Free cloud storage 1的問題，透過圖書和論文來找解法和答案更準確安心。 我們查出實價登入價格、格局平面圖和買賣資訊

Data Analysis with Python and Pyspark

為了解決Free cloud storage 1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

Free cloud storage 1進入發燒排行的影片

基於記憶體存取事件取樣觀測及低耦合汙染源追蹤之記憶體資訊流追蹤技術

為了解決Free cloud storage 1的問題，作者鮑俊安 這樣論述：

資訊流追蹤已被發展多年，此種技術可被用來偵測目標程式之非正常行為，例如外部輸入對程式之影響、機敏資料洩漏、緩衝區覆寫攻擊等等。在過去的研究中，多數選擇使用插入特定程式碼以監控資訊流動，往往造成很大的系統負擔導致效能低落。我們提出在程式執行時期進行系統層級記憶體狀態採樣，並且非同步進行汙染追蹤模擬的方式，以達到同時滿足效能及準確度的目的。根據我們的實驗，在Nginx中只造成約1.6%的效能負擔，在單元測試中有約93%的結果與Taintgrind之結果相符。同時，我們加入變數識別化系統及資訊流視覺化系統，使實驗結果能更清楚呈現。

Google Cloud Platform in Action

為了解決Free cloud storage 1的問題，作者Geewax, J.J. 這樣論述：

SummaryGoogle Cloud Platform in Action teaches you to build and launch applications that scale, leveraging the many services on GCP to move faster than ever. You'll learn how to choose exactly the services that best suit your needs, and you'll be able to build applications that run on Google Cloud P

latform and start more quickly, suffer fewer disasters, and require less maintenance.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyThousands of developers worldwide trust Google Cloud Platform, and for good reason. Wit

h GCP, you can host your applications on the same infrastructure that powers Search, Maps, and the other Google tools you use daily. You get rock-solid reliability, an incredible array of prebuilt services, and a cost-effective, pay-only-for-what-you-use model. This book gets you started.About the B

ookGoogle Cloud Platform in Action teaches you how to deploy scalable cloud applications on GCP. Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language pr

ocessing. Along the way, you'll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes. Broad, deep, and complete, this authoritative book has everything you need.What's insideThe many varieties of cloud storage

and computingHow to make cost-effective choicesHands-on code examplesCloud-based machine learningAbout the ReaderWritten for intermediate developers. No prior cloud or GCP experience required.About the AuthorJJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API desig

n.Table of ContentsPART 1 - GETTING STARTED What is "cloud"? Trying it out: deploying WordPress on Google Cloud The cloud data center PART 2 - STORAGE Cloud SQL: managed relational storage Cloud Datastore: document storage Cloud Spanner: large-scale SQL Cloud Bigtable: large-scale structured data Cl

oud Storage: object storage PART 3 - COMPUTINGCompute Engine: virtual machines Kubernetes Engine: managed Kubernetes clusters App Engine: fully managed applications Cloud Functions: serverless applications Cloud DNS: managed DNS hosting PART 4 - MACHINE LEARNINGCloud Vision: image recognition Cloud

Natural Language: text analysis Cloud Speech: audio-to-text conversion Cloud Translation: multilanguage machine translation Cloud Machine Learning Engine: managed machine learning PART 5 - DATA PROCESSING AND ANALYTICSBigQuery: highly scalable data warehouse Cloud Dataflow: large-scale data processi

ng Cloud Pub/Sub: managed event publishing JJ Geewax is a Software Engineer at Google working specifically on Google Cloud Platform. He has been using cloud services since 2008.

具密文等值測試代理重加密之改善方案

為了解決Free cloud storage 1的問題，作者楊芝辰 這樣論述：

在網路的普及促使雲端的發展，人們開始習慣儲存資料到雲端系統上。如何有效率與其他人共享密文成為了一個問題。代理重加密（Proxy re-encryption, PRE）機制，透過委託可信第三方或是半誠實代理器將自己公鑰加密的密文轉化為可用另一方私鑰解開的密文從而實現密文共享機制。雖然該方案解決上述問題。但是，隨著資料量的急遽上升，若能利用搜尋關鍵字，直接篩選出有興趣的資料能在使用上作廣泛的應用，進而發展出具關鍵字搜索代理重加密（Proxy re-encryption with keywords search, PRES）機制，這個概念比傳統方式，透過執行「搜索-下載-解密-利用對方公鑰加密-傳

送」的步驟更有效率。但是，PRES只能搜索相同公鑰下的關鍵字。為了解決該限制，Li's 等人利用了密文相等性驗證(Public key encryption with equality test, PKEET)的性質，提出了第一篇結合了PRE以及PKEET的新機制稱為密文相等性驗證代理重加密（Proxy re-encryption with equality test, PREET），該方案提供不同公鑰下關鍵字的相等性測試，不幸的是，我們發現他們提出的架構在解密步驟的驗證過程中，該驗證方法無法有效驗證密文的完整性，因此我們提出了改進驗證的方案，使驗證有效達到密文的完整性，這將使該機制可以更廣泛

地應用於實踐。此外，本文還給出了隨機預言機模型下基於Diffie-Hellman假設的安全性證明，證明了所提出的方案能夠抵禦選定的密文攻擊。