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Figure 1: Source: SAP

I was an attendee at this session last week. Below are my rough notes.

Figure 2: Source: SAP

The legal disclaimer applies; things in the future are subject to change

Figure 3: Source: SAP

What are challenges?

Data is around a lot of places

Sitting in storage, or sources hard to access

Now connect to cloud instances, have an API, may be tough to access

IoT – key value pairs; not nice structured

New user types - machine learning, data scientists

Challenges in data management solutions

Compliance, GDPR, regulations to fulfil

Trends – not simpler, trends are getting more complicated

Right – Gartner survey – asked customers how satisfied with data management solution - 92% say needs not covered

HANA Cloud services – market to serve this

Try to “reduce complexity”

Figure 4: Source: SAP

What is HANA Cloud Services? Technologies – HANA Cloud as the storage, federation engine, to run powerful apps on top, and connect to distributed data in organization

Middle – DWC to structure and harmonize records for a data foundation, understandable for business user, semantic layer on top

Right – once data is stored, managed, cleansed, leverage in BI apps, planning or predictive applications – SAC

3 components make up HANA Cloud Services – can be individual use cases, but whole is value

Figure 5: Source: SAP

Bring individual services together so frictionless experience

HANA Cloud – bring HANA on premise functionality to cloud

In HANA – virtualization access, SDI to any source, on premise or cloud

Connect to underlying source, cascade, first bring data in without persistence, caching the data, point to underlying system

Seamlessly switch between federation and persistence

HANA Cloud can switch between layers

Valuable data in memory or cascade down to disk level, bring capability of native storage extension

Embed relational data lake to address byte scale data

HANA core functionality in cloud, leverage scalability and elasticity from hyberscaler

Deploy using Kubernates, scaling up and scaling down

Not feasible on premise with HANA

Now in cloud, changed code line, brought functions

Figure 6: Source: SAP

Peak load, 10 or 20% storage power, leads to higher costs than expected, over size systems

Hyperscaler offerings are cheaper

Offload data from on premise to Redshift, cloud

SAP understands, work on cost side

HANA Cloud, bring software into the cloud using the elasticity and scalability that infrastructure provides, analyze workload patterns, spin up additional instances

Elasticity addresses cost aspect

Pricing model that does not have up front costs

At beginning, will be a subscription based model, put in elasticity as need it

In 2020, establish pay as you go model

Figure 7: Source: SAP

HANA Cloud (HC) can scale in multiple dimensions

Start at the bottom left

Within a single instance/node you can scale up and down the data and compute; once reach server limit, then go to upper right corner

To overcome the single instance limits there is the option to scale out across instances

Uniform scale out

Storage scale out

Upper left corner – elastic scale nodes, add additional compute power without concern for persistency

New with HC – bottom right - scale out data part individually – in memory or on disk

Figure 8: Source: SAP

You can decide if you want to keep data in memory on disk or relational data lake, or keep in S3 or Azure



Figure 9: Source: SAP

Deploy relational data lake on Sybase IQ technology; MPP installation

Similar to HANA architecture

Difference is it is a disk based database – delegate statements from HANA

Based on Kubernates, scales up very well


Figure 10: Source: SAP

Data Warehouse Cloud (DWC) is built on HC

Built based on personas

Different personas

Build solution based on personas

With pre-built adapters, using virtualization layer, use those adapters to quick connect sources and combine sources

Figure 11: Source: SAP

Make things easy so end user can leverage HC capabilities without being an expert

Governance is critical, GDPR

DWC – create spaces, motivation is to avoid offloading data from platform to bring local data next to it

Bring local data with global central data without offloading

Easy to use for LOB users

Using the BusinessObjects universe concept; universe is an abstraction layer to technical data so business user can understand the data

Embed into software

Figure 12: Source: SAP

Spaces – dedicate workspace to model and data analysis for purposes

Space is assigned to LoB users to give them the freedom to not offload data, not take care of data refresh

IT – assign quotas to space to allow you to configure compute/storage

Gives you a central monitoring for consumption, assign costs to individual businesses

Gives transparency – which area has what data, and the usage

Figure 13: Source: SAP

DWC is in beta

When GA, come up with pre-built content

Similar to BW, templates for quick start once connect to sources

Not exclusive to SAP, invite partners to the platform

SAC has a lot of content, quick start connect to DWC models in place

Figure 14: Source: SAP

Why go for DWC?

Some of you may be asking:

"More database technology from SAP? How does this fit in?”

Customers may have SAP data warehouse solutions on premise

What is value of DWC?

Figure 15: Source: SAP

3 scenarios when talk to customer

Connect DWC to on-premise systems, to connect local LoB with central governed data in BW

Modernized DW scenario

Put semantic layer on top, to be consume in BI client such as SAC or other 3rd party BI clients

Accelerate analytics – data marts – cloud instance (SaaS) to bring data to cloud, wrangle with multiple sources

Do you replace BW with DWC? SAP says no

DWC does not have all the BW features

Figure 16: Source: SAP

BI part of SAP Analytics Cloud (SAC) is already embedded in SAC


No additional user license

Depend on size of installation, will be given a few instances of SAC for free

Tool also has planning and predictive with BI

No matter which device, or user segment

Figure 17: Source: SAP

“Sweet spot” of SAC – bring ML to data, so people not familiar with ML can use it to “talk” to system in natural language

System auto generates graphic of question you have asked

Available under “search to insight”

Smart discovery – analyse any point of report, and let the system find out “root cause” of value

System generates, using ML, a story / additional insight to data point

Smart Predict – models – do not need to be a data scientist to use

Figure 18: Source: SAP

If use HC to store any kind of data, use DWC to connect data to harmonize it, users access SAC, creates value, create better decisions

Analytics and data maturity supports growth. 92% of companies with well-established analytics/BI practices have seen revenue growth of 15% or greater over the last three years.

Figure 19: Source: SAP

Not discussed, but interesting innovations

Figure 20: Source: SAP

System generates story with 4 tiles, overview, influencer, outliers, simulation

Understand what drives your business

Go beyond reviewing aggregated KPIs and look for patterns and trends that will help you make the right strategic business decisions

Figure 21: Source: SAP

Collaborative planning – HR plan, finance plan, combine together

A new planning experience

Visual formulas – code free develop planning logic, LoB users can do themselves

SAP Analytics Cloud combines Business Intelligence, Planning and Predictive Analytics in one platform

Figure 22: Source: SAP

HANA Cloud Services is the umbrella of 3 individual products to "fully harness the power of data", regardless of its location, size, or format.


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