celonis intelligent business cloud

CELONIS INTELLIGENT BUSINESS CLOUD

Data Engineer Online Training

IBC Data Engineer Training

2

1. Infrastructure – Understand the basics of the Event Collection

1. Which systems can be connected to the Intelligent Business Cloud?• On-premise source systems [true]• Cloud systems [true]• On-premise databases [true]

2. Why is a Connector needed to facilitate the connection between an on-premise source system and the Intelligent Business Cloud?• On-premise systems typically can’t communicate with external applications. [true]• The Intelligent Business Cloud can’t communicate directly with other systems. [false]• The Connector allows for a more stable and performant way to extract data. [true]

3. Which of the following statements is/are true?• The Connector can synchronize data with the Intelligent Business Cloud in an asynchronous way. [true]• A dedicated Connector server is needed to connect the Intelligent Business Cloud to another Cloud system. [false]• To connect to an On-premise source system a Connector needs to be set up in the network of the Intelligent Business Cloud. [false]


3

2. Connect to source system (1/2)

1. Access your training team in the Intelligent Business Cloud as described in the video and navigate to Event Collection. The source systemwe are using in this training is a MS SQL database. Please follow the instructions below step by step to connect to the training sourcesystem:

• Create a new Data Pool and give it a proper name.• Enter the newly created Data Pool and click on “Data Connections”• Create a new Data Connection.• Choose the on-premise database connector.• Give your connection a proper name.• Insert the following Connection Details:

• Connection type: Direct• Type: Microsoft SQL Server (native)• Server Name: 3.120.99.40• Port: 1433• Database Name: celonis• Maximum Parallel Table Extractions: 1• Schema Name: Training• Username: Training_CelonisDataEngineer• PW: Celonis123!

• Click on “Save”.


Access the Event Collection…

1

… and create a new Data Pool.

2


Name it accordingly…

1

… and click on “Save”.

2


Go to the data connections.


Click on “New Data Connection”.


Create the connection to the training database.


Give the data connection a

unique name…

1

… and choose “Microsoft SQL Server (native)”.

2


Enter all given information…


2


The connection has been set up properly.

C


12

2. Connect to source system (2/2)

2. Which of the following statements is/are true?

• Connections are always secured via HTTPS. [true]• Celonis always performs read-only operations on other systems. [true]• Data Connections are the main structural element in the Event Collection which structure all other elements like Data Pools. [false]


13

3. Get to know the training data (1/2)

For a great overview of all SAP tables, visit the following websites: http://leanx.eu/en/sap/table/search or http://www.sapdatasheet.org/abap/tabl/

1. Which information does the table EKKO contain?• Purchase Requisition Items: Shows what (material, quantity,…) has been requested. [false]• Purchasing Document Header: Shows which purchase orders have been created. [true]• Purchasing Document Item: Shows which purchase order items have been created. [false]• Vendor Master: Shows which suppliers are being used. [false]

2. Which information does the table EBAN contain?• Purchase Requisition Items: Shows what (material, quantity,…) has been requested. [true]• Purchasing Document Header: Shows which purchase orders have been created. [false]• Purchasing Document Item: Shows which purchase order items have been created. [false]• Vendor Master: Shows which suppliers are being used. [false]

3. Which table contains information about the vendors?• EKKO [false]• LFA1 [true]• EBAN [false]• EKPO [false]

4. What is the purpose of a primary key?• The primary key is a field that uniquely identifies each record in a database table. [true]• The primary key connects different tables in a database. [false]

http://leanx.eu/en/sap/table/search

http://www.sapdatasheet.org/abap/tabl/


14

3. Get to know the training data (2/2)

5. Which fields are the primary keys of table EKKO?• MANDT (Client) [true]• LIFNR (Vendor Account Number) [false]• BUKRS (Company Code) [false]• EBELN (Purchasing Document Number) [true]

6. Which tables can be used for translating the technical table and column names?• CDPOS [false]• CDHDR [false]• DD02T [true]• DD03M [true]

