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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Understand Pickling and Unpickling in Python

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Here are the Python pickling and unpickling best examples and the differences between these two.


pickling and unpickling in python




These you can use to serialize and deserialize the python data structures. The concept of writing the total state of an object to the file is called pickling, and to read a Total Object from the file is called unpickling.


Pickle and Unpickle

The process of writing the state of an object to the file (converting a class object into a byte stream) and storing it in the file is called pickling. It is also called object serialization.

The process of reading the state of an object from the file ( converting a byte stream back into a class object) is called unpickling. It is an inverse operation of pickling. It is also called object deserialization. The pickling and unpickling can implement by using a pickling module since binary files support byte streams. Pickling and unpickling should be possible using binary files.


Data types you can pickle

  1. Integers
  2. Booleans
  3. Complex numbers
  4. Floats
  5. Normal and Unicode strings
  6. Tuple
  7. List
  8. Set and dictionaries which contains pickling objects
  9. Classes and built-in functions can define at the top level of a module.

Functions you need


dump()


The above function performs pickling. It returns the pickled representation of an object as a byte object instead of writing it to the file. It is called to serialize an object hierarchy.


Syntax:


import pickle

pickle.dump(object, file, protocol)


where

the object is a python object to serialize

a file is a file object in which the serialized python object will be stored


protocol if not specified is 0. If specified as HIGHEST PROTOCOL or negative, then the highest protocol version available will be used. 



load()


The above function performs unpickling. It reads a pickled object from a binary file and returns it as an object. It is used to deserialize a data stream.


Syntax:


import pickle

pickle.load(file)



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