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I have SAP HANA, why would I need a data orchestration solution like SAP Data Hub Webcast Recap

I have SAP HANA, why would I need a data orchestration solution like SAP Data Hub? – This was the title of the webcast and a common question that SAP receives

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Source: SAP

Data Hub sits between digital platform and intelligent technologies

Data Hub is a “hub” – a hub of data orchestration, not data storage

Can Data Hub store data? Yes, but not location where data resides

Data comes in, refine it, and go somewhere

It is there to support the processing itself, and moves to its final destination

Sweet spot – extract value of data that you didn’t have before

Assume data is in various locations; get data out from various locations

Discover data where it is; metadata cataloging, refine it, governance on metadata

Orchestration – where it needs to

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Master/orchestra – sounds by themselves may not make sense

Extract value from data, reimagine business processes

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Data Hub use cases

Different use cases – machine learning, data warehouses

IoT Ingestion Data Hub Use Case

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Company makes dishwashers

Collecting machine sensor data

See how people use dishwashers

Data is in data lake

Once in a data lake, how manage? Streaming data hard to understand, use, no data quality

How take data lake and connect to enterprise data?

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Source: SAP

Data Hub can connect to data lake, and connect to enterprise data, lets analyst see where customers use the dishwasher and help create a better product

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They were able to cleanse, write to HANA, SAP Analytics Cloud

Real questions received from an ASUG session:

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Hub is not where store data, but orchestrate data, extract value from data

When you are migrating to S/4HANA – prepare data, cleanse, standardize data, and hopefully implementing SAP Master Data Governance solution.

For migration, SAP Data Hub is not required

SAP has specific solutions for S/4HANA migration

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Source: SAP

Transitioning from multiple systems is a challenge

SAP recommends landscape transformation server, aka SLT

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Whenever you are writing code in different languages, becomes a challenge

This is where Data Hub is a good fit

Enclose the environment in docker containers

SAP Data Hub orchestrates work across containers

Can use Data Hub data catalog capabilities and metadata

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Not getting rid of smart data integration

When customers are using SDI to load to SAP HANA

Could use Data Hub for the orchestration

Could extend with SAP Data Hub if pulling from other systems such as pre-processing

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Good use case for Data Hub

Another use case is a European railroad; added sensors to moving parts, to monitor what is happening with the parts – when replacement or repair

Used Data Hub to streamline data collection process

Embed machine learning at critical data points

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Source: SAP

Good use case for Data Hub

Users can call machine learning models that Data Scientists are creating

Can access the source data to support data scientists

Data scientists can access R, Python, etc. and allow them to operationalize daily activities

Recently announced Data Intelligence (cloud version of Data Hub) with additional machine learning capabilities including Jupyter notebooks

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SAP says this is a powerful use case for Data Hub

If have multiple data sources, and need to correlate data, and comments from social media, Data Hub can correlate and curate text analysis against body of text to find critical information (type of comment, type of product, what were they expressing

Can feed results back to analytics environment

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Data Hub can call the text analysis engine in SAP HANA

Data Hub can use the HANA engines

Data Hub is a separate component from HANA

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Source: SAP

Summary of this session.

You can register for the replay here.

I liked the sharing of commonly asked questions.  What do you think?

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