7. When changing both the quantity and the price of a purchase order item and saving the changes with one click, how many rows arecreated in CDPOS and CDHDR?• One in CDPOS, one in CDHDR [false]• Two in CDPOS, one in CDHDR [true]• One in CDPOS, two in CDHDR [false]• Two in CDPOS, two in CDHDR [false]


15

4. Extract data – Understand Event Collection Structure

1. Which of the following statements is/are true?• Inside a single Data Job you can create multiple execution and transformation tasks. [true]• One Data Job can host multiple Data Connections. [false]• The main structural element of the Event Collection is the Data Pool. [true]

2. Which of the following statements is true?• The general methodology of connecting a process differs considerably for different processes and source systems. [false]• The general methodology of connecting a process is applicable to any process and source systems. [true]


16

5. Extract data – Create Extractions (1/5)

Enter the data pool you created in the Event Collection and follow the instructions to create your first data job:• Navigate to “Data Jobs” and click on “New Data Job”.• Give the job a proper name and select the Data Connection you set up previously.• Click on “New Extraction” and give your extraction a proper name.• Add the following tables to your extraction and click on “Save”:

• EKKO• EKPO• EBAN• LFA1• CDHDR• CDPOS• DD02T• DD03M

1. Which of the options are available to configure the extraction for each table?• Option to only extract specific columns [true]• Option the pseudonymize specific columns [true]• Option to change the data type during the extraction [true]• Option to set a filter statement to restrict the data extract [true]


Navigate to “Data Jobs”…

1

… and click on “New Data Job”.

2


Give it a proper name …

1

… and select the Data Connection you set up

previously.

2


Let’s create a new extraction task.


Name the extraction task accordingly and

click on “Save”.


Click on “Add Tables” to choose the tables from the connection

that you want to extract.


Choose the tables listed in the exercise…

1

… and click on “Save”.2


On the left you can see all the tables you

selected before. Let’s move on and define the filters for the extraction!

C


24


Specify the “Filter Statements” for each of the tables as described below:

• EKKO• Filter Statement:

AEDAT >= 01.06.2014 00:00• EKPO

• Join table EKKO• Custom join condition:

EKPO.MANDT = EKKO.MANDT ANDEKPO.EBELN = EKKO.EBELN

• Filter on joined table:EKKO.AEDAT >= ‘01.06.2014’

2. Why does it make sense to join table EKKO in the extraction of table EKPO?• If we do not join the tables, they can’t be connected in our data model later on. [false]• Because we only want to extract Purchase order items for which we also extracted the corresponding purchase order. [true]


The first table we want to adjust is

EKKO. Click on it …

1

… and open the time filter.

2


Activate the creation date filter …

1

… and choose the field AEDAT.

2


Enter the date that is given in the exercise.


Make sure to save these settings before going to

the next table!


Choose table EKPO …

1

... and tick the box to join this table

with EKKO.

2


Click on “Add Join”.


Enter the table EKKO…

1

… specify the join condition…

2

… enter the same time filter as before…

3


4


32



• EBAN• Filter Statement:

BADAT >= 01.04.2014• LFA1

• No configuration needed→ Extract full table

3. Why do we use a different date for the Filter Statement of table EBAN compared to the Filter Statement of table EKKO?

• Purchase Orders which were created on the 01.06.2014 most likely have a purchase requisition which was created on an earlier pointin time. This is why we extract all Purchase Requisitions which were created two months in advance of our first Purchase Order aswell. [true]

• It is always better to extract as much data as possible to keep the load times at a minimum. [false]

4. Why don’t we apply a Filter Statement for table LFA1?

• LFA1 doesn’t contain a date on which the table records were created. [false]• LFA1 is the Vendor Master table and it doesn’t make sense to filter on the creation date of the table records. [true]


The next table we configure is EBAN.

1

We can use the creation date filter again …

2

… and choose the field BADAT.

3


Enter the correct date …

1


1


We can leave the settings for LFA1 as they

are as we want to extract the whole master

data table.

