Skip to main content

SAP MDC vs MCOS

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

Comments

Popular posts from this blog

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat <- colindex="colIndex," endrow="23," file="NGAP.xlsx" header="TRUE)</p" read.xlsx="" sheetindex="1," startrow="18,"> Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip&

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