blocks|key|4107246|text|每当分析数据并将速度作为标准时，有两个关键组件决定速度:您拥有的数据量和数据所在的位置。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4107247|如果你有Big+Data，考虑使用Hadoop或Spark来分析它。这将使它变得更快，并且您将不依赖于加载时间。如果你有几个千兆字节的数据，最好使用python，但它仍然可能减慢你的机器。|offset|length|4107248|现在，为了解决数据所在的位置，如果您的数据在本地，那么python是最好的方法。如果您的数据位于云服务器中，那么Azure、GCP甚至AWS都可以使用大数据工具来简化这种数据探索。这三个云系统都有可用的大数据工具。|4107249|因此，就速度而言，它实际上取决于这两个限制。如果您有大数据，并且数据位于云系统中。考虑使用Hadoop来分析数据。如果您只有几to的数据和内部部署，可以使用python来分析您的数据。|4107250|entityMap|0|LINK|mutability|MUTABLE|url|https://www.sas.com/en_us/insights/big-data/what-is-big-data.html|1|https://azure.microsoft.com/en-us/solutions/big-data/|2|https://cloud.google.com/solutions/big-data/|3|https://aws.amazon.com/big-data/^0|0|4|8|0|0|1K|5|1|1Q|3|2|1V|3|3|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Y|8|@]|9|@$D|Z|E|10|1|11]]|A|$]]|$1|F|3|G|5|6|7|12|8|@]|9|@$D|13|E|14|1|15]|$D|16|E|17|1|18]|$D|19|E|1A|1|1B]]|A|$]]|$1|H|3|I|5|6|7|1C|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]|T|$5|M|N|O|A|$P|U]]|V|$5|M|N|O|A|$P|W]]]]

Whenever analyzing data and considering speed as a criteria, two key components determine the speed: The amount of data you have and where the data is located.

If you have <a href="https://www.sas.com/en_us/insights/big-data/what-is-big-data.html" rel="nofollow noreferrer">Big Data</a>, consider using Hadoop or Spark to analyze it. This will make it much faster and you will not be dependent of load time. If you have a few gigabytes of data it maybe best to use python but it still may slow down your machine. 

Now to address where the data is, if you have your data on premise then python is the best approach. If your data is located in cloud server, then <a href="https://azure.microsoft.com/en-us/solutions/big-data/" rel="nofollow noreferrer">Azure</a>, <a href="https://cloud.google.com/solutions/big-data/" rel="nofollow noreferrer">GCP</a>, or even <a href="https://aws.amazon.com/big-data/" rel="nofollow noreferrer">AWS</a> have big data tools available to make this data exploration easier. All three cloud systems have big data tools available for use. 

So in terms of speed, it really depends on the two constraints. If you have Big Data and your data is located in a cloud system. Consider using Hadoop to analyze your data. If you have only a few gigabytes of data and on-premise, use python to analyze your data.

blocks|key|4107277|text|所以Hadoop主要使用spark。如果你用来分析或处理数据的底层框架包含spark，你可以使用Scala，PySpark或者R。单独使用python不会给你带来Spark的好处，Spark可以让数据分析更快，也可以对大数据进行各种转换。所以无论你使用哪一个，都是关于使用spark的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4107278|Scala或PySpark+:两者都包含几乎所有这些特性。|4107279|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

So Hadoop mostly uses spark. If the underlying framework you are using to analyse or crunch your data contains spark, you are good to go with either Scala, PySpark or maybe R. Using alone python won't give you benefits of Spark which makes data analysis faster and also various transformations on Big Data. So whichever you use, its about using spark.

Scala or PySpark : both contains almost all of these features.

Which technology is efficient to analyze the data hadoop or python? and which technology is speed between the above two?

Data analytics using python or hadoop?

云服务器

数据分析

大数据

服务器

Hadoop 

Spark 

哪种技术可以有效地分析数据hadoop或python？速度介于上述两者之间的是哪种技术？

问使用python或hadoop进行数据分析？
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