<thead id="fflbj"><font id="fflbj"><cite id="fflbj"></cite></font></thead>
    <progress id="fflbj"><thead id="fflbj"><font id="fflbj"></font></thead></progress>

            曙海教育集團
            全國報名免費熱線:4008699035 微信:shuhaipeixun
            或15921673576(微信同號) QQ:1299983702
            首頁 課程表 在線聊 報名 講師 品牌 QQ聊 活動 就業
             
            Oracle數據挖掘技術培訓
             
               班級人數--熱線:4008699035 手機:15921673576( 微信同號)
                  增加互動環節, 保障培訓效果,堅持小班授課,每個班級的人數限3到5人,超過限定人數,安排到下一期進行學習。
               授課地點及時間
            上課地點:【上?!浚和瑵髮W(滬西)/新城金郡商務樓(11號線白銀路站) 【深圳分部】:電影大廈(地鐵一號線大劇院站)/深圳大學成教院 【北京分部】:北京中山學院/福鑫大樓 【南京分部】:金港大廈(和燕路) 【武漢分部】:佳源大廈(高新二路) 【成都分部】:領館區1號(中和大道) 【廣州分部】:廣糧大廈 【西安分部】:協同大廈 【沈陽分部】:沈陽理工大學/六宅臻品 【鄭州分部】:鄭州大學/錦華大廈 【石家莊分部】:河北科技大學/瑞景大廈
            開班時間(連續班/晚班/周末班):2020年3月16日
               課時
                 ◆資深工程師授課
                    
                    ☆注重質量 ☆邊講邊練

                    ☆若學員成績達到合格及以上水平,將獲得免費推薦工作的機會
                    ★查看實驗設備詳情,請點擊此處★
               質量以及保障

                  ☆ 1、如有部分內容理解不透或消化不好,可免費在以后培訓班中重聽;
                  ☆ 2、在課程結束之后,授課老師會留給學員手機和E-mail,免費提供半年的課程技術支持,以便保證培訓后的繼續消化;
                  ☆3、合格的學員可享受免費推薦就業機會。
                  ☆4、合格學員免費頒發相關工程師等資格證書,提升您的職業資質。

