Recap

SAP Analytics Cloud makes it easy for businesses to understand their data through its stories, dashboards and analytical applications. Our worked example is using SAP Ariba Data to create an SAC Story that lets you know how much spend has been approved within Ariba Requisitions created in the last thirty days

In Part One of our blog series we discussed how we can retrieve data from SAP Ariba’s APIs using SAP Data Intelligence Cloud. We stored this data as JSON Documents in the SAP HANA Document Store

In Part Two of our blog series we built SQL and Calculation Views on top of our JSON Document Collection

In this blog post we’ll use the Calculation View in SAP Analytics Cloud as a Live Data Model, which will provide the data to our SAP Analytics Cloud Story

Accessing our HDI Container from SAP Analytics Cloud

Before we can consume our Calculation View in SAP Analytics Cloud, we’ll need to connect SAC to our HDI Container. We can do this from within SAC itself

Next, we’ll have to enter the host and credentials for our HDI Container. If you’re not sure where to find these, refer to Part One of this blog series where we retrieved these for Data Intelligence Cloud (under the heading Creating Our Connections in Data Intelligence)

Our HANA Cloud Connection has been created and now we’re ready to create in SAC

Creating a Live Data Model

Within SAP Analytics Cloud we’re going to use a Live Data Model to access our Calculation View in real time. This means that the data in our Document Store Collection will be available immediately after our Data Intelligence Pipeline updates it

Another benefit of using this Live Data Model compared to creating a Model on Acquired Data is that data doesn’t need to be copied to SAP Analytics Cloud for use

Now we’re looking at the Live Data Model in the SAP Analytics Cloud Modeler. We can see our Calculated Measure, ReportingAmount

We can also check the Live Model’s Dimensions

Now that we’ve got our Live Data Model, we’re ready to create our Story and visualize our Ariba Data

Creating an SAC Story

Stories within SAP Analytics Cloud let us use visualize data in a number of ways, including charts, visualizations and images

Within a Story there are a number of different Page Types available. For our example we’re going to add a Responsive Page. A Responsive Page allows you to create layouts that resize and adapt when viewed on different screen sizes

First we’re going to give our Page a title – for example: Approved Requisitions (Past 30 Days)

Next, we’re going to attach the Live Data Model we created to our Story

Now we’re able to use the data from our Live Data Model in our Story

Now it’s time to give our chart some data

Now we can see a sum of all of our Approved Requisitions. However, we may (or rather we probably will) have Requisitions in more than one currency. To separate these Requisitions we’ll need to use Chart Filters

From here we’ll be able to select which values of ReportingCurrency we’d like to see reflected in our ReportingAmount total. Given that it doesn’t make sense to sum totals in different currencies without first converting them, we’re going to select only a single currency

The data in your system may have different currencies than mine, so feel free to adjust accordingly

While it’s great to have our total, the average end user is not going to know what ReportingAmount means. It’s time to give our Numeric Point a meaningful label

At this point, we have our Numeric Point set up and ready to go

If your system only has the one currency, you can leave it here. If your system has more than one currency, you can duplicate the Numeric Point, then change the Chart Filter and Chart Title using the same steps we just followed

Once we’ve finished with our currencies, it’s time to save

Our Story is now finished and ready to be viewed

Sharing our SAP Analytics Cloud Story

Now we’ve created our Story, but we don’t want it to just sit inside our Files on SAP Analytics Cloud – we want people to use it. Let’s share our story with our colleagues

Now we’ve shared our Story with users, and decided what kind of access we’d like them to have. This isn’t the only type of sharing available in SAP Analytics Cloud – for example you can learn about publishing to the Analytics Catalog here, and read about your other options in the Help Documentation

Scheduling the Data Intelligence Pipeline

Now that our setup work is done and the users can view our SAP Analytics Cloud Story, we want to make sure the underlying data in our SAP HANA Document Store Collection is kept up to date on a regular basis

Since our SAP Data Intelligence Pipeline is responsible for truncating the data and supplying a new batch of data, we want to schedule it to run automatically. We can do this from Data Intelligence Cloud itself

