Skip to main content

How to utilize Connector Groups?

Customers utilize Azure AD's Application Proxy for more and more scenarios and applications. So we've made App Proxy even more flexible by enabling more topologies.
You can create Application Proxy connector groups so that you can assign specific connectors to serve specific applications.
This capability gives you more control and ways to optimize your Application Proxy deployment.
Each Application Proxy connector is assigned to a connector group. All the connectors that belong to the same connector group act as a separate unit for high-availability and load balancing.
All connectors belong to a connector group. If you don't create groups, then all your connectors are in a default group. Your admin can create new groups and assign connectors to them in the Azure portal.

Default configuration – no use for connector groups

If you don’t use connector groups, your configuration would look like this:
AzureAD No Connector Groups
The recommended configuration for large and complex organizations is to have the default connector group as a group that doesn’t serve any applications and is used for idle or newly installed connectors. All applications are served using customized connector groups. This enables all the complexity of the scenarios described above.
In the example below, the company has two datacenters, A and B, with two connectors that serve each site. Each site has different applications that run on it.
AzureAD No Connector Groups

Comments

Popular posts from this blog

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.