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Manipulate Transformatoin Scripts Collectively Store Time Series of Metrics to Table Schedule Notebook to Run Data Jobs Use Webhook to Integrate Skill with Action Flow Aggregate Bundles to Minimize Notification Manage Bundles in between Action Flow Modules Run first Action Flow Scenario Operate Celonis from outside by REST API Include JSON data in HTTP Request and Response Find out HTTP Request from GUI Function Execute Periodic ETL Automatically Validate Data Model by Studio Analysis Construct My First Data Model Inspect Table Data by SELECT statement Adjust Time Zone of Event Time in Global Transformation Handle Day based Activity as Milestone Unite SQL statements by CASE Expression Split Long SQL Using Views Compose Activity from Joining Multiple Tables Insert Simple Record into Activity Table Determine Process Mining Tables based on Project Goal Consider Case ID before Starting Transformation Tune Endpoint Parameter Relevant to Delta Load Setup Dependent Endpoint in Extractor Builder Configure Endpoint for Suitable Extraction Connect to Source System via REST API Prepare Source System to Generate Event Log Pay attention to Extract SAP Tables Use Pseudonymized Column as Grouping Key Understand Delta Load Configuration Difference in Adding Column Scenario Verify Cloning Table Contents via Delta Load Minimize Extraction Time by Delta Load Option Look at Data Transfer Process by Data Job Log Connect to Celonis and Bring Back Instruction Run Extractor on Your Local Machine Categorize and Name Activity Transform Source System Tables to Minimize Data Model Tables

Store Time Series of Metrics to Table Use Webhook to Integrate Skill with Action Flow Aggregate Bundles to Minimize Notification Manage Bundles in between Action Flow Modules Run first Action Flow Scenario Start Deep Dive to Machine Learning and Action Flow Validate Data Model by Studio Analysis Handle Day based Activity as Milestone Copy Previous Value to Blank Period by RUNNING_SUM and RANGE_APPEND functions Create Key Column of Activity Table Investigate Workload Trend of Cropped Subprocess Verify calculation result in OLAP table then convert to visual component Group similar cases by Clustering Convert Quantitative value to Categorical one by Quantile Function Integrate Button Dropdown Entries to one Formula Use BIND function to enable multiple DOMAIN_TABLE Convert count unit of KPI by COUNT DISTINCT Count rows of Tables in various way Make Conditional Function to return 1 or NULL Create Matrix of Throughput Time by Pivot Table Create Additional Entry to Button Dropdown Categorize and Name Activity Utilize N-M relationship between Activity and Dimension Tables Maintain Saved Formulas effectively Handle NULL efficiently in Aggregation Function Understand how Tables are joined in Data Model Calculate Multi Dimensional KPIs Recognize Record to Calculate KPI Use Pull up function as Subquery Determine First Time Right by Pull up function Understand mechanism of Pull up function Customize Process Explorer Understand Difference between Dimension and KPI

Store Time Series of Metrics to Table Validate Data Model by Studio Analysis Handle Day based Activity as Milestone Copy Previous Value to Blank Period by RUNNING_SUM and RANGE_APPEND functions Create Key Column of Activity Table Investigate Workload Trend of Cropped Subprocess Verify calculation result in OLAP table then convert to visual component Group similar cases by Clustering Convert Quantitative value to Categorical one by Quantile Function Integrate Button Dropdown Entries to one Formula Use BIND function to enable multiple DOMAIN_TABLE Convert count unit of KPI by COUNT DISTINCT Count rows of Tables in various way Make Conditional Function to return 1 or NULL Create Matrix of Throughput Time by Pivot Table Create Additional Entry to Button Dropdown Categorize and Name Activity Utilize N-M relationship between Activity and Dimension Tables Maintain Saved Formulas effectively Handle NULL efficiently in Aggregation Function Understand how Tables are joined in Data Model Calculate Multi Dimensional KPIs Recognize Record to Calculate KPI Use Pull up function as Subquery Determine First Time Right by Pull up function Understand mechanism of Pull up function Customize Process Explorer Understand Difference between Dimension and KPI

Store Time Series of Metrics to Table Handle Day based Activity as Milestone Copy Previous Value to Blank Period by RUNNING_SUM and RANGE_APPEND functions Create Key Column of Activity Table Investigate Workload Trend of Cropped Subprocess Verify calculation result in OLAP table then convert to visual component Group similar cases by Clustering Convert Quantitative value to Categorical one by Quantile Function Integrate Button Dropdown Entries to one Formula Use BIND function to enable multiple DOMAIN_TABLE Convert count unit of KPI by COUNT DISTINCT Count rows of Tables in various way Make Conditional Function to return 1 or NULL Share my Analysis by Content-CLI Create Matrix of Throughput Time by Pivot Table Create Additional Entry to Button Dropdown Categorize and Name Activity Utilize N-M relationship between Activity and Dimension Tables Maintain Saved Formulas effectively Handle NULL efficiently in Aggregation Function Understand how Tables are joined in Data Model Calculate Multi Dimensional KPIs Recognize Record to Calculate KPI Use Pull up function as Subquery Determine First Time Right by Pull up function Understand mechanism of Pull up function Customize Process Explorer Understand Difference between Dimension and KPI