When we create our first Schedule, we’ll see My Scheduler Status: Inactive. We don’t need to worry – our Scheduler’s Status is actually Active. To see it, we can click on Refresh

An Important Note on Scheduling Ariba APIs

You may remember back in our first Blog Post that our Pipeline waits twenty seconds between each call of the Ariba APIs. This is because each of Ariba’s APIs has rate limiting. These rate limits are cumulative

What that means for us is that for each Realm and API (for example MyCorp-Test Realm and Operational Reporting for Procurement – Synchronous API) we have a shared rate limit, no matter how it’s called

The Pipeline we provided in Part One of this blog series is optimized for performance – i.e. it makes calls as fast as Ariba’s rate limiting will allow

If there’s more than one instance of this Pipeline running at once, both will receive a rate limit error and no data will be uploaded to our Document Store Collections

Please keep this in mind when you plan for the scheduling of these pipelines, and refer to the Ariba API Documentation for up-to-date information on Ariba Rate Limits as well as how many Records are returned each time the API is called

Wrap-Up

Throughout this blog series we’ve shown how we can set up a pipeline that will get data from Ariba and persist it in HANA Cloud Document Store, as well as how we can schedule it to run periodically

We’ve also shown how we can create a Calculation View on top of these JSON Documents, and finally how we can create a Story in SAP Analytics Cloud that will let us visualise our Ariba Data

The data and models in these blog posts have been rather simple by design, in a productive scenario we will likely want much more complex combinations and views.

The post Ariba Analytics using SAP Analytics Cloud, Data Intelligence Cloud and HANA DocStore – Part 3 first appeared on ERP Q&A.

Recap

SAP Analytics Cloud makes it easy for businesses to understand their data through its stories, dashboards and analytical applications. Our worked example is using SAP Ariba Data to create an SAC Story that lets you know how much spend has been approved within Ariba Requisitions created in the last thirty days

In Part One of our blog series we discussed how we can retrieve data from SAP Ariba’s APIs using SAP Data Intelligence Cloud. We stored this data as JSON Documents in the SAP HANA Document Store

In this blog post, we’re going to build SQL and Calculation Views on top of our JSON Document Collection

Design-Time Artifacts in Business Application Studio

As we discussed in our last blog post, objects within HANA usually have both a design-time and runtime artifact. Design-time artifacts are useful because they fully describe the object and can be deployed consistently across multiple HDI Containers or even HANA instances

Our JSON Document Collection has already been created, and is already storing our Ariba JSON Documents. From here, it’s time to model our other artifacts

Creating our SQL View

JSON Documents are useful in a variety of situations where you don’t have strict, predefined schemas. When we retrieve our data from the Ariba APIs, we may retrieve data that doesn’t map cleanly to a table schema (for example, data that is nested). Putting this data in the HANA DocStore Collection allows us to store the complete document, ensuring nothing is lost

In order for us to use this data for analytics, we’ll need to map it to some sort of schema. We can create a logical schema on top of our Collection using a SQL View. This allows us to access a predefined subset of our data for analytics while leaving the full data untouched in our Collection

We’ll create the SQL View in Business Application Studio

SQL Views use the following format:

VIEW "aribaRequisitionSQLView"
AS SELECT "UniqueName", "Name", [...]
FROM "aribaRequisition"

If you’re familiar with SQL, you may recognise this as the same syntax that you would use to create a standard SQL View, just missing the word “CREATE”

The SQL View doesn’t duplicate any data, just provides a schema that we can use to access the underlying data

Our data in JSON Documents are Key-Value pairs

"Status":"Complete"

To retrieve the value “Complete”, we would SELECT “Status”

JSON Documents may also have nested data

"Content":{"ItemId":"3728754507"}

To retrieve the value “3728754507”, we would SELECT “Content”.”ItemId”, with the full stop marking nested keys

Our example will use the following SQL View:

