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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 Convert Dictionary to Dataframe: Pandas from_dict

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 Pandas is a data analysis Python library.  The example shows you to convert a dictionary to a data frame. The point to note here is DataFrame will take only 2D data. So you need to supply 2D data. 


Dictionary to Data frame example


Pandas Dictionary to Dataframe


import pandas as pd

import numpy as np

data_dict = {'item1' : np.random.randn(4), 'item2' : np.random.randn(4)}

df3=pd.DataFrame.from_dict(data_dict, orient='index')

print(df3)


Output


0 1 2 3 item1 -0.109300 -0.483624 0.375838 1.248651 item2 -0.274944 -0.857318 -1.203718 -0.061941


Explanation

Using the NumPy package, created a dictionary with random values. There are two items - item 1 and item 2. The data_dict is input to the data frame. The from_dict method needs two parameters. These are data_dict and index. Here's the syntax you can refer to quickly.


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