Copy Previous Value to Blank Period by RUNNING_SUM and RANGE_APPEND functions Create Key Column of Activity Table Investigate Workload Trend of Cropped Subprocess Group similar cases by Clustering Convert Quantitative value to Categorical one by Quantile Function Integrate Button Dropdown Entries to one Formula Use BIND function to enable multiple DOMAIN_TABLE Convert count unit of KPI by COUNT DISTINCT Count rows of Tables in various way Make Conditional Function to return 1 or NULL Create Matrix of Throughput Time by Pivot Table Create Additional Entry to Button Dropdown Categorize and Name Activity Utilize N-M relationship between Activity and Dimension Tables Maintain Saved Formulas effectively Handle NULL efficiently in Aggregation Function Understand how Tables are joined in Data Model Calculate Multi Dimensional KPIs Recognize Record to Calculate KPI Use Pull up function as Subquery Determine First Time Right by Pull up function Understand mechanism of Pull up function Customize Process Explorer Understand Difference between Dimension and KPI

Manipulate Transformatoin Scripts Collectively Execute Periodic ETL Automatically Validate Data Model by Studio Analysis Construct My First Data Model Inspect Table Data by SELECT statement Adjust Time Zone of Event Time in Global Transformation Handle Day based Activity as Milestone Unite SQL statements by CASE Expression Split Long SQL Using Views Compose Activity from Joining Multiple Tables Insert Simple Record into Activity Table Determine Process Mining Tables based on Project Goal Consider Case ID before Starting Transformation Prepare Source System to Generate Event Log Use Pseudonymized Column as Grouping Key Understand Delta Load Configuration Difference in Adding Column Scenario Verify Cloning Table Contents via Delta Load Minimize Extraction Time by Delta Load Option Categorize and Name Activity Transform Source System Tables to Minimize Data Model Tables

Manipulate Transformatoin Scripts Collectively Store Time Series of Metrics to Table Schedule Notebook to Run Data Jobs Operate Celonis from outside by REST API Include JSON data in HTTP Request and Response Find out HTTP Request from GUI Function Observe HTTP request in Pycelonis login script Limit permissions of API token to minimize risk Login to Celonis EMS from Jupyter Workbench Start Deep Dive to Machine Learning and Action Flow Execute Periodic ETL Automatically Share my Analysis by Content-CLI

Group similar cases by Clustering Convert Quantitative value to Categorical one by Quantile Function Use BIND function to enable multiple DOMAIN_TABLE Convert count unit of KPI by COUNT DISTINCT Count rows of Tables in various way Categorize and Name Activity Maintain Saved Formulas effectively Handle NULL efficiently in Aggregation Function Use Pull up function as Subquery Determine First Time Right by Pull up function Understand mechanism of Pull up function

Execute Periodic ETL Automatically Validate Data Model by Studio Analysis Construct My First Data Model Determine Process Mining Tables based on Project Goal Create Key Column of Activity Table Categorize and Name Activity Transform Source System Tables to Minimize Data Model Tables Utilize N-M relationship between Activity and Dimension Tables Understand how Tables are joined in Data Model

Execute Periodic ETL Automatically Pay attention to Extract SAP Tables Use Pseudonymized Column as Grouping Key Understand Delta Load Configuration Difference in Adding Column Scenario Verify Cloning Table Contents via Delta Load Minimize Extraction Time by Delta Load Option Look at Data Transfer Process by Data Job Log

Use Pseudonymized Column as Grouping Key Understand Delta Load Configuration Difference in Adding Column Scenario Verify Cloning Table Contents via Delta Load Minimize Extraction Time by Delta Load Option Look at Data Transfer Process by Data Job Log Connect to Celonis and Bring Back Instruction Run Extractor on Your Local Machine

Use Webhook to Integrate Skill with Action Flow Aggregate Bundles to Minimize Notification Manage Bundles in between Action Flow Modules Run first Action Flow Scenario Start Deep Dive to Machine Learning and Action Flow

Use Pseudonymized Column as Grouping Key Understand Delta Load Configuration Difference in Adding Column Scenario Verify Cloning Table Contents via Delta Load Minimize Extraction Time by Delta Load Option Look at Data Transfer Process by Data Job Log

Tune Endpoint Parameter Relevant to Delta Load Setup Dependent Endpoint in Extractor Builder Configure Endpoint for Suitable Extraction Connect to Source System via REST API

Investigate Workload Trend of Cropped Subprocess Create Additional Entry to Button Dropdown Categorize and Name Activity Customize Process Explorer

Execute Periodic ETL Automatically Share my Analysis by Content-CLI

Validate Data Model by Studio Analysis Use BIND function to enable multiple DOMAIN_TABLE

Limit permissions of API token to minimize risk

Validate Data Model by Studio Analysis