Featured Post

The Quick and Easy Way to Analyze Numpy Arrays

Image
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

Apache HIVE Top Features

Apache Hive aids the examination of great datasets kept in Hadoop’s HDFS and harmonious file setups such as the Amazon S3 filesystem.


Apache HIVE Top Features


It delivers an SQL-like lingo named when keeping complete aid aimed at map/reduce. To accelerate requests, it delivers guides, containing bitmap guides.

By preset, Hive stores metadata in an implanted Apache Derby database, and different client/server databases like MySQL may optionally be applied.

Currently, there are 4 file setups maintained in Hive, which are TEXTFILE, SEQUENCE FILE, ORC, and RCFILE.

Other attributes of Hive include:
  • Indexing to supply quickening, directory sort containing compacting, and Bitmap directory as of 0.10, further directory kinds are designed.
  • Different depository kinds such as simple written material, RCFile, HBase, ORC, and other ones.
  • Metadata depository in an RDBMS, notably decreasing the time to accomplish verbal examines throughout request implementation.
  • Operating on compressed information kept into the Hadoop environment, set of rules containing gzip, bzip2, snappy, etcetera.
  • Built-in exploiter described purposes (UDFs) to manipulate dates, cords, and different data-mining implements. Hive aids expanding the UDF set to cover use-cases not maintained by integrated purposes.
  • SQL-like requests (Hive QL), that are completely changed into map-reduce appointments.

Comments

Popular posts from this blog

How to Decode TLV Quickly

7 AWS Interview Questions asked in Infosys, TCS