VIEW "aribaRequisitionSQLView"
AS SELECT "UniqueName", "Name", 
"TotalCost"."AmountInReportingCurrency" AS "AmountInReportingCurrency", 
"ReportingCurrency"."UniqueName" AS "ReportingCurrency", 
"ApprovedState", "Preparer"."UniqueName" AS "Preparer",
"Requester"."UniqueName" AS "Requester", "StatusString", 
"CreateDate", "SubmitDate", "ApprovedDate", "LastModified", 
"ProcurementUnit"."UniqueName" AS "ProcurementUnit"
FROM "aribaRequisition"

The fields we’re using are only a fraction of the fields available in the Documents within our Collection – if we want to customize the scenario later, there are plenty more to choose from

We want to make sure this SQL View is deployed and ready for use, so click on the Deploy rocket

Creating our Calculation View

While we’re in Business Application Studio, we’re going to create our Calculation View. This Calculation View is what we’ll be consuming in SAP Analytics Cloud

As before, we’re using View->Find Command then Create SAP HANA Database Artifact

Business Application Studio has an inbuilt editor for Calculation Views, which we’ll use to create ours

Now that our SQL View is available as a Data Source, we want to make sure its columns end up in our Calculation View

Because this is a Calculation View of type Cube (rather than Dimension), we’ll need to make sure it includes at least one Measure

The columns in our SQL View all have the default data type NVARCHAR(5000). If we try to mark this column as a Measure directly, it will treat it as a string – giving us the Aggregation options COUNT, MIN and MAX

We want to treat this column as the number it is – as a workaround, we’ll need to create a Calculated Column

Creating our Calculated Column

A Calculated Column is an output column that we create within the Calculation View itself. Rather than being persisted, the values are calculated at runtime based on the result of an expression

For our example, we’re using a very simple expression. First, we have to make our way to the Expression Editor

Next we’re going to give our Calculated Column a name and data type. Because the granularity of our example is the Requisition-level and not the item-level, the decimal points won’t meaningfully change the results. Given that, we’re going to use the Integer data type

The Expression Editor is where we’ll define how the column is calculated. Select our AmountInReportingCurrency Column

Our Created Column will take the value of AmountInReportingCurrency and convert it to an Integer

Now we want to validate the syntax of our Expression

We have one last thing to do inside our Calculation View – we want to filter the data to only include Approved Requisitions. If we want to use the Value Help to set our Filter, we’ll need to Deploy the Calculation View

Now we can check the syntax of our Filter

Before we Deploy our Calculation View, we want to make sure that we’re only sending our integer Calculated Column and not the string version. To do this, we go the Semantics Node

All of the Columns we need, including our new Calculated Column are available within the Calculation View. Now we’re ready to Deploy it one last time

Checking our Runtime Artifacts

Now that we’ve finished deploying our Design-time artifacts, we’ll have the corresponding Runtime artifacts inside of our HDI Container. We can check these by going to SAP HANA Database Explorer from within Business Application Studio

In the Database Explorer, we want to first check on our SQL View

We can see all of the Columns in our created SQL View. If we want to check out some of the data returned by our SQL View, we can click on Open Data

Next it’s time to check on our Calculation View

Database Explorer will open our Calculation View for Analysis. We’re going to do our analysis in SAP Analytics Cloud, so for now we just want to verify the Raw Data

Wrap-Up

During this blog post we’ve built a SQL View and Calculation View on top of our HANA DocStore Collection. We’ve also made sure that our Calculation View only contains Approved Requisitions

In the third and final blog post we’ll consume our Calculation View as a Live Data Model before visualizing it in an SAP Analytics Cloud Story. We’ll also schedule the Data Intelligence Pipeline we created in our first blog post so that the data in our HANA DocStore Collection is updated on a regular basis automatically

The post Ariba Analytics using SAP Analytics Cloud, Data Intelligence Cloud and HANA DocStore – Part 2 first appeared on ERP Q&A.

