This document provides an overview of SAP HANA SQL data warehousing, including the process and products. It discusses why data warehousing is still necessary, how SAP approaches it with HANA SQL data warehousing, and the next generation landscape with BW/4HANA and SQL data warehouse on the same platform. It then details the integrated data warehouse process with tools for design, development, and deployment, including SAP Enterprise Architect Designer, SAP Web IDE, native data store objects, data lifecycle manager, and deployment with DevOps. Strengths and use cases of the SQL data warehouse approach are also summarized.

1. PUBLIC
Sefan Linders
Data Warehouse Architect
Customer Innovation & Enterprise Platform
November 2017
SAP HANA SQL Data Warehousing
Overview, Process, and Products

2. 2PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Disclaimer
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▪ This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms
directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice.
The information in this presentation is not a commitment, promise or legal obligation to deliver any material, code or
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▪ All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially
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as of their dates, and they should not be relied upon in making purchasing decisions.

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Why is data warehousing still necessary?
The factors beside performance
Characteristics
▪ Consolidates data across the
enterprise
▪ Standardizes data model
▪ Supports decision making
Main Tasks
▪ Define common semantics
▪ Harmonize data values
▪ Establish a ‘single version of truth’
▪ Provide actuals and history
Data Lake
BI | Predictive | Planning
Data Warehouse
“Single Point of Truth”
Analytics
Hadoop
Data Sources
SAP | non-SAP | Cloud

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How does SAP approach Data Warehousing
A closer look at SAP HANA Data Warehousing
SAP HANA Platform
Data Warehouse

5. 5PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Next Generation Data Warehousing Landscape
BW/4HANA and SQL Data Warehouse on one platform
SAP HANA Platform
SAP Business Warehouse
SAP BW/4HANA
SAP HANA SQL
Data Warehouse
SAP HANA
Application
Services
SAP HANA
Integration
Services
SAP HANA
Processing
Services
SAP HANA
Database
Services
Data Warehouse

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Application driven approach, SAP BW/4HANA as
premium DW application with integrated services
▪ SAP BW/4HANA is an application offering. All data
warehousing services via one integrated repository
▪ Optional integration of additional tools for modelling,
monitoring and managing the data warehouse
SQL driven approach, SAP HANA with loosely coupled
tools and platform services, logically combined
▪ SQL approaches require several loosely coupled tools, usually
having separate repositories
▪ “Best of breed” approach to build your own model
BW/4HANA and SQL Data Warehouse
Two ways to run, or get the best of both
SAP HANA Platform
SCHEDULING &
MONITORING
MODELING PLANNING
OLAP
LIFECYCLE
MANAGEMENT
ETL
SAP BW/4HANA
SAP HANA Platform
SCHEDULING &
MONITORING
MODELING PLANNING
OLAP
LIFECYCLE
MANAGEMENT
ETL
HANA SQL DW

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SQL Data Warehouse
Data process perspective of SAP defined SQL DW
Compute
& Data Store
Ingest
Sources
Consume
Data Lake
SQL Data Warehousing
ETL Replication Streaming Virtual Access …
3rd-PartyAnalytics
Sensor Machine
…
SAP Vora
BI | Predictive | PlanningBusinessObjects™
CDS - NDSO
Procedures
Flowgraphs
CalcViews
Virtual Tables
SQL
SQL
• WebIDE
• DW Foundation
• XS Advanced
DW
Scheduler
Enterprise
Architect
EIM ->

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The SQL DW runs on XS Advanced
Cloud and on-premise application platform
Compute
& Data Store
Ingest
Sources
Consume
Data Lake
SQL Data
Warehousing
ETL Replication Streaming Virtual Access …
3rd-PartyAnalytics
Sensor Machine
…
SAP Vora
In-
Memory
BI | Predictive | PlanningBusinessObjects™
XS advanced runtime
SAP Web
IDE …HALM
EA
Designer
HANA deployment
infrastructure

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Integrated Data Warehouse Process
Introducing the SQL DW application toolset
DESIGN RUNDEVELOP DEPLOY

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Designing the SQL DW
Modeling your processes and data
SAP Enterprise Architect Designer
Model across the enterprise
Native HANA 2 application
SAP PowerDesigner
Model across the enterprise
Desktop application
DESIGN RUNDEVELOP DEPLOY