C


36



• CDHDR• Filter Statement:

UDATE >= 01.06.2014• CDPOS

• Custom join condition:CDPOS.MANDANT = CDHDR.MANDANT ANDCDPOS.OBJECTCLAS = CDHDR.OBJECTCLAS ANDCDPOS.OBJECTID = CDHDR.OBJECTID ANDCDPOS.CHANGENR = CDHDR.CHANGENR

• Filter on joined table:• CDHDR.UDATE >= ‘01.06.2014’

5. Why do we set up the Filter Statement CDHDR.UDATE >= ‘01.06.2014’?

• Because we only want to extract Changes which occurred after or on the 01.06.2014. [true]• Because we only want to extract Purchase Orders which went through at least one change. [false]


Click on CDHDR … 1

… enable the creation date filter …

2

… and choose UDATE.

3


Always make sure to click on “Save”.


Open the configuration of CDPOS …

1

… tick the box …2

… and add the join.

3


Enter the name of the joined table, the

join condition and the date filter

statement as before.


Don’t forget to click on “Save”.


42



• DD02T• No configuration needed→ Extract full table

• DD03M• No configuration needed→ Extract full table

6. Which of the following statements is/are true?

• Name Mapping tables like DD02T and DD03M are one of the larger tables in a source system and typically take a long time to extract. [false]• Name Mapping tables like DD02T and DD03M are one of the smallest tables in a source system and typically take only a few seconds to

extract. [true]

7. Exit the extraction task and execute the entire Data Job! Which information can you derive from the logs?

• Execution Date [true]• Load time [false]• Schedule Name [true]• User [false]• Status [true]


For both DD02T and DD03M we don’t have

to set any filters …

1

… so let’s go back to the data job overview.

2


Click on “Execute Data Job”.


Make sure to reload all data …

1… and press the

execution button.

2


In the logs you can monitor the execution of

every task.

C


47

6. Extract data – Extraction Logic

1. Which of the following statements is/are true for the communication between the Intelligent Business Cloud and an on-premise sourcesystem?• Every source system uses the same export mechanism. [false]• The on-premise connector transforms all files into parquet files before it sends the files to the Intelligent Business Cloud. [true]• The communication between the systems always starts with the on-premise connector establishing a connection to the Intelligent

Business Cloud. [true]• The Intelligent Business Cloud can directly communicate with an on-premise source system. [false]

2. Which of the following statements is/are true for the communication between the Intelligent Business Cloud and another cloud system?• The Cloud Connector of the Intelligent Business Cloud establishes a connection to the other Cloud System via public API endpoints of

the Cloud Service. [true]• The Data Engineer defines which tables to extract in the Cloud Connector interface. [false]• The Celonis data storage transforms all files to parquet files. [false]


7. Understand the concept of the Activity table

1. Is the Activity Table mandatory for process mining?• Yes [true]• No [false]

2. Which columns could reasonably be added to the Activity Table?• Sorting [true]• User type [true]• Purchase quantity [false]• Vendor information [false]• General arbitrary activity-specific attributes [true]

3. Which component(s) does the Celonis data model consist of?• Only the Activity table [false]• Multiple Activity tables from different source systems [false]• Only master tables [false]• One Activity and arbitrarily many master data tables [true]


49

8. Validate the data extract

Before we continue with creating the activity table, we quickly want to validate the data you extracted earlier. Create a new transformation taskand get familiar with the SQL workbench. To validate the data, please count the records of every of your extracted tables.

Hint: You can use the SQL statement “SELECT COUNT (*) FROM TABLENAME”;

1. How many table records does the table EKKO contain?• 28500 [false]• 14067 [true]• None of the numbers above [false]

2. How many table records does the table EKPO contain?• 35820 [false]• 28360 [true]• None of the numbers above [false]

3. How many table records does the table EBAN contain?• 137 [true]• 679 [false]• None of the numbers above [false]


Click on “New Transformation”.


Give the task a proper name …

1


2


Insert the SELECT COUNT(*) FROM statement to count the entries in

the respective table.


Select the line and click on “Execute”.


The exact number is displayed here.