SAP Analytics Cloud makes it easy for businesses to understand their data through its stories, dashboards and analytical applications. However, sometimes we might not be sure how we can leverage SAC to create these based on data from other applications

For this worked example, we’re going to make use of SAP Data Intelligence Cloud to retrieve data from SAP Ariba through its APIs, before storing it in SAP HANA Cloud’s JSON Document Store

In further blog posts, we will build a model on top of this stored data and show how this can be consumed in an SAP Analytics Cloud Story. The focus of this series of blog posts is to show a technical approach to this need, not to provide a turnkey ready-to-run SAC story. After following this series you should have an understanding of how you can prepare your own stories using this approach

Ariba Analytics in SAP Analytics Cloud

For this example, we’re going to create a simple story that lets you know how much spend has been approved within Ariba Requisitions created in the last thirty days

A Requisition is the approvable document created when a request is made to purchase goods or services. Our approach will let us view only the Approved Requisitions, excluding those still awaiting approval

For those feeling more adventurous, this setup can be repeated with different document types, and those combined to create more in depth SAP Analytics Cloud Stories. This is outside of the scope of our blog series

Solution Overview

Our finished solution will need SAP HANA runtime artifacts such as Document Store Collections, SQL Views and Calculation Views. We will define these as design-time artifacts in Business Application Studio, then deploy them to an HDI Container within our SAP HANA Cloud instance

Using a scheduled SAP Data Intelligence Cloud Pipeline, we’ll query SAP Ariba’s APIs and place the data within our HANA Cloud Document Store Collection

Our SQL View lets us create a view on top of the data within our JSON Documents. Creating a Calculation View on top of one or many SQL views will let us expose the data to SAP Analytics Cloud

SAP Analytics Cloud can use HANA Cloud Calculation Views as the source for Live Data Models. With Live Data Models, data is stored in HANA Cloud and isn’t copied to SAP Analytics Cloud

This gives us two main benefits: We avoid unnecessarily duplicating the data, and ensure changes in the source data are available immediately (provided no structural changes are made)

Finally, we use the Live Data Model to create a Story within SAP Analytics Cloud. Once we’ve got everything set up, we can use this story to check our data at any time, with the Data Intelligence Pipeline refreshing the data in the background on a predefined schedule

Creating an Ariba Application

In order to access the APIs provided by Ariba, we’ll need to have what’s known as an Ariba Application. We do this through the SAP Ariba Developer Portal

For our use case we will be requesting access to the Operational Reporting for Procurement API

Once the Application has been created, we’ll need to request API access for the Application

Once the API Access Request has been approved by Ariba, your admin will be able to generate the OAuth Secret for our application

This will generate our OAuth Secret, which is required to use the API. The secret will only be displayed once, so the admin should (securely) store this and provide it to you for use in the application

If the OAuth Secret is lost, the admin can regenerate it, at which point the old secret will stop working and you will have to use the newly generated secret

Ariba API

When we call the Ariba API, we have a number of things to consider. For our example, we’re using the Synchronous API to retrieve data, but there’s also a set of Asynchronous APIs you should consider when retrieving bulk data

In addition, when retrieving data sets, you have to specify an Ariba View that you wish to retrieve. These are similar to reporting facts in the Ariba solution, such as Requisition or Invoice. Views will specify which fields are returned, and may also specify filters you should provide when calling them

To simplify our example we’re going to use a System View, which is predefined in Ariba. You are also able to work with Custom Views using the View Management API to better match your requirements but this falls outside the scope of this blog series

To explore these at your own pace, you can visit developer.ariba.com

Enabling Document Store in HANA Cloud

The Document Store is SAP HANA Cloud‘s solution for storing JSON Documents. While the Column and Row Stores use Tables to store their data, the Document Store stores data inside Collections

Before we activate the Document Store in HANA Cloud, just a word about resources. Like the Script Server, the Document Store is an additional feature that can be enabled, however we should consider HANA Cloud’s current resourcing before enabling it.

When we’re ready to enable, we’ll need to navigate to SAP HANA Cloud Central.