            課程大綱
             

            Introduction
            Course Objectives
            Course Schedule
            Course Pre-requisites and Suggested Pre-requisites
            The sh and dm Sample Schemas and Appendices Used in the Course
            Class Account Information
            SQL Environments and Data Warehousing Tools Used in this Course
            Oracle 11g Data Warehousing and SQL Documentation and Oracle By Examples
            Continuing Your Education: Recommended Follow-Up Classes
            Data Warehousing, Business Intelligence, OLAP, and Data Mining
            Data Warehouse Definition and Properties
            Data Warehouses, Business Intelligence, Data Marts, and OLTP
            Typical Data Warehouse Components
            Warehouse Development Approaches
            Extraction, Transformation, and Loading (ETL)
            The Dimensional Model and Oracle OLAP
            Oracle Data Mining
            Defining Data Warehouse Concepts and Terminology
            Data Warehouse Definition and Properties
            Data Warehouse Versus OLTP
            Data Warehouses Versus Data Marts
            Typical Data Warehouse Components
            Warehouse Development Approaches
            Data Warehousing Process Components
            Strategy Phase Deliverables
            Introducing the Case Study: Roy Independent School District (RISD)
            Business, Logical, Dimensional, and Physical Modeling
            Data Warehouse Modeling Issues
            Defining the Business Model
            Defining the Logical Model
            Defining the Dimensional Model
            Defining the Physical Model: Star, Snowflake, and Third Normal Form
            Fact and Dimension Tables Characteristics
            Translating Business Dimensions into Dimension Tables
            Translating Dimensional Model to Physical Model
            Database Sizing, Storage, Performance, and Security Considerations
            Database Sizing and Estimating and Validating the Database Size
            Oracle Database Architectural Advantages
            Data Partitioning
            Indexing
            Optimizing Star Queries: Tuning Star Queries
            Parallelism
            Security in Data Warehouses
            Oracle’s Strategy for Data Warehouse Security
            The ETL Process: Extracting Data
            Extraction, Transformation, and Loading (ETL) Process
            ETL: Tasks, Importance, and Cost
            Extracting Data and Examining Data Sources
            Mapping Data
            Logical and Physical Extraction Methods
            Extraction Techniques and Maintaining Extraction Metadata
            Possible ETL Failures and Maintaining ETL Quality
            Oracle’s ETL Tools: Oracle Warehouse Builder, SQL*Loader, and Data Pump
            The ETL Process: Transforming Data
            Transformation
            Remote and Onsite Staging Models
            Data Anomalies
            Transformation Routines
            Transforming Data: Problems and Solutions
            Quality Data: Importance and Benefits
            Transformation Techniques and Tools
            Maintaining Transformation Metadata
            The ETL Process: Loading Data
            Loading Data into the Warehouse
            Transportation Using Flat Files, Distributed Systems, and Transportable Tablespaces
            Data Refresh Models: Extract Processing Environment
            Building the Loading Process
            Data Granularity
            Loading Techniques Provided by Oracle
            Postprocessing of Loaded Data
            Indexing and Sorting Data and Verifying Data Integrity
            Refreshing the Warehouse Data
            Developing a Refresh Strategy for Capturing Changed Data
            User Requirements and Assistance
            Load Window Requirements
            Planning and Scheduling the Load Window
            Capturing Changed Data for Refresh
            Time- and Date-Stamping, Database triggers, and Database Logs
            Applying the Changes to Data
            Final Tasks
            Materialized Views
            Using Summaries to Improve Performance
            Using Materialized Views for Summary Management
            Types of Materialized Views
            Build Modes and Refresh Modes
            Query Rewrite: Overview
            Cost-Based Query Rewrite Process
            Working With Dimensions and Hierarchies
            Leaving a Metadata Trail
            Defining Warehouse Metadata
            Metadata Users and Types
            Examining Metadata: ETL Metadata
            Extraction, Transformation, and Loading Metadata
            Defining Metadata Goals and Intended Usage
            Identifying Target Metadata Users and Choosing Metadata Tools and Techniques
            Integrating Multiple Sets of Metadata
            Managing Changes to Metadata
            Data Warehouse Implementation Considerations
            Project Management
            Requirements Specification or Definition
            Logical, Dimensional, and Physical Data Models
            Data Warehouse Architecture
            ETL, Reporting, and Security Considerations
            Metadata Management
            Testing the Implementation and Post Implementation Change Management
            Some Useful Resources and White Papers

             
             
              備案號:備案號:滬ICP備08026168號-1 .(2024年07月24日)....................
            友情鏈接:Cadence培訓 ICEPAK培訓 PCB設計培訓 adams培訓 fluent培訓系列課程 培訓機構課程短期培訓系列課程培訓機構 長期課程列表實踐課程高級課程學校培訓機構周末班培訓 南京 NS3培訓 OpenGL培訓 FPGA培訓 PCIE培訓 MTK培訓 Cortex訓 Arduino培訓 單片機培訓 EMC培訓 信號完整性培訓 電源設計培訓 電機控制培訓 LabVIEW培訓 OPENCV培訓 集成電路培訓 UVM驗證培訓 VxWorks培訓 CST培訓 PLC培訓 Python培訓 ANSYS培訓 VB語言培訓 HFSS培訓 SAS培訓 Ansys培訓 短期培訓系列課程培訓機構 長期課程列表實踐課程高級課程學校培訓機構周末班 端海 教育 企業 學院 培訓課程 系列班 長期課程列表實踐課程高級課程學校培訓機構周末班 短期培訓系列課程培訓機構 端海教育企業學院培訓課程 系列班
            538在线视频二三区视视频