Skip to main content

What are the benefits of Azure Site Recovery

Here's what Site Recovery can do for your business:
  • Simplify your BCDR strategy—Site Recovery makes it easy to handle replication, failover and recovery of multiple business workloads and apps from a single location. Site recovery orchestrates replication and failover but doesn't intercept your application data or have any information about it.
  • Provide flexible replication—Using Site Recovery you can replicate workloads running on Hyper-V virtual machines, VMware virtual machines, and Windows/Linux physical servers.
  • Easy failover and recovery—Site Recovery provides test failovers to support disaster recovery drills without affecting production environments. You can also run planned failovers with a zero-data loss for expected outages, or unplanned failovers with minimal data loss (depending on replication frequency) for unexpected disasters. After failover you can failback to your primary sites. Site Recovery provides recovery plans that can include scripts and Azure automation workbooks so that you can customize failover and recovery of multi-tier applications.
  • Eliminate secondary datacenter—You can replicate to a secondary on-premises site, or to Azure. Using Azure as a destination for disaster recovery eliminates the cost and complexity maintaining a secondary site, and replicated data is stored in Azure Storage, with all the resilience that provides.
  • Integrate with existing BCDR technologies—Site Recovery partners with other application BCDR features. For example you can use Site Recovery to protect the SQL Server back end of corporate workloads, including native support for SQL Server AlwaysOn to manage the failover of availability groups.

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