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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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Why Learning Python is so useful?

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Why Learning Python is so useful?

I have recently started learning Python. During my learning time, my friends have asked since you are interested in analytics why you need to learn Python. I explained the below reasons. This is one of the powerful languages after Java.

Python is similar to many programming languages that people generally know about: 

Python is very similar to JavaScript, Ruby, and PHP in many respects. 

Most programmers have a working knowledge of these programming languages and this makes it easier for programmers to learn Python. The basic features of these languages such as the use of arrays, anonymous functions, etc., are also present in Python. 

 
1. Python Machine Learning Libraries:

The variety of machine learning libraries that are available in Python is large.

One can choose between Scikitlearn, Keras, Theano, and Tensorflow. Many neural network libraries such as Keras, Theano, etc., are exclusively available in Python. So, if you want to do cutting edge machine learning work, you must know Python.

 
2. Python Handles Text Data: 

Unlike statistical software environments such as R, Python excels at handling text data. People who know Python can easily mine text corpus for useful insights. 


Python also provides support for Natural Language Processing through NLTK and sPacy
Python makes distributed computing very easy: Apache Spark has a Python API called PySpark. Using this piece of software, one can easily do distributed computing. PySpark has in recent times become the de-facto API for Spark. 


Extensive support for different data sources: It doesn’t matter if one needs to fetch data from an SQL server, a MongoDB database, or JSON data from some web API; Python can easily support all these data sources with a very clean and elegant syntax. 

3. Benefits of Learning Python

  • Learning Python has many advantages – it gives a user many skills, one can fetch data from different sources, create machine learning models, and do distributed computing seamlessly. 

  • For any programmer, learning Python will not be a difficult task. One can reap a lot of benefits by devoting time to learning Python.

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