{"product_id":"data-analytics-in-the-aws-cloud-9781119909248","title":"Data Analytics in the AWS Cloud","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA comprehensive and accessible roadmap to performing data analytics in the AWS cloud\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eData Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS\u003c\/i\u003e, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you'll explore every relevant aspect of data analyticsfrom data engineering to analysis, business intelligence, DevOps, and MLOpsas you discover how to integrate machine learning predictions with analytics engines and visualization tools. \u003c\/p\u003e\u003cp\u003eYou'll also find: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eReal-world use cases of AWS architectures that demystify the applications of data analytics\u003c\/li\u003e \u003cli\u003eAccessible introductions to data acquisition, importation, storage, visualization, and reporting\u003c\/li\u003e \u003cli\u003eExpert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify m\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 AWS Data Lakes and Analytics Technology Overview 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy AWS? 1\u003c\/p\u003e \u003cp\u003eWhat Does a Data Lake Look Like in AWS? 2\u003c\/p\u003e \u003cp\u003eAnalytics on AWS 3\u003c\/p\u003e \u003cp\u003eSkills Required to Build and Maintain an AWS Analytics Pipeline 3\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Path to Analytics: Setting Up a Data and Analytics Team 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Data Vision 6\u003c\/p\u003e \u003cp\u003eSupport 6\u003c\/p\u003e \u003cp\u003eDA Team Roles 7\u003c\/p\u003e \u003cp\u003eEarly Stage Roles 7\u003c\/p\u003e \u003cp\u003eTeam Lead 8\u003c\/p\u003e \u003cp\u003eData Architect 8\u003c\/p\u003e \u003cp\u003eData Engineer 8\u003c\/p\u003e \u003cp\u003eData Analyst 9\u003c\/p\u003e \u003cp\u003eMaturity Stage Roles 9\u003c\/p\u003e \u003cp\u003eData Scientist 9\u003c\/p\u003e \u003cp\u003eCloud Engineer 10\u003c\/p\u003e \u003cp\u003eBusiness Intelligence (BI) Developer 10\u003c\/p\u003e \u003cp\u003eMachine Learning Engineer 10\u003c\/p\u003e \u003cp\u003eBusiness Analyst 11\u003c\/p\u003e \u003cp\u003eNiche Roles 11\u003c\/p\u003e \u003cp\u003eAnalytics Flow at a Process Level 12\u003c\/p\u003e \u003cp\u003eWorkflow Methodology 12\u003c\/p\u003e \u003cp\u003eThe DA Team Mantra: “Automate Everything” 14\u003c\/p\u003e \u003cp\u003eAnalytics Models in the Wild: Centralized, Distributed, Center of Excellence 15\u003c\/p\u003e \u003cp\u003eCentralized 15\u003c\/p\u003e \u003cp\u003eDistributed 16\u003c\/p\u003e \u003cp\u003eCenter of Excellence 16\u003c\/p\u003e \u003cp\u003eSummary 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Working on AWS 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAccessing AWS 20\u003c\/p\u003e \u003cp\u003eEverything Is a Resource 21\u003c\/p\u003e \u003cp\u003eS3: An Important Exception 21\u003c\/p\u003e \u003cp\u003eIAM: Policies, Roles, and Users 22\u003c\/p\u003e \u003cp\u003ePolicies 22\u003c\/p\u003e \u003cp\u003eIdentity- Based Policies 24\u003c\/p\u003e \u003cp\u003eResource- Based Policies 