| Multitenant Database Containers (MDC) | Multiple Components One System (MCOS) |
| All the tenant DB sharing the same system HANA revision | Independent HANA revision of each HANA instance |
| No additional SAP HANA License | Separate SAP HANA License |
| No additional hypervisor virtualization license and hardware independent | No additional hypervisor virtualization license and hardware independent |
| No additional machine for hardware/vm management console | No additional machine for hardware/vm management console |
| No support on storage snap shot backup | Support on storage snap shot backup |
| Shared SAP HANA Binaries - Yes | Shared SAP HANA Binaries - No |
| 1 Linux license | 1 Linux license |
| Support > 4 socket hardware | Support > 4 socket hardware |
| Support > 1TB memory | Support > 1TB memory |
| Tenant DB only can restore to tenant DB | No dependency of tenant DB backup/restore |
| Multiple BW on HANA - Yes | Multiple BW on HANA - No |
| No Performance degrade | No Performance degrade |
| No additional maintenance required for vsphere patch, Lpar patching | No additional maintenance required for vsphere patch, Lpar patching |
| Hardware Resource Management – SAP HANA internal | Hardware Resource Management - SAP HANA internal |
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...
Comments