Key Benefits of Oracle Advanced Compression

1. 2-4X reduction in storage across all environments such as production, standby, test, development, backup, etc.

2. Improved memory efficiency as data remains compressed in memory

3. Minimal or no performance impact on DML operations due to performance optimized compression technology

4. Faster queries in many cases, due to improved I/O and memory efficiency

5. Better network bandwidth utilization

6. Transparent to applications - no application changes required

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

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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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