Adjust the statement for EKPO and execute it again.

C


Adjust the statement for EBAN and execute it again.

C


57


4. How many table records does the table LFA1 contain?• 2058 [true]• 5280 [false]• None of the numbers above [false]

5. How many table records does the table CDHDR contain?• 23004 [false]• 17002 [true]• None of the numbers above [false]

6. How many table records does the table CDPOS contain?• 34648 [true]• 45203 [false]• None of the numbers above [false]

7. How many table records does the table DD02T contain?• 18 [true]• 20 [false]• None of the numbers above [false]


Adjust the statement for LFA1 and execute it again.

C

Adjust the statement for CDHDR and execute it again.

C

Adjust the statement for CDPOS and execute it again.

C

Adjust the statement for DD02T and execute it again.

C


62


8. How many table records does the table DD03M contain?• 1474 [true]• 2100 [false]• None of the numbers above [false]

In case you got one of the questions wrong, please use the solutions in the next section to correct your extraction task and run the extractionagain. When running the extraction again, please make sure to activate the toggle “Reload all data”. By activating this toggle all existing data isdeleted and you won’t receive any duplicates.


Adjust the statement for DD03M and execute it again.

C


64

9. Transformation - Build up Activity Table (1/4)

Please delete the transformation task you created to validate the data and create a new transformation task called “Create Activity Table”.Open the attachment SQL code and copy it to your transformation, to create an Activity Table. Run the code.

1. When creating an Activity Table, which fields must always be included?• Case Key [true]• Sorting [false]• Activity name [true]• Event Time [true]• Change Information [false]


Open the menu for the validation task.


Click on “Delete”.


Let’s now create a new transformation.


Give it a proper name …

1


2


Insert the SQL Code from the attachment.


Select the lines and click on “Execute”.


You can see that the Activity Table has been

created successfully.


If you click on the “Refresh” button, …

1

… you can see the table and all its columns in

your Schema Explorer.

2


73


Create a second transformation task and name it “Insert Activity: Create Purchase Requisition Item”. Open the attachment, which contains thecode to create the activity Create Purchase Requisition, and paste it into the transformation. Run the code and have a look at the output.

2. How many activities were added to your activity table?

• 136 [true]• 2340 [false]• 417 [false]• None of the above [false]

3. Now, create a new transformation for the activity Create Purchase Order Item. Use the code for the first activity as guideline. Which tablesdo you need for the activity "Create Purchase Order Item"?

• EKPO [true]• EKKO [true]• LFA1 [false]• EBAN [false]


Let’s create a new transformation.


Insert a name …1


2


Insert the SQL Code from the attachment.


Select the lines and click on “Execute”.


The code has been executed successfully and 136 rows have

been added to the Activity Table!


79


4. Now, we need to join the tables EKKO and EKPO. Which are the foreign keys to join the tables?• Vendor Number (EKPO.LIFNR = EKKO.LIFNR) [false]• Client (EKPO.MANDT = EKKO.MANDT) [true]• Purchase Order Item (EKPO.EBELP = EKKO.EBELP) [false]• Purchase Order Number (EKPO.EBELN = EKKO.EBELN) [true]

5. Is it necessary to include any restrictions (WHERE clause) when adding the activity "Create Purchase Order Item"?• Yes. The purchasing document category needs to be a purchase requisition (EKKO.BSTYP='B’) [false]• Yes. The purchasing document category needs to be a purchase order (EKKO.BSTYP='F’) [true]• No. No restrictions are needed. [false]

6. Which fields should be selected as Case Key for the activity "Create Purchase Order Item"?• LIFNR (Vendor Number) [false]• MANDT (Client) [true]• EBELN (Purchasing Document Number) [true]• EBELP (Purchasing Document Item Number) [true]

7. Which field should you select as event time for the activity "Create Purchase Order Item"?• FRGDT (Purchase Requisition Release Date, table EBAN) [false]• ERDAT (Date on which vendor record was created, table LFA1) [false]• AEDAT (Date on which purchase order item record was created, table EKKO) [true]• BEDAT (Purchasing Document Date, table EKKO) [false]


80


8. Given the current sorting 10 for "Create Purchase Requisition Item", which sorting value is appropriate for the activity "Create PurchaseOrder Item"?• 0 [false]• 5 [false]• 10 [false]• 20 [true]

8. Run the script to insert the activity “Create Purchase Order Item”. How many rows were added to the Activity Table?• 27,937 [true]• 3,860 [false]• 25,390 [false]• 1,210 [false]


Create a new transformation,

insert a meaningful name …

1


2


Write the correct SQL script.


