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How to Work With Tuple in Python

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Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: Tuple example my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8

6 Exclusive Differences Between Structured and Unstructured data

Here's a basic interview question for Big data engineers. Why it's basic means many Bachelor degrees now offering courses on Big data, as a beginner, understanding of data is a little tricky. So interviewers stress this point.

Don't worry, I made it simplified. So you get a clear concept. I share here a total of six differences between these. In today's world, we have a lot of data. That data is the unstructured format.

Structured Vs Unstructured data - 6 Top Differences
 

Structured Data

  1. The major data format is text, which can be string or numeric. The date is also supported.
  2. The data model is fixed before inserting the data.
  3. Data is stored in the form of a table, making it easy to search.
  4. Not easy to scale.
  5. Version is maintained as a column in the table.
  6. Transaction management and concurrency are easy to support.

Unstructured data

  • The data format can be anything from text to images, audio to videos.
  • The data model cannot be fixed since the nature of the data can change. Consider a tweet message that could be text followed by images and audio.
  • Data is not stored in the form of a table.
  • Very easy to scale.
  • Versioning is at an entire level.
  • Transaction management and concurrency are difficult to support.

References

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