Skip to main content

What is IDOC?


IDOC = Intermediate Document.
ALE = Application Link Enabling
EDI = Electronic Document interchange

IDoc is an SAP object that carries data of a business transaction from one system to another in the form of electronic message.

The purpose of an IDoc is to transfer data or information from SAP to other systems and vice versa. 

The transfer from SAP to non-SAP system is done via EDI (Electronic Data Interchange) subsystems whereas for transfer between two SAP systems, ALE is used.

IDoc is an SAP object that carries data of a business transaction from one system to another in the form of electronic message. IDoc is an acronym forIntermediate Document.
The purpose of an IDoc is to transfer data or information from SAP to other systems and vice versa.  The transfer from SAP to non-SAP system is done via EDI (Electronic Data Interchange) subsystems whereas for transfer between two SAP systems, ALE is used.
IDoc can be triggered in SAP system or in EDI subsystem. This depends on the direction in which IDoc is sent and is called as Inbound IDoc and Outbound IDoc accordingly.
In case of outbound flow, IDoc is triggered in SAP through document message control which is then sent to EDI subsystem. EDI converts the data from IDoc into XML or equivalent format and then sends the data to partner system through Internet.
For inbound flow, EDI converts partner data and IDoc is created in SAP. After successful processing of this IDoc, Application Document is posted in SAP.


 
Labels:

Comments

Post a Comment

Popular posts from this blog

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

Got this during the execution of following command in R > dat 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' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...
Post a Comment
Read more

Training LLM model requires more GPU RAM than storing same LLM

Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more