Home Database Mysql Tutorial IntegrationServices架构概述

IntegrationServices架构概述

Jun 07, 2016 pm 03:22 PM
Architecture Overview

Integration Services平台包括许多组件,但在最高层次上,它由4个主要部分组成。 1、Integration Services运行时。SSIS运行时提供了运行SSIS包所需的核心功能,包括执行、记录、配置、调试等。 2、数据流引擎。SSIS数据库引擎(也成为管道)提供了将数据从源

Integration Services平台包括许多组件,但在最高层次上,它由4个主要部分组成。

1、Integration Services运行时。SSIS运行时提供了运行SSIS包所需的核心功能,包括执行、记录、配置、调试等。

2、数据流引擎。SSIS数据库引擎(也成为管道)提供了将数据从源移动到SSIS包中的目标所需的核心ETL功能,包括管理管道所基于的内存缓冲区,以及组成包的数据流逻辑的源、转换盒目标。

3、Integration Services对象模型。SSIS对象模型是一个托管.net应用程序编程接口(API),支持工具、使用工具和组件与SSIS运行时和数据流引擎交互。

4、Integration Services服务。SSIS服务是一种 Windows服务,提供了存储和管理SSIS包的功能。

这4个关键组件构成了SSIS的基础,但实际上它们只是SSIS架构的冰山一角。当然,主要的工作单元是SSIS包。

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Comparative analysis of deep learning architectures Comparative analysis of deep learning architectures May 17, 2023 pm 04:34 PM

The concept of deep learning originates from the research of artificial neural networks. A multi-layer perceptron containing multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representations to represent categories or characteristics of data. It is able to discover distributed feature representations of data. Deep learning is a type of machine learning, and machine learning is the only way to achieve artificial intelligence. So, what are the differences between various deep learning system architectures? 1. Fully Connected Network (FCN) A fully connected network (FCN) consists of a series of fully connected layers, with every neuron in each layer connected to every neuron in another layer. Its main advantage is that it is "structure agnostic", i.e. no special assumptions about the input are required. Although this structural agnostic makes the complete

This 'mistake' is not really a mistake: start with four classic papers to understand what is 'wrong' with the Transformer architecture diagram This 'mistake' is not really a mistake: start with four classic papers to understand what is 'wrong' with the Transformer architecture diagram Jun 14, 2023 pm 01:43 PM

Some time ago, a tweet pointing out the inconsistency between the Transformer architecture diagram and the code in the Google Brain team's paper "AttentionIsAllYouNeed" triggered a lot of discussion. Some people think that Sebastian's discovery was an unintentional mistake, but it is also surprising. After all, considering the popularity of the Transformer paper, this inconsistency should have been mentioned a thousand times. Sebastian Raschka said in response to netizen comments that the "most original" code was indeed consistent with the architecture diagram, but the code version submitted in 2017 was modified, but the architecture diagram was not updated at the same time. This is also the root cause of "inconsistent" discussions.

Multi-path, multi-domain, all-inclusive! Google AI releases multi-domain learning general model MDL Multi-path, multi-domain, all-inclusive! Google AI releases multi-domain learning general model MDL May 28, 2023 pm 02:12 PM

Deep learning models for vision tasks (such as image classification) are usually trained end-to-end with data from a single visual domain (such as natural images or computer-generated images). Generally, an application that completes vision tasks for multiple domains needs to build multiple models for each separate domain and train them independently. Data is not shared between different domains. During inference, each model will handle a specific domain. input data. Even if they are oriented to different fields, some features of the early layers between these models are similar, so joint training of these models is more efficient. This reduces latency and power consumption, and reduces the memory cost of storing each model parameter. This approach is called multi-domain learning (MDL). In addition, MDL models can also outperform single

Ten elements of machine learning system architecture Ten elements of machine learning system architecture Apr 13, 2023 pm 11:37 PM

This is an era of AI empowerment, and machine learning is an important technical means to achieve AI. So, is there a universal machine learning system architecture? Within the cognitive scope of experienced programmers, Anything is nothing, especially for system architecture. However, it is possible to build a scalable and reliable machine learning system architecture if applicable to most machine learning driven systems or use cases. From a machine learning life cycle perspective, this so-called universal architecture covers key machine learning stages, from developing machine learning models, to deploying training systems and service systems to production environments. We can try to describe such a machine learning system architecture from the dimensions of 10 elements. 1.

1.3ms takes 1.3ms! Tsinghua's latest open source mobile neural network architecture RepViT 1.3ms takes 1.3ms! Tsinghua's latest open source mobile neural network architecture RepViT Mar 11, 2024 pm 12:07 PM

Paper address: https://arxiv.org/abs/2307.09283 Code address: https://github.com/THU-MIG/RepViTRepViT performs well in the mobile ViT architecture and shows significant advantages. Next, we explore the contributions of this study. It is mentioned in the article that lightweight ViTs generally perform better than lightweight CNNs on visual tasks, mainly due to their multi-head self-attention module (MSHA) that allows the model to learn global representations. However, the architectural differences between lightweight ViTs and lightweight CNNs have not been fully studied. In this study, the authors integrated lightweight ViTs into the effective

What is the architecture and working principle of Spring Data JPA? What is the architecture and working principle of Spring Data JPA? Apr 17, 2024 pm 02:48 PM

SpringDataJPA is based on the JPA architecture and interacts with the database through mapping, ORM and transaction management. Its repository provides CRUD operations, and derived queries simplify database access. Additionally, it uses lazy loading to only retrieve data when necessary, thus improving performance.

Software architecture design and software and hardware decoupling methodology in SOA Software architecture design and software and hardware decoupling methodology in SOA Apr 08, 2023 pm 11:21 PM

For the next generation of centralized electronic and electrical architecture, the use of central+zonal central computing unit and regional controller layout has become a must-have option for various OEMs or tier1 players. Regarding the architecture of the central computing unit, there are three ways: separation SOC, hardware isolation, software virtualization. The centralized central computing unit will integrate the core business functions of the three major domains of autonomous driving, smart cockpit and vehicle control. The standardized regional controller has three main responsibilities: power distribution, data services, and regional gateway. Therefore, the central computing unit will integrate a high-throughput Ethernet switch. As the degree of integration of the entire vehicle becomes higher and higher, more and more ECU functions will be slowly absorbed into the regional controller. And platformization

How steep is the learning curve of golang framework architecture? How steep is the learning curve of golang framework architecture? Jun 05, 2024 pm 06:59 PM

The learning curve of the Go framework architecture depends on familiarity with the Go language and back-end development and the complexity of the chosen framework: a good understanding of the basics of the Go language. It helps to have backend development experience. Frameworks that differ in complexity lead to differences in learning curves.

See all articles