25\u003c\/p\u003e \u003cp\u003eRoles 25\u003c\/p\u003e \u003cp\u003eUsers and User Groups 25\u003c\/p\u003e \u003cp\u003eSummarizing IAM 26\u003c\/p\u003e \u003cp\u003eWorking with the Web Console 26\u003c\/p\u003e \u003cp\u003eThe AWS Command- Line Interface 29\u003c\/p\u003e \u003cp\u003eInstalling AWS cli 29\u003c\/p\u003e \u003cp\u003eLinux Installation 30\u003c\/p\u003e \u003cp\u003emacOS Installation 30\u003c\/p\u003e \u003cp\u003eWindows 31\u003c\/p\u003e \u003cp\u003eConfiguring AWS cli 31\u003c\/p\u003e \u003cp\u003eA Note on Region 33\u003c\/p\u003e \u003cp\u003eSetting Individual Parameters 33\u003c\/p\u003e \u003cp\u003eUsing Profiles and Configuration Files 33\u003c\/p\u003e \u003cp\u003eFinal Notes on Configuration 36\u003c\/p\u003e \u003cp\u003eUsing the AWS cli 36\u003c\/p\u003e \u003cp\u003eUsing Skeletons and File Inputs 39\u003c\/p\u003e \u003cp\u003eCleaning Up! 43\u003c\/p\u003e \u003cp\u003eInfrastructure- as- Code: CloudFormation and Terraform 44\u003c\/p\u003e \u003cp\u003eCloudFormation 44\u003c\/p\u003e \u003cp\u003eCloudFormation Stacks 46\u003c\/p\u003e \u003cp\u003eCloudFormation Template Anatomy 47\u003c\/p\u003e \u003cp\u003eCloudFormation Changesets 52\u003c\/p\u003e \u003cp\u003eGetting Stack Information 55\u003c\/p\u003e \u003cp\u003eCleaning Up Again 57\u003c\/p\u003e \u003cp\u003eCloudFormation Conclusions 58\u003c\/p\u003e \u003cp\u003eTerraform 58\u003c\/p\u003e \u003cp\u003eCoding Style 58\u003c\/p\u003e \u003cp\u003eModularity 59\u003c\/p\u003e \u003cp\u003eLimitations 59\u003c\/p\u003e \u003cp\u003eTerraform vs. CloudFormation 60\u003c\/p\u003e \u003cp\u003eInfrastructure- as- Code: CDK, Pulumi, Cloudcraft, and Other Solutions 60\u003c\/p\u003e \u003cp\u003eAWS CDK 60\u003c\/p\u003e \u003cp\u003ePulumi 62\u003c\/p\u003e \u003cp\u003eCloudcraft 62\u003c\/p\u003e \u003cp\u003eInfrastructure Management Conclusions 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Serverless Computing and Data Engineering 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eServerless vs. Fully Managed 65\u003c\/p\u003e \u003cp\u003eAWS Serverless Technologies 66\u003c\/p\u003e \u003cp\u003eAWS Lambda 67\u003c\/p\u003e \u003cp\u003ePricing Model 67\u003c\/p\u003e \u003cp\u003eLaser Focus on Code 68\u003c\/p\u003e \u003cp\u003eThe Lambda Paradigm Shift 69\u003c\/p\u003e \u003cp\u003eVirtually Infinite Scalability 70\u003c\/p\u003e \u003cp\u003eGeographical Distribution 70\u003c\/p\u003e \u003cp\u003eA Lambda Hello World 71\u003c\/p\u003e \u003cp\u003eLambda Configuration 74\u003c\/p\u003e \u003cp\u003eRuntime 74\u003c\/p\u003e \u003cp\u003eContainer- Based Lambdas 75\u003c\/p\u003e \u003cp\u003eArchitectures 75\u003c\/p\u003e \u003cp\u003eMemory 75\u003c\/p\u003e \u003cp\u003eNetworking 76\u003c\/p\u003e \u003cp\u003eExecution Role 76\u003c\/p\u003e \u003cp\u003eEnvironment Variables 76\u003c\/p\u003e \u003cp\u003eAWS EventBridge 77\u003c\/p\u003e \u003cp\u003eAWS Fargate 77\u003c\/p\u003e \u003cp\u003eAWS DynamoDB 77\u003c\/p\u003e \u003cp\u003eAWS SNS 77\u003c\/p\u003e \u003cp\u003eAmazon SQS 78\u003c\/p\u003e \u003cp\u003eAWS CloudWatch 78\u003c\/p\u003e \u003cp\u003eAmazon QuickSight 78\u003c\/p\u003e \u003cp\u003eAWS Step Functions 78\u003c\/p\u003e \u003cp\u003eAmazon API Gateway 79\u003c\/p\u003e \u003cp\u003eAmazon Cognito 79\u003c\/p\u003e \u003cp\u003eAWS Serverless Application Model (SAM) 79\u003c\/p\u003e \u003cp\u003eEphemeral Infrastructure 80\u003c\/p\u003e \u003cp\u003eAWS SAM Installation 80\u003c\/p\u003e \u003cp\u003eConfiguration 80\u003c\/p\u003e \u003cp\u003eCreating Your First AWS SAM Project 81\u003c\/p\u003e \u003cp\u003eApplication Structure 83\u003c\/p\u003e \u003cp\u003eSAM Resource Types 85\u003c\/p\u003e \u003cp\u003eSAM Lambda Template 86\u003c\/p\u003e \u003cp\u003e!! Recursive Lambda Invocation !! 88\u003c\/p\u003e \u003cp\u003eFunction Metadata 88\u003c\/p\u003e \u003cp\u003eOutputs 89\u003c\/p\u003e \u003cp\u003eImplicitly Generated Resources 89\u003c\/p\u003e \u003cp\u003eOther Template Sections 90\u003c\/p\u003e \u003cp\u003eLambda Code 90\u003c\/p\u003e \u003cp\u003eBuilding Your First SAM Application 93\u003c\/p\u003e \u003cp\u003eTesting the AWS SAM Application Locally 96\u003c\/p\u003e \u003cp\u003eDeployment 99\u003c\/p\u003e \u003cp\u003eCleaning Up 104\u003c\/p\u003e \u003cp\u003eSummary 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Data Ingestion 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAWS Data Lake Architecture 106\u003c\/p\u003e \u003cp\u003eServerless Data Lake Architecture Structure 106\u003c\/p\u003e \u003cp\u003eIngestion 106\u003c\/p\u003e \u003cp\u003eStorage and Processing 108\u003c\/p\u003e \u003cp\u003eCataloging, Governance, and Search 108\u003c\/p\u003e \u003cp\u003eSecurity and Monitoring 109\u003c\/p\u003e \u003cp\u003eConsumption 109\u003c\/p\u003e \u003cp\u003eSample Processing Architecture: Cataloging Images into DynamoDB 109\u003c\/p\u003e \u003cp\u003eUse Case Description 109\u003c\/p\u003e \u003cp\u003eSAM Application Creation 110\u003c\/p\u003e \u003cp\u003eS3- Triggered Lambda 111\u003c\/p\u003e \u003cp\u003eAdding DynamoDB 119\u003c\/p\u003e \u003cp\u003eLambda Execution Context 121\u003c\/p\u003e \u003cp\u003eInserting into DynamoDB 121\u003c\/p\u003e \u003cp\u003eCleaning Up 123\u003c\/p\u003e \u003cp\u003eServerless Ingestion 124\u003c\/p\u003e \u003cp\u003eAWS Fargate 124\u003c\/p\u003e \u003cp\u003eAWS Lambda 124\u003c\/p\u003e \u003cp\u003eExample Architecture: Fargate- Based Periodic Batch Import 125\u003c\/p\u003e \u003cp\u003eThe Basic Importer 125\u003c\/p\u003e \u003cp\u003eECS CLI 128\u003c\/p\u003e \u003cp\u003eAWS Copilot cli 128\u003c\/p\u003e \u003cp\u003eClean Up 136\u003c\/p\u003e \u003cp\u003eAWS Kinesis Ingestion 136\u003c\/p\u003e \u003cp\u003eExample Architecture: Two- Pronged Delivery 137\u003c\/p\u003e \u003cp\u003eFully Managed Ingestion with AppFlow 146\u003c\/p\u003e \u003cp\u003eOperational Data Ingestion with Database Migration Service 151\u003c\/p\u003e \u003cp\u003eDMS Concepts 151\u003c\/p\u003e \u003cp\u003eDMS Instance 151\u003c\/p\u003e \u003cp\u003eDMS Endpoints 152\u003c\/p\u003e \u003cp\u003eDMS Tasks 152\u003c\/p\u003e \u003cp\u003eSummary of the Workflow 152\u003c\/p\u003e \u003cp\u003eCommon Use of DMS 153\u003c\/p\u003e \u003cp\u003eExample Architecture: DMS to S3 154\u003c\/p\u003e \u003cp\u003eDMS Instance 154\u003c\/p\u003e \u003cp\u003eDMS Endpoints 156\u003c\/p\u003e \u003cp\u003eDMS Task 