Preface

13

Who This Book Is for

13

How This Book Is Organized

14

Acknowledgments

16

Conclusion

16

1 Introduction

17

1.1 What Are the Tools for Provisioning Data?

17

1.1.1 Extract, Transform, and Load

18

1.1.2 Cleansing

26

1.1.3 Replication

30

1.2 How Are These Tools Used Together?

31

1.3 Summary

36

2 SAP HANA Smart Data Integration

37

2.1 What Is SAP HANA Smart Data Integration?

37

2.2 Use Cases for SAP HANA Smart Data Integration

38

2.3 Installation and Configuration

39

2.3.1 Data Provisioning Server

40

2.3.2 Data Provisioning Delivery Unit

41

2.3.3 Data Provisioning Agent

44

2.4 Using SAP HANA Smart Data Integration

48

2.4.1 SAP HANA Web-Based Development Workbench

48

2.4.2 Creating Flowgraphs

50

2.4.3 Configuring the Data Provisioning Agent for Flat File Access

54

2.4.4 Reading Flat Files

57

2.4.5 Building Blocks

67

2.4.6 Real-Time Flowgraphs

78

2.4.7 Monitoring

83

2.5 Summary

89

3 SAP HANA Smart Data Quality

91

3.1 What Is SAP HANA Smart Data Quality?

91

3.2 How Do SAP HANA Smart Data Integration and SAP HANA Smart Data Quality Work Together?

92

3.3 Installation and Configuration

93

3.3.1 Enabling the Script Server

93

3.3.2 Downloading and Deploying SAP Smart Data Quality Directories

95

3.3.3 Creating Authorized Users for SAP Smart Data Quality

101

3.4 Using SAP HANA Smart Data Quality

103

3.4.1 Identifying Cleansing Options

103

3.4.2 Identifying Matching Options

110

3.4.3 Identifying Geocode Solution Options

117

3.4.4 The Script Server

121

3.5 Summary

122

4 SAP Agile Data Preparation

123

4.1 What Is SAP Agile Data Preparation?

123

4.2 SAP Agile Data Preparation and SAP HANA

124

4.3 SAP Agile Data Preparation: On-Premise versus Cloud

124

4.4 Installation and Configuration

126

4.4.1 Downloading the Files

126

4.4.2 Importing the Delivery Units

132

4.4.3 Adding Data Domain Tiles

138

4.4.4 Security Management

139

4.5 Using SAP Agile Data Preparation

140

4.5.1 Creating a Project and Loading Data

140

4.5.2 Navigating the Side Panel

145

4.5.3 Reviewing Data Quality Statistics

147

4.5.4 Actioning Data

149

4.5.5 Cleansing and De-duplicating Data

156

4.5.6 Creating Rules

161

4.5.7 Sharing Data from a Project

163

4.6 Summary

165

5 SAP Data Services

167

5.1 What Is SAP Data Services?

168

5.1.1 Datastores

168

5.1.2 Jobs

172

5.1.3 Workflows

174

5.1.4 Data Flows and Transforms

183

5.1.5 Real-Time Jobs in SAP Data Services

192

5.2 Installation and Configuration

194

5.2.1 Install Information Platform Services

194

5.2.2 Install SAP Data Services

196

5.3 Using SAP Data Services

202

5.3.1 Batch Data Loading

202

5.3.2 Best Practices

211

5.4 Summary

217

6 SAP Landscape Transformation Replication Server

219

6.1 What Is the SAP Landscape Transformation Replication Server?

219

6.2 Installation and Configuration

222

6.2.1 ABAP Source System

223

6.2.2 Separate Server with an ABAP Source System

224

6.2.3 Separate Server with a Non-ABAP Source System

224

6.3 Using the SAP LT Replication Server

225

6.3.1 Configuring and Managing the Replication Process

225

6.3.2 Creating a Configuration

230

6.3.3 Authorizations

232

6.3.4 Initial versus Ongoing Data Replication

234

6.3.5 Transformation Capabilities

236

6.4 Summary

238

7 SAP Data Quality Management, Microservices for Location Data

241

7.1 What Is SAP Data Quality Management, Microservices for Location Data?

241

7.2 Invoking Microservices for Location Data

243

7.2.1 Address Cleansing and Geocoding

243

7.2.2 Reverse Geocoding

249

7.2.3 Information Codes and Messages

251

7.3 Installation and Configuration

252

7.3.1 Getting Started

252

7.3.2 Supported Integrations

253

7.3.3 Authentication

256

7.3.4 Configuration Editor

257

7.4 Using Prebuilt Functions

258

7.5 Summary

259

8 SAP HANA Data in the Cloud

261

8.1 Cloud Considerations

261

8.2 SAP Cloud Platform

265

8.2.1 SAP Cloud Connector

265

8.2.2 Architecture

267

8.2.3 Integration

268

8.3 Amazon Web Services

270

8.4 Microsoft Azure

275

8.5 Summary

279

9 Data Provisioning Case Studies

281

9.1 Data Preparation for an Omnichannel Initiative

281

9.1.1 Company Background

282

9.1.2 Solution

284

9.2 Supply Chain Analytics for Reducing Cost of Goods Sold

303

9.2.1 Company Background

304

9.2.2 Solution

307

9.3 Profile and Transform Customer Data

323

9.3.1 Company Background

323

9.3.2 Solution

324

9.4 Cleaning and De-duplicating a Mailing List

332

9.4.1 Company Background

332

9.4.2 Solution

333

9.5 Summary

343