SQL WHERE in的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列各種有用的問答集和懶人包

I Am Data!

為了解決SQL WHERE in的問題，作者Qizilbash, Mustafa 這樣論述：

This book takes you to a Journey where most of the terms used in Data Field will be touch-based in a layman term. Focus of this book is not to technically train people rather its focus is to elaborate most of the terms used in the data field. There is no technical background required to read this

book, in fact this book will be bring you to a level where you can choose whether you want to get into Data Field or not. If yes, then you can choose one or more data terms in this book to pursue as a full-time career. Another aim for this book is to become a ’Quick Reference’ handbook for data fol

ks or management who can have a quick glance to any topic before jumping into a data project meeting.72 Terms covers in this bookdata warehouse, data marts, analytics, Business Intelligence, data lake, delta lake, data lakehouse, data vault, business vault, data architecture, cloud, data governance,

data dictionary, data catalog, glossary, data quality, data integrity, master data, reference data, metadata, data lineage, data observability, data pipelines, CDC, real time, data security, data privacy, data encryption, data masking, data subsetting, data scraping, web scrapping, sql, nosql, data

mesh, data mashup, data cardinality, canonical data model, the chasm trap, the fan trap, data swamp, data hub, data fabric, object storage, hadoop architecture, hdfs, hive, data sprawl, dark data, dormant data, data dividend, data assets, data citizens, data spread, data intuition, big data file fo

rmats, query optimization, index, partitioning, sharding, acid, base, devops, devsecops, dataops, mlops, data mining, data science, data algorithms, data classification, data clustering, data scrubbing, data cleansing, data cleaning, data dredging, data snooping, data wrangling, data munging, data v

isualization, data blending, data integration, data discovery, heatmap etc.

Pro Microsoft Power Bi Administration: Creating a Consistent, Compliant, and Secure Corporate Platform for Business Intelligence

為了解決SQL WHERE in的問題，作者Gunnarsson, Ásgeir,Johnson, Michael 這樣論述：

Ásgeir Gunnarsson is a data platform MVP and Chief Consultant at Datheos. He works on business intelligence solutions using the whole of the Microsoft BI stack. Ásgeir has been working in BI since 2007 both as a consultant and internal employee. Before turning to BI, he worked as a technical trainer

and he currently teaches BI courses at the Continuing Education Department of the University of Iceland. Ásgeir speaks regularly at events both domestic and internationally and is the group leader of the Icelandic PASS Group as well as the Icelandic Power BI user group. He is passionate about data

and loves solving problems with BI.Michael Johnson is a data platform MVP from Johannesburg, South Africa where he works as a business intelligence architect. Outside of work, Michael runs the local SQL Server User Group and provides Power BI presentations and training both locally and abroad.

公文辨識資料整合系統-以公司部門為例

為了解決SQL WHERE in的問題，作者周仲屏 這樣論述：

文件辨識系統適用於任何文書業務，文書工作不僅需花時間與人力資源去完成，文書業務不僅會直接影響公司整體營運亦會間接影響績效。最近有很多公司透過雲端服務開發屬於自己的文件辨識系統，如使用Google的Cloud vision、AWS的文件辨識及Azure的Computer vision。在文中應用雲端辨識服務及比較系統開發和購置的成本與時間，發現對於中小型企業而言，這樣的系統應用開發具有成本效益，將每份原本資料處理時間從30-40分鐘降至5-10分鐘，每份文件節省時間約30分鐘。在辨識檔案不壓縮的情況下，中文打字錯誤平均從每20字錯1字降至0字；數字打反或打錯機率從30%降至0%；英文打字錯誤從

每20組錯1組降至0組，辨識系統讓計算錯誤率降低，且日後如需查閱時，不再需要花費1-2工作天至倉庫尋找，只需花5-15分鐘完成確認，自行開發系統有顯著提升整體業務效率。