- We must not consider Auto Scaling for Database Tier. It may not be applicable. It needs distributed architecture ; might be suitable for certain NoSQL DBs. Needs a test case prior to take decision for Production.
- For very small EC2 instance we must not consider Auto Scaling. Rather a bit lager EC2 instance can fix the issue.
- For very frequent changes, EC2 auto scaling may not be right choice. e.g: Cricket match update. Need to plan for CDN.
Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
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