11. 11PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Enterprise Architect Designer
Edition for SAP HANA
Create and integrate enterprise, landscape,
process, and data models to manage information
and systems effectively
▪ Business process architecture
▪ Landscape and application architecture
▪ Requirements management
▪ Strategy architecture to document goals and projects
▪ Physical data modeling & data architecture
▪ Reverse engineering capabilities
▪ Lineage & Impact analysis
Design
Implementation
Strategy
TechnologyBusiness
Process
Data
Landscape
Requirements

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Demo screenshots EA Designer
(the live presentation has a demo video instead)

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Building the SQL DW
One environment to build all artefacts
SAP Web IDE for HANA
Develop the entire DW from your browser
Successor of HANA Studio Dev
Major extensions for DW functions
DESIGN RUNDEVELOP DEPLOY

15. 15PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

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Demo screenshots SAP WebIDE (flowgraphs, calcviews, DB Explorer)
(the live presentation has a demo video instead)
▪ SAP Web IDE for SAP HANA is the successor to
SAP HANA web development workbench and the
development perspectives of SAP HANA studio.
▪ It offers
– Development of SAP HANA content and models
– UI development with SAPUI5
– Node.js or XSJS business code
– Git integration
▪ It is
– Browser based
– Installed as SAP HANA XSA application

17. 17PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Demo screenshots SAP WebIDE (flowgraphs, calcviews, DB Explorer)
(the live presentation has a demo video instead)

18. DWH Innovations Native DSO

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Native DataStoreObject (NDSO)
Simplification of the Data Warehouse
Classic DWH practise for request
management and delta handling
DB
procedu
re
DB
DB Metadata tables
Batch ID
Date Time
User
RunTime
Batch 5 | Jan 17 |
Batch 4 | Jan 16 |
Batch 3 | Jan 15 |
Batch 2 | Jan 14 |
Batch 1 | Jan 13 |
Native Data Store Object
Custom design and
development effort
Out of the box
NDSO
Metadata tables
Batch ID
Date Time
User
RunTime
Batch 5 | Jan 17 |
Batch 4 | Jan 16 |
Batch 3 | Jan 15 |
Batch 2 | Jan 14 |
Batch 1 | Jan 13 |

20. 20PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Native DataStoreObject (NDSO)
Simplification of the Data Warehouse process
Classic DWH practise for request management and delta
handling
- To be able to enable delta propagation, or roll-back of data loads, “Request”
or “Batch” management is needed
- Metadata on data loads needs to be stored in the target table load to (e.g. a
batch ID), and a metadata framework is developed to record load date/time,
execution user, number of records loaded
- To allow for roll-back, additional table is needed to record all changes
(before/after image), or all data changes need to be time-sliced in target
table
- Setting this up and keeping it running can take considerable effort, for
example for design of metadata tables, roll-back database procedures, and
monitoring functions.
- Running these processes can be resource intensive and increase DWH load
times
DB
procedu
re
DB
DB Metadata tables
Batch ID
Date Time
User
RunTime
Batch 5 | Jan 17 |
Batch 4 | Jan 16 |
Batch 3 | Jan 15 |
Batch 2 | Jan 14 |
Batch 1 | Jan 13 |
Native Data Store Object
- The NDSO provides request management and delta handling out of the box
- The NDSO is delivered with a friendly user interface for load monitoring and
request handling features such as roll-back
- The NDSO integrates natively with EIM flowgraphs, and with 3rd party ETL
- The NDSO supports the “delta language” of SAP data source extractors
Design and development effort Out of the box
NDSO
Metadata tables
Batch ID
Date Time
User
RunTi
me
Batch 5 | Jan 17 |
Batch 4 | Jan 16 |
Batch 3 | Jan 15 |
Batch 2 | Jan 14 |
Batch 1 | Jan 13 |