Select the code and click on “Execute”.


27,937 rows have been added to your Activity Table.


85

10. Set up Data Model (1/2)

1. Navigate to “Process Data Models” and create a new Data Model. Give it a proper name and click on “Save”. Select the Activity Table youcreated previously and click on it, to configure the process. Which column should you select as Case ID? Please click on the column.• _CASE_KEY [true]• EVENTTIME [false]• _ACTIVITY_EN [false]

2. Which columns should you select for the characteristics activity and timestamp, respectively? Please select the right columns. Then skipthe sorting column and click on “Finish”.• _CASE_KEY [false]• EVENTTIME [true]• _ACTIVITY_EN [true]

3. Where can you a posteriori change the specification (case key, activity column etc.) of the Activity Table? Specify the Sorting Column andclick on “Save”.• By clicking on the three dots on the Activity Table and then clicking on “Edit” [false]• By clicking on the three dots on the Activity Table and then clicking on “Edit activity columns” [true]• "Name mapping" tab in the data model editor [false]

4. In which tab(s) in the data model editor can you do a complete reload of the data model? Please do so.• Permissions [false]• Calendar [false]• Data Loads [true]• Model [false]


Go to “Process Data Models” …

1

… and create a new Data Model.

2


Name the Data Model reasonably …

1


2


Choose your newly created Activity Table in order to

add it to your Data Model.


Click on “Next”.


Now you have to choose the Activity Table. As you only

selected one table, this is quite easy.


First, we have to select the Case ID column.


Click on the left column to choose

the Case ID.


The Activity Name can be found in the second column.


The third column contains the timestamp.

1

Skip the other steps and click on “Finish”.

2


Later changes can be made here.


Click on “Edit activity columns”.


Specify the sorting column.


Click on “Data Loads” to force a complete reload.


Let’s do the complete reload.


Wait until the tables have been loaded

successfully.

C


101

10. Set up Data Model (2/2)

Load the data model. Navigate to Process Analytics and create a new workspace. Connect your data model from Event Collection and click on“finish”. Create a new analysis and add a process explorer to answer the following questions:

5. How many purchase order items were created?• 27.937 [true]• 28.073 [false]• 24.346 [false]• 23.427 [false]

6. How many cases start with the creation of the purchase requisition item?• 349 [false]• 421 [false]• 136 [true]• 260 [false]

7. What is the average throughput time between purchase requisition item and purchase order item creation?• 20 days [false]• 27 days [false]• 25 days [true]


Switch to “Process Analytics” …

1 … and click on “New Workspace”.

2


... select your Data Model…

Choose the Data Pool you created earlier…

1

2

… and click on “Connect”.

3


Give your Workspace a proper name…

1

… and click on “Create”.

2


Create a new analysis.


Give your analysis a proper name …

1

… and create it.2


Click on the analysis to open it.


Simply add a full screen Process Explorer to your analysis.


27,937 purchase order items were created.


Move the slider up …

1

… and you can see that for 136 purchase order items the process

starts with the creation of the purchase requisition item.

2


Change the metric to average

throughput time…


…and you see that the average throughput time between purchase requisition item and purchase order

item creation is 25 days.