From HANA Cloud Central, we can then activate the Document Store

Once our HANA Cloud instance has restarted, we’ll be able to use the Document Store

Creating a DocStore Collection in Business Application Studio

While we can create a Collection directly using SQL through Database Explorer, we want to make sure we also have a design-time artifact for our DocStore Collection

To do this, we’ll use the Business Application Studio. For those unfamiliar with Business Application Studio, you can follow this Learning Journey Lesson to set up a Workspace – we’ll assume this is already in place

It’s time to set up our SAP HANA Database Project, and create the HDI Container where our runtime objects will reside

Next we’ll need to provide some information for our project

Double check the Database Version and Binding settings then click Next

Next we have to bind the project to a HANA Cloud instance within Cloud Foundry. The Endpoint should be automatically filled, but we have to provide our Email and Password before we can perform the binding

For this example we’re going to create a new HDI Container

If our Cloud Foundry space has more than one HANA Cloud instance, we may want to disable the default selection and manually choose the HANA Cloud instance where our container will reside

Now that we have our HDI Container and SAP HANA Project set up, it’s time to create our design-time objects. First, we login to Cloud Foundry

Next, we’ll create our DocStore Collection

After the deployment is successful, we have both our design-time .hdbcollection artifact, as well as the runtime DocStore collection which has been created in our HDI Container

Creating our Connections in Data Intelligence

So far we’ve gained access to Ariba APIs and enabled the Document Store in our HANA Cloud Instance. Next, we’ll be setting up two Connections in Data Intelligence Cloud

The first Connection will allow our Data Intelligence Pipeline to query the Ariba APIs to retrieve our data, and the second will allow us to store this data in the Document Store within our HDI Container

First, we use the DI Connection Manager to create a new Connection, selecting OPENAPI as the Connection Type

Our OpenAPI Connection will be used to send the request to Ariba. We’re going to set the connection up as below, using the credentials we received when we created our Ariba Application

Next, we’re going to create a HANA Connection that will let us work with the HDI Container we created earlier. To get the credentials, we have to go to the BTP Cockpit

We’ll want to keep this window open as we create our HANA DB Connection, as it has the details we need. Within Data Intelligence Cloud, create a new connection of type HANA_DB and fill it out as below using the credentials

While we have the credentials open, take note of the Schema name. We’ll need this to set up our pipeline

Pipeline Overview

The source code for our pipeline can be found. Copy the contents of this JSON to a new Graph within the Data Intelligence Modeler. If you’re not familiar with how to do this, you can refer to the README

When the pipeline starts, a GET request is made to the Ariba API. If there are more records to be fetched, the pipeline will make further requests until it has all available data. To avoid breaching Ariba’s rate limiting, there is a delay of 20 seconds between each call

Once all of the records have been fetched, the Document Store Collection is truncated to remove outdated results, and the most up to date data is inserted into our collection

1. A copy of the data is stored as a flat file in the DI Data Lake as reference
   
2. The HANA Document Store Collection is truncated, and Documents are added to the Collection one at a time
   
3. Once all records have been added to the Collection, the Graph will be terminated after a configurable buffer time (1 minute by default)

Configuring our Pipeline

In order to run this pipeline, you will have to make some changes to the pipeline:

In the Format API Request Javascript Operator, you should set your own values for openapi.header_params.apiKey and openapi.query_params.realm

If your Connection names are different to ARIBA_PROCUREMENT and ARIBA_HDI, then you will want to select those under Connection for the OpenAPI Client and SAP HANA Client respectively

Check the Path values for the operators “Write Payload Flat File” and “Write Error Log”. This will be where the pipeline will write the Flat File and API Error Logs respectively. If you’d like them to save elsewhere, edit that here

Finally, we’ll want to set the Document Collection Schema name in the DocStoreComposer Operator. This is the Schema we noted earlier while setting up the Connections

Testing our Pipeline

Now we’re ready to test our pipeline. Click on Save, then Run

Once our pipeline has completed successfully, we’ll be able to see that our JSON Documents are stored within our Collection by checking in the Database Explorer. We can access this easily through Business Application Studio by clicking the icon next to our SAP HANA Project

We can see that our pipeline has been successful, and that 206 JSON Documents have been stored in our Collection

Wrap-Up

In this blog post we’ve walked through how we can use SAP Data Intelligence Cloud to extract data from SAP Ariba, before storing it in a collection in SAP HANA Cloud’s Document Store

The post Ariba Analytics using SAP Analytics Cloud, Data Intelligence Cloud and HANA DocStore – Part 1 first appeared on ERP Q&A.