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Data set A
Data set B
The Native DataStoreObject
(NDSO) adds a management layer
to a “simple” table.
The NDSO provides out of the box
Delta and Request management.
Request management (example)
Data set C updated and deleted
data from earlier loaded data set
B. The NDSO “roll-back” function
uses the changelog to restore to
earlier state, in case of errors.
Access as usual
NDSO data can be accessed
by CalcViews or any other
tool like any other table
Delta propagation
The built-in changelog enables delta
loads from SAP data sources, and
to subsequent NDSO or BW-ADSO
NDSO
Any datasource
E.g. SAP Extractor, SQL
statement, SDI Flowgraph, Data
Services, 3rd party ETL
Data set C
active table change log
Native DataStoreObject (NDSO)
Simplification of the Data Warehouse process

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Deploying the SQL DW
This is where DevOps comes in
DESIGN RUNDEVELOP DEPLOY
Open Source deployment
Bring your own tools: Jenkins,
Bamboo, XL release, etc.
SAP HALM*
Native HANA 2 application
*Planned
CTS+
XSA integrates with
enhanced change and
transport system (CTS+)

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Classic DWH development
All developers work in the same workspace and runtime, on the same version
• In HANA XS Classic, or in a
common best-of-breed data
warehouse project, all
developers work on the same
repository and the same run-
time environment.
• Any change made by one
developer and activated on the
database, in the ETL tool, or
other tooling, is immediately
visible for all other developers.
• This “shared workspace” and
“shared runtime” make it hard to
develop and test features or user
stories isolated from other
developers.

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Developer and feature isolation
Enabling parallel development and test
• In HANA XS Advanced, all
developers work in their isolated
workspace.
• Each developer also works with
an isolated runtime. HANA XS
Advanced automatically creates
a runtime container for each
developer.
• All developed objects are stored
in a shared repository: GIT,
which keeps a full version
history, and uses branching to
support isolated feature
development.
GIT
repository
(continuous)
Testing
Deployment

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Versioning and development with GIT
Working in parallel on different repository versions
User story 1
User story 2
Master
Time

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Deployment example
Continuous…
WebIDE
Continuous Integration (CI) Server
Daily
Builds
SIT/UAT Prod
DeployDeploy
Assemble
& Deploy
Regression
Deploy
Test++ Production
Continuous Testing | Integration | Deployment

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Deploying the SQL DW
This is where DevOps comes in
DESIGN RUNDEVELOP DEPLOY
EIM & DWF Monitoring
EIM, Scheduler & NDSO Monitor
Build into Webide
Data Lifecycle Manager
Data Warehouse Foundation
PowerDesigner &
Enterprise Architect Designer
Data Lineage

28. 33PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Demo screenshots (the live presentation has a demo video instead)

29. 34PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Data Lifecycle Manager
Data Warehouse Foundation
Data Lake
(Cold Store)
SQL Data
Warehousing
SAP Vora
In-Memory
(Hot Store)
Dynamic Tiering
(Warm Store)
TBs - 10s of TBs 10s of TBs - PBs
HADOOP
SAP IQ
DLM Generated
Union & Pruning CalcViews
Structured data
for fast analytics
Less frequently
accessed,
structured data
Raw data:
semi-structured,
unstructured,
streaming data etc.
DLM
DLM managed data placement
Based on aging rules

30. 35PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Integrated Data Warehouse Process
Introducing the SQL DW application toolset
DESIGN RUNDEVELOP DEPLOY

31. 37PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Strengths
▪ Complete web approach with HANA XS Advanced platform. Still
100% open SQL approach.
▪ Strong and open repository versioning with Git
▪ Freedom to custom built data models and data management
processes. Example: adopt Data Vault model.
▪ Leverage 3rd party tools and in-house standards, skills &
knowledge
▪ DevOps enabler: Continuous Testing | Integration | Deployment
Use Case
▪ Considerable share of non-SAP source systems and interfacing
▪ Specific data model requirements, for example for for auditability
▪ 3rd party DW replacement
▪ DevOps requirements
Why should you choose HANA SQL DW
SAP HANA Platform
SCHEDULING &
MONITORING
MODELING PLANNING
OLAP
LIFECYCLE
MANAGEMENT
ETL
HANA SQL DW

32. Thank you.
Sefan Linders
Data Warehouse Architect
Customer Innovation & Enterprise Platform
sefan.linders@sap.com
SAP HANA SQL Data Warehousing
Overview, Process, and Products