162\u003c\/p\u003e \u003cp\u003eSummary 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Processing Data 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePhases of Data Preparation 170\u003c\/p\u003e \u003cp\u003eWhat Is ETL? Why Should I Care? 170\u003c\/p\u003e \u003cp\u003eETL Job vs. Streaming Job 171\u003c\/p\u003e \u003cp\u003eOverview of ETL in AWS 172\u003c\/p\u003e \u003cp\u003eETL with AWS Glue 172\u003c\/p\u003e \u003cp\u003eETL with Lambda Functions 172\u003c\/p\u003e \u003cp\u003eETL with Hadoop\/EMR 173\u003c\/p\u003e \u003cp\u003eOther Ways to Perform ETL 173\u003c\/p\u003e \u003cp\u003eETL Job Design Concepts 173\u003c\/p\u003e \u003cp\u003eSource Identification 174\u003c\/p\u003e \u003cp\u003eDestination Identification 174\u003c\/p\u003e \u003cp\u003eMappings 174\u003c\/p\u003e \u003cp\u003eValidation 174\u003c\/p\u003e \u003cp\u003eFilter 175\u003c\/p\u003e \u003cp\u003eJoin, Denormalization, Relationalization 175\u003c\/p\u003e \u003cp\u003eAWS Glue for ETL 176\u003c\/p\u003e \u003cp\u003eReally, It’s Just Spark 176\u003c\/p\u003e \u003cp\u003eVisual 176\u003c\/p\u003e \u003cp\u003eSpark Script Editor 177\u003c\/p\u003e \u003cp\u003ePython Shell Script Editor 177\u003c\/p\u003e \u003cp\u003eJupyter Notebook 177\u003c\/p\u003e \u003cp\u003eConnectors 177\u003c\/p\u003e \u003cp\u003eCreating Connections 178\u003c\/p\u003e \u003cp\u003eCreating Connections with the Web Console 178\u003c\/p\u003e \u003cp\u003eCreating Connections with the AWS cli 179\u003c\/p\u003e \u003cp\u003eCreating ETL Jobs with AWS Glue Visual Editor 184\u003c\/p\u003e \u003cp\u003eETL Example: Format Switch from Raw (JSON) to Cleaned (Parquet) 184\u003c\/p\u003e \u003cp\u003eJob Bookmarks 187\u003c\/p\u003e \u003cp\u003eTransformations 188\u003c\/p\u003e \u003cp\u003eApply Mapping 189\u003c\/p\u003e \u003cp\u003eFilter 189\u003c\/p\u003e \u003cp\u003eOther Available Transforms 190\u003c\/p\u003e \u003cp\u003eRun the Edited Job 191\u003c\/p\u003e \u003cp\u003eVisual Editor with Source and Target Conclusions 192\u003c\/p\u003e \u003cp\u003eCreating ETL Jobs with AWS Glue Visual Editor (without Source and Target) 192\u003c\/p\u003e \u003cp\u003eCreating ETL Jobs with the Spark Script Editor 192\u003c\/p\u003e \u003cp\u003eDeveloping ETL Jobs with AWS Glue Notebooks 193\u003c\/p\u003e \u003cp\u003eWhat Is a Notebook? 194\u003c\/p\u003e \u003cp\u003eNotebook Structure 194\u003c\/p\u003e \u003cp\u003eStep 1: Load Code into a DynamicFrame 196\u003c\/p\u003e \u003cp\u003eStep 2: Apply Field Mapping 197\u003c\/p\u003e \u003cp\u003eStep 3: Apply the Filter 197\u003c\/p\u003e \u003cp\u003eStep 4: Write to S3 in Parquet Format 198\u003c\/p\u003e \u003cp\u003eExample: Joining and Denormalizing Data from Two S3 Locations 199\u003c\/p\u003e \u003cp\u003eConclusions for Manually Authored Jobs with Notebooks 203\u003c\/p\u003e \u003cp\u003eCreating ETL Jobs with AWS Glue Interactive Sessions 204\u003c\/p\u003e \u003cp\u003eIt’s Magic 205\u003c\/p\u003e \u003cp\u003eDevelopment Workflow 206\u003c\/p\u003e \u003cp\u003eStreaming Jobs 207\u003c\/p\u003e \u003cp\u003eDifferences with a Standard ETL Job 208\u003c\/p\u003e \u003cp\u003eStreaming Sources 208\u003c\/p\u003e \u003cp\u003eExample: Process