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Access Project Book

為了解決SQL CREATE TABLE的問題，作者Korol, Julitta 這樣論述：

This is a project book that guides you through the process of building a traditional Access desktop database that uses one Access database as the front-end (queries, reports, and forms) and another Access database to contain the tables and data. By separating the data from the rest of the database,

the Access database can be easily shared by multiple users over a network. When you build a database correctly at the outset, later this database can be migrated to another system with fewer issues and fewer objects that need to be redone. FEATURES-Understand the concepts of normalization-Build tabl

es and links to other data sources and understand table relationships-Connect and work with data stored in other formats (d104, Word, Excel, Outlook, and PowerPoint)-Retrieve data with DAO, ADO, and DLookup statements-Learn how to process text files for import and export-Create expressions, queries,

and SQL statements-Build bound and unbound forms and reports and write code to preview and print-Incorporate macros in your database-Work with attachments and image files-Learn how to display and query your Access data in the Internet browser-Secure your database for multi-user access-Compact your

database to prevent corruption resulting in data loss

SQL CREATE TABLE進入發燒排行的影片

IoTtalk AA 子系統與帳號子系統設計與實作

為了解決SQL CREATE TABLE的問題，作者張彧豪 這樣論述：

近年來，物聯網已經為我們的生活帶來極大的便利性。然而，伴隨便利性所產生的安全性問題也困擾著我們，如何在提升便利性的同時也保證其安全性是當前物聯網相關產業開發者的一大課題。對此，我們開發了Authentication and Authorization子系統 (AA 子系統)。AA子系統是一個實作在 IoTtalk 物聯網平台中的子系統。該子系統管理每一個與 IoTtalk 連線的物聯網裝置所使用的安全性資訊及權限，在其幫助下，每個物聯網裝置都會有一組連線時須使用的安全性資訊，稱之為「Connection Credential」。IoTtalk 也透過 AA 子系統為不同的 IoT devic

e 制定 permissions，稱之為「Permission Rules」。以AA子系統管理的 connection credentials 及 permission rules 並配合「Mosquitto」內建的存取控制功能，可以確保惡意的物聯網裝置不影響正常的物聯網裝置並同時保證使用中的裝置不會互相干擾。同時，我們利用「OAuth 2.0」與「OpenID Connect」開發集中式帳號子系統，提供使用者以單一帳號密碼即可存取各項以 IoTtalk 為基礎開發的物聯網服務。在本論文中，我們會描述AA子系統是如何管理物聯網裝置使用的安全性資訊、權限以及集中式帳號系統的實作細節。

Data Analysis with Python and Pyspark

為了解決SQL CREATE TABLE的問題，作者Rioux, Jonathan 這樣論述：

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales acros

s multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that t

ransform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book

teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. Purchase of the print boo

k includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep

learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the bookData Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machin

es while ingesting data from any source--whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What’s inside

Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the reader Written for data scientists and data engineers comfortable with Python. About th

e author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 Introduction PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK 2 Your first data program in PySp

ark 3 Submitting and scaling your first PySpark program 4 Analyzing tabular data with pyspark.sql 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE 6 Multidimensional data frames: Using PySpark with JSON data 7 Bilingual PySpark: Blending Python and

SQL code 8 Extending PySpark with Python: RDD and UDFs 9 Big data is just a lot of small data: Using pandas UDFs 10 Your data under a different lens: Window functions 11 Faster PySpark: Understanding Spark’s query planning PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK 12 Setting the stag

e: Preparing features for machine learning 13 Robust machine learning with ML Pipelines 14 Building custom ML transformers and estimators

以.NET平台為基礎的電商系統整合技術研究

為了解決SQL CREATE TABLE的問題，作者周柏勳 這樣論述：

隨著越來越多組織進行數位轉型，誕生出越來越多的資訊系統以完成不同產業的業務需求。若組織想擴大業務內容，因而需要進行既有系統的更新或是新系統的導入時，便會遇到需要將新、舊系統重新整合的情況，且這樣的需求將只增不減。本研究以電商系統為例，根據電商系統中的會員、購物、訂單三個要素，分析電商系統與會員系統、商品管理系統、帳務系統，三個關聯系統之間整合的需求。透過對電商系統與其關聯系統的需求分析，可以將電商系統與關聯系統的整合以資料整合角度切入，分為對兩個系統的資料庫資料同步的資料庫整合，與使用Entity Framework Core連結商品管理系統資料庫取得的API整合。本研究的系統資料庫以Mic

rosoft SQL Server為例，在資料庫整合部分，於關聯系統的資料庫的資料表設置Trigger接收資料表變動，並藉由預存程序將其轉換為XML型態後經由Service Broker服務將變動資料列資料傳送到電商系統的資料庫，再由電商系統資料庫的預存程序執行資料表操作達到非同步的處理效果，以降低對外部系統的效能影響。在API整合部分，則使用Entity Framework Core撰寫商品管理系統API，使電商系統可以排程或在加入商品到購物車、送出訂單等動作發生時，主動從商品管理系統獲取即時庫存的資料。