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ETL with Azure Cookbook
ETLisoneofthemostcommonandtediousproceduresformovingandprocessingdatafromonedatabasetoanother.Withthehelpofthisbook,youwillbeabletospeeduptheprocessbydesigningeffectiveETLsolutionsusingtheAzureservicesavailableforhandlingandtransforminganydatatosuityourrequirements.Withthiscookbook,you’llbecomewellversedinallthefeaturesofSQLServerIntegrationServices(SSIS)toperformdatamigrationandETLtasksthatintegratewithAzure.You’lllearnhowtotransformdatainAzureandunderstandhowlegacysystemsperformETLon-premisesusingSSIS.LaterchapterswillgetyouuptospeedwithconnectingandretrievingdatafromSQLServer2019BigDataClusters,andevenshowyouhowtoextendandcustomizetheSSIStoolboxusingcustom-developedtasksandtransforms.ThisETLbookalsocontainspracticalrecipesformovingandtransformingdatawithAzureservices,suchasDataFactoryandAzureDatabricks,andletsyouexplorevariousoptionsformigratingSSISpackagestoAzure.Towardtheend,you’llfindouthowtoprofiledatainthecloudandautomateservicecreationwithBusinessIntelligenceMarkupLanguage(BIML).Bytheendofthisbook,you’llhavedevelopedtheskillsyouneedtocreateandautomateETLsolutionson-premisesaswellasinAzure.
目錄(69章)
倒序
- 封面
- 版權信息
- Why subscribe?
- Preface
- Chapter 1: Getting Started with Azure and SSIS 2019
- Technical requirements
- Installing Microsoft SQL Server 2019 Integration Services
- Installing SQL Server Management Studio
- Installing SQL Server Data Tools
- Installing Azure Data Studio
- Creating an Azure subscription
- Chapter 2: Introducing ETL
- Creating a SQL Azure database
- Connecting SQL Management Studio to Azure SQL
- Creating a simple ETL package
- Loading data before its transformation
- Chapter 3: Creating and Using SQL Server 2019 Big Data Clusters
- Technical requirements
- Creating a SQL Server 2019 Big Data Cluster
- Stopping and starting Azure resources
- Setting up a Big Data Cluster
- Loading data into a Big Data Cluster
- Extracting data from a Big Data Cluster
- Chapter 4: Azure Data Integration
- Setting up the Azure Feature Pack in SSIS
- Setting up a Java Runtime Environment
- Setting up an Azure storage account
- Creating an on-demand Azure HDInsight cluster
- Transforming data with Hive
- Transforming data with Pig
- Chapter 5: Extending SSIS with Custom Tasks and Transformations
- Technical requirements
- Designing a Custom Control Flow Task
- Designing a Custom Data Flow Component
- Deploying SSIS customizations
- Upgrading a Custom Data Flow Component
- Chapter 6: Azure Data Factory
- Data factory creation
- Copying data from the internet
- Moving and transforming data
- Triggering and monitoring our pipeline
- Chapter 7: Azure Databricks
- Creating a Databricks workspace
- Creating a cluster in our workspace
- Connecting to an internet data source
- Using Delta Lake
- Writing in Azure SQL Server
- Chapter 8: SSIS Migration Strategies
- Running SSIS packages in ADF
- Rewriting an SSIS package using ADF
- Chapter 9: Profiling data in Azure
- Technical requirements
- Using SQL in Spark
- Using Spark built-in functions
- Using pandas profiling
- Chapter 10: Manage SSIS and Azure Data Factory with Biml
- Technical requirements
- Installing prerequisites
- Generating T-SQL select statements for all tables
- Generating T-SQL to drop and create all indexes
- Generating a basic SSIS package
- Generating an SSIS package with Execute SQL Tasks
- Using Convert SSIS package to Biml
- Generating a mass change to stored procedures
- Generating an SSIS package containing a Data Flow Task
- Generating your first Azure Data Factory
- Loading multiple files using Data Factory
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- Leave a review - let other readers know what you think 更新時間:2021-06-11 18:03:00
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