Can you influence the scheduling priority of Pig jobs? How does Apache Pig handle speculative execution of tasks?
- Are there better tools for machine learning on Hadoop? How does Apache Pig handle geospatial data?
- Is Pig well-suited for real-time data processing? What are some alternatives to Pig for streaming data processing?
- Can you control whether speculative execution is enabled for Pig jobs? How does Apache Pig handle container reuse in YARN?
- Can Pig contribute to or leverage data virtualization solutions?
- Does Pig support different character sets and locales?
- What kind of performance gains can be expected from these advanced techniques? How does Apache Pig handle the challenges of data heterogeneity and variety in big data environments?
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