MS/20767B : Implementing a SQL Data Warehouse
5 gün (30 Saat) İleri Seviye Sınıf / Online İş Zekası ve İleri Analitik
Microsoft SQL Server ürün ailesi ile uçtan uca büyük ölçekli kurumsal iş zekası projelerinizi yürütebilirsiniz. İş zekası projelerinin belkemiğini oluşturan kaynakarın keşfi, veri ambarı tasarımı, veri taşıma ve veri kalitesini arttırma konusunda teknik ve teknolojilere odaklanıyoruz. "Implementing a SQL Data Warehouse" eğitimi genel olarak SQL Server, SQL Server Integration Service, Data Quality Services, Master Data Services, ColumnStore Indexs ve tasarım prensiplerini içermektedir. Dilerseniz bu eğitimden sonra Microsoft'un 70-767 sınavına giriş yapabilir, İş Zekası Uzmanı olma yolunda büyük bir adım atabilirsiniz.
Eğitim İçeriği
Module 1: Introduction to Data Warehousing
- Lesson
- Overview of Data Warehousing
- Considerations for a Data Warehouse Solution
- Lab : Exploring a Data Warehouse Solution
- Exploring data sources
- Exploring an ETL process
- Exploring a data warehouse
Module 2: Planning Data Warehouse Infrastructure
- Lesson
- Considerations for data warehouse infrastructure.
- Planning data warehouse hardware.
- Lab : Planning Data Warehouse Infrastructure
- Planning data warehouse hardware
Module 3: Designing and Implementing a Data Warehouse
- Lesson
- Data warehouse design overview
- Designing dimension tables
- Designing fact tables
- Physical Design for a Data Warehouse
- Lab : Implementing a Data Warehouse Schema
- Implementing a star schema
- Implementing a snowflake schema
- Implementing a time dimension table
Module 4: Columnstore Indexes
- Lesson
- Introduction to Columnstore Indexes
- Creating Columnstore Indexes
- Working with Columnstore Indexes
- Lab : Using Columnstore Indexes
- Create a Columnstore index on the FactProductInventory table
- Create a Columnstore index on the FactInternetSales table
- Create a memory optimized Columnstore table
Module 5: Implementing an Azure SQL Data Warehouse
- Lesson
- Advantages of Azure SQL Data Warehouse
- Implementing an Azure SQL Data Warehouse
- Developing an Azure SQL Data Warehouse
- Migrating to an Azure SQ Data Warehouse
- Copying data with the Azure data factory
- Lab : Implementing an Azure SQL Data Warehouse
- Create an Azure SQL data warehouse database
- Migrate to an Azure SQL Data warehouse database
- Copy data with the Azure data factory
Module 6: Creating an ETL Solution
- Lesson
- Introduction to ETL with SSIS
- Exploring Source Data
- Implementing Data Flow
- Lab : Implementing Data Flow in an SSIS Package
- Exploring source data
- Transferring data by using a data row task
- Using transformation components in a data row
Module 7: Implementing Control Flow in an SSIS Package
- Lesson
- Introduction to Control Flow
- Creating Dynamic Packages
- Using Containers
- Managing consistency.
- Lab : Implementing Control Flow in an SSIS Package
- Using tasks and precedence in a control flow
- Using variables and parameters
- Using containers
- Lab : Using Transactions and Checkpoints
- Using transactions
- Using checkpoints
Module 8: Debugging and Troubleshooting SSIS Packages
- Lesson
- Debugging an SSIS Package
- Logging SSIS Package Events
- Handling Errors in an SSIS Package
- Lab : Debugging and Troubleshooting an SSIS Package
- Debugging an SSIS package
- Logging SSIS package execution
- Implementing an event handler
- Handling errors in data flow
Module 9: Implementing a Data Extraction Solution
- Lesson
- Introduction to Incremental ETL
- Extracting Modified Data
- Loading modified data
- Temporal Tables
- Lab : Extracting Modified Data
- Using a datetime column to incrementally extract data
- Using change data capture
- Using the CDC control task
- Using change tracking
- Lab : Loading a data warehouse
- Loading data from CDC output tables
- Using a lookup transformation to insert or update dimension data
- Implementing a slowly changing dimension
- Using the merge statement
Module 10: Enforcing Data Quality
- Lesson
- Introduction to Data Quality
- Using Data Quality Services to Cleanse Data
- Using Data Quality Services to Match Data
- Lab : Cleansing Data
- Creating a DQS knowledge base
- Using a DQS project to cleanse data
- Using DQS in an SSIS package
- Lab : De-duplicating Data
- Creating a matching policy
- Using a DS project to match data
Module 11: Using Master Data Services
- Lesson
- Introduction to Master Data Services
- Implementing a Master Data Services Model
- Hierarchies and collections
- Creating a Master Data Hub
- Lab : Implementing Master Data Services
- Creating a master data services model
- Using the master data services add-in for Excel
- Enforcing business rules
- Loading data into a model
- Consuming master data services data
Module 12: Extending SQL Server Integration Services (SSIS)
- Lesson
- Using scripting in SSIS
- Using custom components in SSIS
- Lab : Using scripts
- Using a script task
Module 13: Deploying and Configuring SSIS Packages
- Lesson
- Overview of SSIS Deployment
- Deploying SSIS Projects
- Planning SSIS Package Execution
- Lab : Deploying and Configuring SSIS Packages
- Creating an SSIS catalog
- Deploying an SSIS project
- Creating environments for an SSIS solution
- Running an SSIS package in SQL server management studio
- Scheduling SSIS packages with SQL server agent
Module 14: Consuming Data in a Data Warehouse
- Lesson
- Introduction to Business Intelligence
- An Introduction to Data Analysis
- Introduction to reporting
- Analyzing Data with Azure SQL Data Warehouse
- Lab : Using a data warehouse
- Exploring a reporting services report
- Exploring a PowerPivot workbook
- Exploring a power view report
Öncesinde Önerilenler
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Data Engineer
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-
BI Professional
Veri kaynaklarının Keşfi, Veri Kalitesini Arttırma, ETL, Veriambarı Tasarım Prensipleri, Veri Modelleme ve Raporlama
Sonrasında Önerilenler
-
Data Engineer
Büyük Verinin İşlenmesi, Yönetimi, Veri Kalitesini Arttırma, Bulut Bilişim ve Veri Bilimi için Kodlama, Spark ve Hadoop gibi Dağıtık Mimariler ile Çalışma.
-
BI Professional
Veri kaynaklarının Keşfi, Veri Kalitesini Arttırma, ETL, Veriambarı Tasarım Prensipleri, Veri Modelleme ve Raporlama