What are the best parallel tools for data processing?
Some of the best parallel tools for data processing include Apache Spark, Dask, and Hadoop. Each has its strengths depending on your needs.
Can you explain how Apache Spark works?
Apache Spark is an open-source distributed computing system that processes large datasets quickly by using in-memory caching and optimized query execution.
What are the advantages of using Dask over Spark?
Dask is more flexible and integrates seamlessly with Python, making it easier for data scientists to scale their workflows without a steep learning curve.
Are there any specific use cases where Hadoop is preferred?
Hadoop is preferred for batch processing of large datasets, especially in scenarios where data is stored in a distributed file system like HDFS.
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