113

11. Transformation - Extend Activity Table (1/6)

1. Which SAP table(s) contain(s) information about the change history of many tables?1. CDPOS (Change Document Items) [true]2. CDHDR (Change Document Header) [true]3. EKBE (History per Purchasing Document) [false]4. EKPO (Purchasing Document Items) [false]

2. Which table stores the name of the table and column that has been changed?• CDPOS (Change Document Items) [true]• CDHDR (Change Document Header) [false]

3. Which table stores the username who conducted the change?• CDPOS (Change Document Items) [false]• CDHDR (Change Document Header) [true]


114


Create a new transformation task called “Insert Activity: Change Price”. Open the attached code for the activity “Change Price” and paste it in thetransformation task. Run the code.

4. Have a look at the output, how many activities have been added to the activity table?• 57 [true]• 602 [false]• 77 [false]• 31 [false]

Create another transformation task (“Insert Activity: Change Quantity”) and write the code for the activity "Change Quantity". Answering thefollowing questions will help you accomplish this:

5. Which tables do you have to join for creating the activity "Change Quantity"?• EKKO [true]• CDPOS [true]• CDHDR [true]• EKPO [true]• EKBE [false]• LFA1 [false]


Let’s create a new transformation.


Name it accordingly …

1


2


Insert the attached code.


Select the code and press “Execute”.


57 price changes have been added to the Activity Table.


120


6. Which columns in both tables have to be used for linking the tables CDPOS and CDHDR? Hint: You can get information about the tableshere: https://leanx.eu/en/sap/table/search• MANDANT [true]• TABNAME [false]• CHANGENR [true]• TCODE [false]

7. You already know how to link the pairs EKPO/EKKO and CDPOS/CDHDR. How can you link the change tables to the purchasing tables?• Connect CDPOS and EKPO [true]• Connect CDPOS and EKKO [false]• Connect CDHDR and EKPO [false]• Connect CDHDR and EKKO [false]

8. Which CDPOS columns do you have to consider for joining the table to the changed purchase order items in EKPO?• TABNAME [true]• CHANGENR [false]• TABKEY [true]• VALUE_NEW [false]

https://leanx.eu/en/sap/table/search


121


9. How does the join condition for CDPOS.TABKEY look like?Hint: After you have joined the change tables with EKPO, don't forget to join table EKKO, just like you did when creating the activity"Create Purchase Order Item".• CDPOS.TABKEY = EKPO [false]• CDPOS.TABKEY = EKPO.EBELP [false]• CDPOS.TABKEY = EKPO.EBELN [false]• CDPOS.TABKEY = EKPO.MANDT || EKPO.EBELN || EKPO.EBELP [true]

10. In the WHERE clause of the activity SQL statement, we already restrict the selection to EKKO.BSTYP='F'. Which other columns have to berestricted when looking at quantity changes?• EKPO.MENGE [false]• CDPOS.FNAME [true]• CDPOS.VALUE_OLD [false]• EKPO.NETPR [false]

11. Where is the information on the purchase quantity of a given purchase order item stored?• EKPO.NETPR [false]• EKPO.MENGE [true]• EKPO.MEINS [false]• EKKO.MENGE [false]


122


12. How does the restriction for CDPOS.FNAME look like?• CDPOS.FNAME = 'MENGE’ [true]• CDPOS.FNAME = EKPO.MENGE [false]• CDPOS.FNAME = 'EKPO’ [false]• CDPOS.FNAME = 'Changes’ [false]

13. Let’s focus on the SELECT part of the statement. What to use as case key?• CDHDR.CHANGENR [false]• CDPOS.TABNAME + CDPOS.FNAME [false]• EKKO.MANDT + EKKO.EBELN [false]• EKPO.MANDT || EKPO.EBELN || EKPO.EBELP [true]

14. Which value should you use for the activity column?• 'Change Price’ [false]• CDPOS.FNAME [false]• 'Change Quantity’ [true]• EKPO.MENGE [false]

15. Which time information would you choose for the event time?• EKKO.AEDAT [false]• CAST(CDHDR.UDATE AS DATE) + CAST(CDHDR.UTIME AS TIME) [true]• CDHDR.UDATE [false]• EKPO.AEDAT [false]


123


16. Last but not least: What is a suitable value for the SORTING column of the Activity Table?• 10 [false]• 20 [false]• A [false]• 30 [true]

17. Execute your SQL script. How many quantity changes are inserted into the Activity Table?• 13,423 [false]• 257 [true]• 4,352 [false]• 158 [false]

18. Reload the data model and look at the updated process flow in Process Analytics. What is the average throughput time (in days) betweenPO item creation and quantity change?• 10 [false]• 5 [false]• 8 [true]• 13 [false]



name it, …

1


2


Write the correct code for the activity

“Change Quantity”.


