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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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Python Abstract Classes to Learn Quickly

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Here's the best example of how to understand the abstract classes of python quickly. An abstract class means it is a skeleton. You cannot instantiate abstract classes. For Example: The abstract class name is 'dog' o = dog() It gives an error. Since the ' dog' is an abstract class. I am sharing with you a clear understanding of abstract class. So that you can tell confidently about these classes. The syntax for Abstract Class from abc , import ABC , abstractmethod  class <class name>(ABC): @abstractmethod  def <method name>():  #abstract class definition Python Abstract Class Explanation from abc import ABC, abstractmethod class AbsBaseClass(ABC): def __init__(self): print("Abstract class") @abstractmethod def abMeth(self): pass def conMeth(self): print("I'm a concrete method") class Derived(AbsBaseClass): def abMeth(self): print("I'm redefined") o=Derived() o.abMeth() o.conMeth() References Star Python Certification
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