Kinesis Streams with a Streaming Job 208\u003c\/p\u003e \u003cp\u003eStreaming ETL Jobs Conclusions 217\u003c\/p\u003e \u003cp\u003eSummary 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Cataloging, Governance, and Search 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCataloging with AWS Glue 219\u003c\/p\u003e \u003cp\u003eAWS Glue and the AWS Glue Data Catalog 219\u003c\/p\u003e \u003cp\u003eGlue Databases and Tables 220\u003c\/p\u003e \u003cp\u003eDatabases 220\u003c\/p\u003e \u003cp\u003eThe Idea of Schema- on- Read 221\u003c\/p\u003e \u003cp\u003eTables 222\u003c\/p\u003e \u003cp\u003eCreate Table Manually 223\u003c\/p\u003e \u003cp\u003eCreating a Table from an Existing Schema 225\u003c\/p\u003e \u003cp\u003eCreating a Table with a Crawler 225\u003c\/p\u003e \u003cp\u003eSummary on Databases and Tables 226\u003c\/p\u003e \u003cp\u003eCrawlers 226\u003c\/p\u003e \u003cp\u003eUpdating or Not Updating? 230\u003c\/p\u003e \u003cp\u003eRunning the Crawler 231\u003c\/p\u003e \u003cp\u003eCreating a Crawler from the AWS CLI 231\u003c\/p\u003e \u003cp\u003eRetrieving Table Information from the CLI 233\u003c\/p\u003e \u003cp\u003eClassifiers 235\u003c\/p\u003e \u003cp\u003eClassifier Example 236\u003c\/p\u003e \u003cp\u003eCrawlers and Classifiers Summary 237\u003c\/p\u003e \u003cp\u003eSearch with Amazon Athena: The Heart of Analytics in AWS 238\u003c\/p\u003e \u003cp\u003eA Bit of History 238\u003c\/p\u003e \u003cp\u003eInterface Overview 238\u003c\/p\u003e \u003cp\u003eCreating Tables Manually 239\u003c\/p\u003e \u003cp\u003eAthena Data Types 240\u003c\/p\u003e \u003cp\u003eComplex Types 241\u003c\/p\u003e \u003cp\u003eRunning a Query 242\u003c\/p\u003e \u003cp\u003eConnecting with JDBC and ODBC 243\u003c\/p\u003e \u003cp\u003eQuery Stats 243\u003c\/p\u003e \u003cp\u003eRecent Queries and Saved Queries 243\u003c\/p\u003e \u003cp\u003eThe Power of Partitions 244\u003c\/p\u003e \u003cp\u003eAthena Pricing Model 244\u003c\/p\u003e \u003cp\u003eAutomatic Naming 245\u003c\/p\u003e \u003cp\u003eAthena Query Output 246\u003c\/p\u003e \u003cp\u003eAthena Peculiarities (SQL and Not) 246\u003c\/p\u003e \u003cp\u003eComputed Fields Gotcha and WITH Statement Workaround 246\u003c\/p\u003e \u003cp\u003eLowercase! 247\u003c\/p\u003e \u003cp\u003eQuery Explain 248\u003c\/p\u003e \u003cp\u003eDeduplicating Records 249\u003c\/p\u003e \u003cp\u003eWorking with JSON, Flattening, and Unnesting 250\u003c\/p\u003e \u003cp\u003eAthena Views 251\u003c\/p\u003e \u003cp\u003eCreate Table as Select (CTAS) 252\u003c\/p\u003e \u003cp\u003eSaving Queries and Reusing Saved Queries 253\u003c\/p\u003e \u003cp\u003eRunning Parameterized Queries 254\u003c\/p\u003e \u003cp\u003eAthena Federated Queries 254\u003c\/p\u003e \u003cp\u003eAthena Lambda Connectors 255\u003c\/p\u003e \u003cp\u003eNote on Connection Errors 256\u003c\/p\u003e \u003cp\u003ePerforming Federated Queries 257\u003c\/p\u003e \u003cp\u003eCreating a View from a Federated Query 258\u003c\/p\u003e \u003cp\u003eGoverning: Athena Workgroups, Lake Formation, and More 258\u003c\/p\u003e \u003cp\u003eAthena Workgroups 259\u003c\/p\u003e \u003cp\u003eFine- Grained Athena Access with IAM 262\u003c\/p\u003e \u003cp\u003eRecap of Athena- Based