Select the code and click on “Execute”.


There have been 257 quantity changes.


Go to the Data Models …

1

… and access the one you previously

created.

2


Open the data loads tab …

1

… and conduct a complete reload.

2


Back in the analysis, you can see an average

throughput time of 8 days.


131

12. Extend data model (1/4)

1. What's the purpose of extending the data model?• Add master data information to the analysis [true]• Split the activity table [false]• Add translations to the technical table and column names [false]• Add activity-specific information [false]

2. Which database objects can be added to the Celonis data model?• Table [true]• Stored procedure [false]• Schema [false]• View [false]

3. What are possible reasons for limiting the entries in a table (e.g. on the table LFA1) as opposed to embedding all rows of the source table?• Avoid duplicates [false]• Consider only vendors with purchase orders [true]• Consider only top 10 vendors [false]• There is no reason to do so [false]


132


Create three transformation tasks to create the tables “P2P_EKPO”, “P2P_EKKO” and “P2P_LFA1”. Open the attachment and paste it in thetransformation for “P2P_EKPO”. Use the code as a guideline to create the transformations for “P2P_EKKO” and “P2P_LFA1”.

4. Create the table “P2P_EKKO”, restricted to purchase orders with at least one position in table “EKPO”. How many rows does the tablecontain?• 24,675 rows [false]• 9,753 rows [false]• 13,829 rows [true]• 267 rows [false]

5. Add the tables “P2P_EKKO” and “P2P_EKPO”to your data model and connect the tables. Which columns do you have to connect?• EBELN [true]• EBELP [false]• MANDT [true]• AEDAT [false]

6. Connect the two tables to the Activity Table. Which tables(s) do you have to connect?• EKKO with Activity Table [false]• EKPO with Activity Table [true]• EKPO and EKKO with Activity Table [false]



name it …

1


2


Insert the code from the attachment.


Select the code and press “Execute”.


The code has been executed

successfully.

1

You can also refresh the browser.

2


After refreshing, the new table shows up on the left side.


Repeat the same steps to create the

table for EKKO.


Adjust the code accordingly.


Click on “Execute”.


Insert an SELECT COUNT(*) FROM statement to count the number of

rows in your new table.

1

EKKO consists of 13, 829 rows.

2


Go back to your Data Model …

1

… and click on “Add Data” to add EKPO and EKKO.

2


Select both tables.1

… and click on “Finish”.

2


Click on “New Foreign Key” to connect EKKO and EKPO.


Choose the second table.


Click on “New Data

Connection”

The columns to connect are MANDT and

EBELN. Simply click on the key and the keyhole

symbol to connect each line.


As soon as the connection is set up, you can click on

“Save”.


The first two tables are connected.

C


149


7. Which column in the Activity Table do you have to use to connect to table “EKPO”?• ACTIVITY_EN [false]• _CASE_KEY [true]• EVENTTIME [false]• SORTING [false]

8. In the same way as before, create the table “P2P_LFA1” for the vendor master data, restricted to relevant vendors (i.e. subject to the“P2P_EKKO” table created in the last exercise). How many different vendors remain?• 249 [true]• 354 [false]• 645 [false]• 43 [false]

9. Add the “P2P_LFA1” to your data model. Which existing table(s) in the data model do you have to connect it to?• _CEL_P2P_ACTIVITIES [false]• EKPO [false]• EKKO [true]• None of the above [false]


To connect the Activity Table with EKPO click on

“New Foreign Key” …

1

… and on “Connect to

Target Table”.

2


You can use the search function.


The tables are uniquely connected

via the case key.

1

Simply click on “Save”.

