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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Top Key Architecture Components in HIVE

5 architectural components present in Hadoop Hive: Shell: allows interactive queries like MySQL shell connected to a database – Also supports web and JDBC clients Driver: session handles, fetch, execute Compiler: parse, plan, optimize Execution engine: DAG of stages (M/R, HDFS, or metadata) Metastore: schema, location in HDFS, SerDe Data Mode of Hive: Tables – Typed columns (int, float, string, date, boolean) – Also, list: map (for JSON-like data) Partitions – e.g., to range-partition tables by date Buckets – Hash partitions within ranges (useful for sampling, join optimization) HIVE Meta Store Database: namespace containing a set of tables Holds table definitions (column types, physical layout) Partition data  Uses JPOX ORM for implementation; can be stored in Derby, MySQL, many other relational databases Physical Layout of HIVE Warehouse directory in HDFS – e.g., /home/hive/warehouse Tables stored in subdirectories of warehouse – Partitions, buckets

Top Hive interview Questions for quick read (1 of 2)

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The selected interview questions on HIVE. Hive is a technology being used in Hadoop eco system. 1) What are major activities in Hadoop eco system? Within the Hadoop ecosystem, HDFS can load and store massive quantities of data in an efficient and reliable manner. It can also serve that same data back up to client applications, such as MapReduce jobs, for processing and data analysis. 2)What is the role of HIVE in HADOOP Eco system? Hive, often considered the Hadoop data warehouse platform, got its start at Facebook as their analyst struggled to deal with the massive quantities of data produced by the social network. Requiring analysts to learn and write MapReduce jobs was neither productive nor practical. Stockphotos.io 3)What is Hive in Hadoop? Facebook developed a data warehouse-like layer of abstraction that would be based on tables. The tables function merely as metadata, and the table schema is projected onto the data, instead of actually moving potentially massive set