Governance 264\u003c\/p\u003e \u003cp\u003eAWS Lake Formation 265\u003c\/p\u003e \u003cp\u003eRegistering a Location in Lake Formation 266\u003c\/p\u003e \u003cp\u003eCreating a Database in Lake Formation 268\u003c\/p\u003e \u003cp\u003eAssigning Permissions in Lake Formation 269\u003c\/p\u003e \u003cp\u003eLF- Tags and Permissions in Lake Formation 271\u003c\/p\u003e \u003cp\u003eData Filters 277\u003c\/p\u003e \u003cp\u003eGovernance Conclusions 279\u003c\/p\u003e \u003cp\u003eSummary 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Data Consumption: BI, Visualization, and Reporting 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eQuickSight 283\u003c\/p\u003e \u003cp\u003eSigning Up for QuickSight 284\u003c\/p\u003e \u003cp\u003eStandard Plan 284\u003c\/p\u003e \u003cp\u003eEnterprise Plan 284\u003c\/p\u003e \u003cp\u003eUsers and User Groups 285\u003c\/p\u003e \u003cp\u003eManaging Users and Groups 285\u003c\/p\u003e \u003cp\u003eManaging QuickSight 286\u003c\/p\u003e \u003cp\u003eUsers and Groups 287\u003c\/p\u003e \u003cp\u003eYour Subscriptions 287\u003c\/p\u003e \u003cp\u003eSPICE Capacity 287\u003c\/p\u003e \u003cp\u003eAccount Settings 287\u003c\/p\u003e \u003cp\u003eSecurity and Permissions 287\u003c\/p\u003e \u003cp\u003eVPC Connections 288\u003c\/p\u003e \u003cp\u003eMobile Settings 289\u003c\/p\u003e \u003cp\u003eDomains and Embedding 289\u003c\/p\u003e \u003cp\u003eSingle Sign- On 289\u003c\/p\u003e \u003cp\u003eData Sources and Datasets 289\u003c\/p\u003e \u003cp\u003eCreating an Athena Data Source 291\u003c\/p\u003e \u003cp\u003eCreating Other Data Sources 292\u003c\/p\u003e \u003cp\u003eCreating a Data Source from the AWS cli 292\u003c\/p\u003e \u003cp\u003eCreating a Dataset from a Table 294\u003c\/p\u003e \u003cp\u003eCreating a Dataset from a SQL Query 295\u003c\/p\u003e \u003cp\u003eDuplicating Datasets 296\u003c\/p\u003e \u003cp\u003eNote on Creating Datasets 297\u003c\/p\u003e \u003cp\u003eQuickSight Favorites, Recent, and Folders 297\u003c\/p\u003e \u003cp\u003eSPICE 298\u003c\/p\u003e \u003cp\u003eManage SPICE Capacity 298\u003c\/p\u003e \u003cp\u003eRefresh Schedule 299\u003c\/p\u003e \u003cp\u003eQuickSight Data Editor 299\u003c\/p\u003e \u003cp\u003eQuickSight Data Types 302\u003c\/p\u003e \u003cp\u003eChange Data Types 302\u003c\/p\u003e \u003cp\u003eCalculated Fields 303\u003c\/p\u003e \u003cp\u003eJoining Data 305\u003c\/p\u003e \u003cp\u003eExcluding Fields 309\u003c\/p\u003e \u003cp\u003eFiltering Data 309\u003c\/p\u003e \u003cp\u003eRemoving Data 310\u003c\/p\u003e \u003cp\u003eGeospatial Hierarchies and Adding Fields to Hierarchies 310\u003c\/p\u003e \u003cp\u003eUnsupported Format Dates 311\u003c\/p\u003e \u003cp\u003eVisualizing Data: QuickSight Analysis 312\u003c\/p\u003e \u003cp\u003eAdding a Title and a Description to Your Analysis 313\u003c\/p\u003e \u003cp\u003eRenaming the Sheet 314\u003c\/p\u003e \u003cp\u003eYour First Visual with AutoGraph 314\u003c\/p\u003e \u003cp\u003eField Wells 314\u003c\/p\u003e \u003cp\u003eVisuals Types 315\u003c\/p\u003e \u003cp\u003eSaving and Autosaving 316\u003c\/p\u003e \u003cp\u003eA First Example: Pie Chart 316\u003c\/p\u003e \u003cp\u003eRenaming a Visual 317\u003c\/p\u003e \u003cp\u003eFiltering Data 318\u003c\/p\u003e \u003cp\u003eAdding Drill- Downs 320\u003c\/p\u003e \u003cp\u003eParameters 321\u003c\/p\u003e \u003cp\u003eActions 324\u003c\/p\u003e \u003cp\u003eInsights 328\u003c\/p\u003e \u003cp\u003eML- Powered Insights 330\u003c\/p\u003e \u003cp\u003eSharing an Analysis 335\u003c\/p\u003e \u003cp\u003eDashboards 335\u003c\/p\u003e \u003cp\u003eDashboard Layouts and Themes 335\u003c\/p\u003e \u003cp\u003ePublishing a Dashboard 336\u003c\/p\u003e \u003cp\u003eEmbedding Visuals and Dashboards 337\u003c\/p\u003e \u003cp\u003eData Consumption: Not Only Dashboards 337\u003c\/p\u003e \u003cp\u003eSummary 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Machine Learning at Scale 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine Learning and Artificial Intelligence 339\u003c\/p\u003e \u003cp\u003eWhat Are ML\/AI Use Cases? 340\u003c\/p\u003e \u003cp\u003eTypes of ML Models 340\u003c\/p\u003e \u003cp\u003eOverview of ML\/AI AWS Solutions 341\u003c\/p\u003e \u003cp\u003eAmazon SageMaker 341\u003c\/p\u003e \u003cp\u003eSageMaker Domains 342\u003c\/p\u003e \u003cp\u003eAdding a User to the Domain 344\u003c\/p\u003e \u003cp\u003eSageMaker Studio 344\u003c\/p\u003e \u003cp\u003eSageMaker Example Notebook 346\u003c\/p\u003e \u003cp\u003eStep 1: Prerequisites and Preprocessing 346\u003c\/p\u003e \u003cp\u003eStep 2: Data Ingestion 347\u003c\/p\u003e \u003cp\u003eStep 3: Data Inspection 348\u003c\/p\u003e \u003cp\u003eStep 4: Data Conversion 349\u003c\/p\u003e \u003cp\u003eStep 5: Upload Training Data 349\u003c\/p\u003e \u003cp\u003eStep 6: Train the Model 349\u003c\/p\u003e \u003cp\u003eStep 7: Set Up Hosting and Deploy the Model 351\u003c\/p\u003e \u003cp\u003eStep 8: Validate the Model 352\u003c\/p\u003e \u003cp\u003eStep 9: Use the Model 353\u003c\/p\u003e \u003cp\u003eInference 353\u003c\/p\u003e \u003cp\u003eReal Time 354\u003c\/p\u003e \u003cp\u003eAsynchronous 354\u003c\/p\u003e \u003cp\u003eServerless 354\u003c\/p\u003e \u003cp\u003eBatch Transform 354\u003c\/p\u003e \u003cp\u003eData Wrangler 356\u003c\/p\u003e \u003cp\u003eSageMaker Canvas 357\u003c\/p\u003e \u003cp\u003eSummary 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Example Data Architectures in AWS 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModern Data Lake Architecture 360\u003c\/p\u003e \u003cp\u003eETL in a Lake House 361\u003c\/p\u003e \u003cp\u003eConsuming Data in the Lake House 361\u003c\/p\u003e \u003cp\u003eThe Modern Data Lake Architecture 362\u003c\/p\u003e \u003cp\u003eBatch Processing 362\u003c\/p\u003e \u003cp\u003eStream Processing 363\u003c\/p\u003e \u003cp\u003eArchitecture Design Recommendations 364\u003c\/p\u003e \u003cp\u003eAutomate Everything 365\u003c\/p\u003e \u003cp\u003eBuild on Events 365\u003c\/p\u003e \u003cp\u003ePerformance = Cost Savings 365\u003c\/p\u003e \u003cp\u003eAWS Glue Catalog and Athena- Centric Workflow 365\u003c\/p\u003e \u003cp\u003eDesign Flexible 365\u003c\/p\u003e \u003cp\u003ePick Your Battles 365\u003c\/p\u003e \u003cp\u003eParquet 366\u003c\/p\u003e \u003cp\u003eSummary 366\u003c\/p\u003e \u003cp\u003eIndex 367\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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