| Types | Key Value Store | Column Store | Document Store | Graph Database |
| Performance | High | High | High | Variable |
| Scalability | High | High | Variable (High) | Variable |
| Flexibility | High | Moderate | High | High |
| Complexity | None | Low | Low | High |
| Read | Consistent Read | Read rare | Read Intensive | More Read Intensive |
| Write | Consistent Write | Write Many | Not write Intensive | Less Write Intensive |
| Others | Caching
User Session Caching Contents |
IOT Quick stream OS |
Handle lots of variety of data | Data
Type may relate each other
Vertical Scaleout Horizontal Scaleout |
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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