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
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
Source: SAP
Master/orchestra - sounds by themselves may not make sense
Extract value from data, reimagine business processes
Source: SAP
Data Hub use cases
Different use cases - machine learning, data warehouses
Source: SAP
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?
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
Source: SAP
They were able to cleanse, write to HANA, SAP Analytics Cloud
Real questions received from an ASUG session:
Source: SAP
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
Source: SAP
Transitioning from multiple systems is a challenge
SAP recommends landscape transformation server, aka SLT
Source: SAP
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
Source: SAP
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
Source: SAP
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
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
Source: SAP
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
Source: SAP
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
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?
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
Source: SAP
Master/orchestra - sounds by themselves may not make sense
Extract value from data, reimagine business processes
Source: SAP
Data Hub use cases
Different use cases - machine learning, data warehouses
IoT Ingestion Data Hub Use Case
Source: SAP
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?
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
Source: SAP
They were able to cleanse, write to HANA, SAP Analytics Cloud
Real questions received from an ASUG session:
Source: SAP
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
Source: SAP
Transitioning from multiple systems is a challenge
SAP recommends landscape transformation server, aka SLT
Source: SAP
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
Source: SAP
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
Source: SAP
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
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
Source: SAP
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
Source: SAP
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
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?
- SAP Managed Tags:
