| Feature | Enterprise | Standard |
| User instances | No | No |
| LocalDB | No | No |
| Dedicated admin connection | Yes | Yes |
| PowerShell scripting support | Yes | Yes |
| SysPrep support 1 | Yes | Yes |
| Support for data-tier application component operations - extract, deploy, upgrade, delete | Yes | Yes |
| Policy automation (check on schedule and change) | Yes | Yes |
| Performance data collector | Yes | Yes |
| Able to enroll as a managed instance in multi-instance management | Yes | Yes |
| Standard performance reports | Yes | Yes |
| Plan guides and plan freezing for plan guides | Yes | Yes |
| Direct query of indexed views (using NOEXPAND hint) | Yes | Yes |
| Automatic indexed views maintenance | Yes | Yes |
| Distributed partitioned views | Yes | No |
| Parallel indexed operations | Yes | No |
| Automatic use of indexed view by query optimizer | Yes | No |
| Parallel consistency check | Yes | No |
| SQL Server Utility Control Point | Yes | No |
| Buffer pool extension | Yes | Yes |
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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