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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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AI Project 5 things You need to be Successful

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Suppose you have got an opportunity to create a project on AI. Try implementing these five before the start. These five are Learning, Programming Language, Knowledge representation, Problem Solving, and Hardware.

Ensure These 5 Things Done, if you want to be your AI Project Successful

1. Learning Process.

What is learning? - adding knowledge to the storehouse, and improving its performance. The success of an AI program depends on two things- the extent of wisdom it has and how frequently it acquires it. Learning agents consist of four main components. They are the:
  • The Learning element - is part of the agent responsible for improving its performance. 
  • The Performance element- is the part that chooses the actions to take. 
  • Critics, that tell the learning element of how the agent is doing. 
  • The Problem generator - suggests actions that could lead to new information experiences.

2. Programming Language.

  • LISP and Prolog are the primary languages used in AI programming.
  • LISP (List Processing): LISP is an AI programming language developed by John McCarthy in 1950. LISP is a symbolic processing language that represents information in lists and manipulates lists to derive information.
  • PROLOG (Programming in Logic): Prolog, which is developed by Alain Colmeraver and P. Roussel at Marseilles University in France in the early 1970s. 
  • Prolog uses the syntax of predicate logic to perform symbolic, logical computations.

Artificial Intelligence Project Know these Five Before Start
Artificial Intelligence Project Know these Five Before Start

3. Knowledge Representation.

The quality of the result depends on how much knowledge the system acquires. You should represent the current knowledge efficiently. Hence, knowledge representation is a vital component of the system. The best-known representations schemes are:
  • Associative Networks or Semantic Networks
  • Frames
  • Conceptual Dependencies and
  • Scripts

4. Problem Solving.

The objective of this particular area of research is how to implement the procedures on AI systems to solve problems as humans do.

The inference process should also be equally fit to obtain satisfactory results. Inference-process, you can be divided into brute and heuristic search procedures.

5. Hardware.

Most of the AI programs, implemented on Von Neumann machines. However, for AI programming, dedicated workstations have emerged - classified into one of the following four categories:
  • SISD, Single Instruction Single Data Machines
  • SIMD, Single Instruction Multiple Data Machines
  • MISD, Multiple Instruction Single Data Machines
  • MIMD, Multiple Instruction Multiple Data Machines

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