Decision Criteria for Memory Parameter for OLTP and Reporting environment

In transaction environment, multiple copies of block exists in Database Buffer Cache, It is advisable to have more SGA_Target / Memory_Target size in comparison to reporting environment.

Whereas in Reporting environment, multiple copies of block does not exist in Buffer Cache rather more sorting, searching operation is taking place, hence more PGA size is required.

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What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

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How are vector databases used?

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Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data...

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