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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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How to use Pandas Series Method top ideas

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How to use Pandas Series Method top ideas

Here is an example of how to use a Series constructor in Pandas. A one-dimensional array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.) is called a Series object in pandas.

Sample DataFrame




Single dimension data


Below is the single dimension data of Index and Value.


 Index Value
 1 10           
 2 40
 3 01
 4 99

Having single value for an index is called Single dimensional data. On the other hand, when one index has multiple values, it is called multi-dimensional array.  

Below is the example for Multi-dimensional array. 

a = (1, (10,20))
mySeries = pd.Series(data, index=index)
Here, pd is a Pandas object. The data and index are two arguments. The data refers to a Python dictionary of "ndarray"  and index is index of data.

Generating DataFrame from single dimension data

The below example shows, how to construct single dimension data (Values and Index).

>>>mySeries = pd.Series([10,20,30], index=[1,2, 'a'])

Special Notes: In the above index list the 'a' represents alpha type.

Once mySeries object created, you can verify Values and Index. Do follow the steps in the screen.

series data 

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