Scalability & Performance - SQL Server Enterprise Edition and Standard Edition

Feature	Enterprise	Standard
Columnstore ¹	Yes	Yes ²
In-Memory OLTP ¹	Yes	Yes ²
Stretch Database	Yes	Yes
Persistent Main Memory	Yes	Yes
Multi-instance support	50	50
Table and index partitioning	Yes	Yes ²
Data compression	Yes	Yes ²
Resource Governor	Yes	No
Partitioned Table Parallelism	Yes	No
Multiple Filestream containers	Yes	Yes ²
NUMA Aware and Large Page Memory and Buffer Array Allocation	Yes	No
Buffer Pool Extension	Yes	Yes
IO Resource Governance	Yes	No
Delayed Durability	Yes	Yes

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What is Tensor Parallelism and relationship between Buffer and GPU

Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU . It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...

Data Wrangling vs EDA

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...

What's replicated, what's not?

Logged operations are replicated. These include, but are not limited to: DDL DML Create/alter table space Create/alter storage group Create/alter buffer pool XML data. Logged LOBs Not logged operations are not replicated. These include, but are not limited to: Database configuration parameters (this allows primary and standby databases to be configured differently). "Not logged initially" tables Not logged LOBs UDF (User Defined Function) libraries. UDF DDL is replicated. But the libraries used by UDF (such as C or Java libraries) are not replicated, because they are not stored in the database. Users must manually copy the libraries to the standby. Note: You can use database configuration parameter BLOCKNONLOGGED to block not logged operations on the primary.

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