2


Right now, all three tables are connected.Now, go back to the

data jobs.

C


Create a new transformation.


Insert the code for the creation of table LFA1.


Execute the script.


The creation of the table was successful.


Count the entries of LFA1 as before.

1

The table contains 249 rows.

2


Go back to the data model and click on

“Add Data”.


Select your new table.


Confirm your selection.


LFA1 has to be connected to EKKO.


Connect MANDT and LIFNR with each other.

C


If you have selected both

columns, click on “Save”.


Finally, all tables are connected correctly.

C


166


Specify table “P2P_EKPO” as case table, reload your data model and navigate to your Celonis analysis. Add a new sheet and create a tabledisplaying the number of purchase order items (case count) with respect to the vendor names (LFA1.NAME1).

10. Who is the vendor with most purchase order items?• PAQ Deutschland GmbH [false]• Unisono AG [false]• C.E.B. BERLIN [false]• GUM Company [true]


Click on the configuration icon

on your Activity Table and assign

EKPO as your case table


The case table is always marked with this icon.

C


Don’t forget to do a complete reload.

1

Then go back to “Process Analytics”.

2


Add a new app to your analysis.


Add a new OLAP table to your empty sheet.


Click on “Add” to define the

dimensions of the table.


Click on P2P_LFA1, …

1 … select NAME1, …

2

… and proceed to the KPI menu.

3


Add the case count …

1

… and click on “Done”.

2


Make sure to sort based on the case

count.


176

13. Finalize data model (1/3)

1. Which difficulty did you observe when adding master data information to your analysis?• Incomplete tables [false]• No connection between activity and master data information [false]• Technical table and column names without translation [true]

2. In SAP, which tables provide information with respect to table and column translations?• EKPO [false]• DD02T [true]• DD03M [true]• MAKT [false]

3. What is the purpose of table DD02T?• Column name translations [false]• Table name translations [true]• Material Descriptions [false]• Login Data [false]

4. What is the purpose of table DD03M?• Column name translations [true]• Table name translations [false]• Material Descriptions [false]• Login Data [false]


177


5. Before answering the following questions, please make sure to rename the tables as explained in the video:P2P_EKKO to EKKO,P2P_EKPO to EKPO,P2P_LFA1 to LFA1.Which tab in the Celonis data model editor can be used to incorporate translations?• Model [false]• Data Loads [false]• Name Mapping [true]• Data Permissions [false]

6. Configure “Name mappings from pool” for your data model by using the tables DD03M and DD02T. Which columns in DD02T contain thetechnical table name, the translation text as well as the language key?• DDTEXT [true]• TABNAME [true]• AS4LOCAL [false]• DDLANGUAGE [true]


Click on the configuration

icon of LFA1 and click on “Edit”.


Enter “LFA1” as an alias for “P2P_LFA1”

to make sure that the name mapping

can become effective later on.


Repeat the same steps for

EKKO and EKPO.

1

Then go to “Name Mapping”.

2


Start with the table name mapping and

click on “Select”.


Choose DD02T …1


2


Select the columns as displayed here.


184


7. Which columns in DD03M contain the technical table name, the technical column name, the translation text as well as the language key?• SCRTEXT_M [true]• TABNAME [true]• AS4LOCAL [false]• DDLANGUAGE [true]• FIELDNAME [true]• COLNAME [false]

8. Click on “Load Mapping From Pool” and reload your data model for the name mapping to become effective. How many columns weretranslated for the Language Key “E”?• 510 [true]• 3 [false]• 506 [false]• 157 [false]

9. What are Data Permissions in a data model used for?• To grant viewer rights on selected analysis. [false]• To restrict access to a specific data model with respect the specific column values. [true]• To grant the rights to create new Data Connections. [false]


For the column name mapping

select DD03M …

1


2


Select all the columns …

1… and load the mappings from

pool.

2


You can see that the name mapping successfully

translated 3 tables (EKKO, EKPO, LFA1) and 510

columns for the Language key “E”

C

celonis intelligent business cloud

Documents