Skip to main content

SMB vs FTP

Server Message Block(SMB), one version of which was also known as Common Internet File System (CIFS) is a protocol for sharing files, printers, serial ports, and miscellaneous communications between nodes on a network.

File Transfer Protocol (FTP) is a standard network protocol used for the transfer of computer files between a client and server on a computer network. FTP is built on a client-server model 

Though both are file sharing, application layer protocols here are few differences between them-
  • SMB is a "real" file sharing tool but it relies on a "virtual network" implementation that makes it impossible to limit it's functionality on the TCP/IP level.
  • SMB is firewall-unfriendly. It's also more or less limited to the windows platform only. (For UNIX systems Samba is available.)
  • SMB uses a LOT of short messages which makes it VERY sensible to network latency.
  • FTP’s main advantage is that since it's so OLD and UNIVERSAL,you can find the servers and clients for virtually all platforms and they communicate to one another without too much difficulty.
  • FTP can be extremely fast to transfer large documents (though it's way less efficient with small files).FTP is faster than SMB but it has less functionality.
  • FTP clients also have the option to split files into parts to do multi-part transfers which accelerate the speed even further for single file transfers, and this can be used in conjunction with multiple simultaneous file transfers.
  • FTP clients main disadvantage is that “usernames, passwords and files are sent in clear text.”
 
 

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