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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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Machine Learning Quick Tutorial - Part:1

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The following are the list of languages useful for Machine learning. There's no such thing as one language being "better" than another. It's a case of picking the right tool for the job. Your Resume has value if you put any one of these languages.

Python

The Python language has increased in usage because it's easy to learn and easy to read. Python has good libraries such as scikit-learn, PyML, Jython and pybrain.

R

R is an open-source statistical programming language. The syntax is not the easiest to learn, but I do encourage you to have a look at it. It also has a large number of machine learning packages and visualization tools. 

 The R-Java project allows Java programmers to access R functions from Java code.

Matlab

The Matlab language is used widely within academia for technical computing and algorithm creation. Like R, it also has a facility for plotting visualizations and graphs.

Scala

A new breed of languages is emerging that takes advantage of Java's runtime environment, which potentially increases performance, based on the threading architecture of the platform. Scala (which is an acronym for Scalable Language) is one of these, and it is being widely used by a number of startups.

There are machine learning libraries, such as ScalaNLP, but Scala can access Java jar files, and it can also implement the likes of Classifier4J and Mahout, which are covered in this book. It's also core to the Apache Spark project.

Clojure

Another JVM-based language, Clojure, is based on the Lisp programming language. It's designed for concurrency, which makes it a great candidate for machine learning applications on large sets of data.

Ruby

Many people know about the Ruby language by association with the Ruby On Rails web development framework, but it's also used as a standalone language. 

 The best way to integrate machine learning frameworks is to look at JRuby, which is a JVM-based alternative that enables you to access the Java machine learning libraries.

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