Скачать с ютуб Using OData Services in SAP Analytics Cloud | SAP Analytics Cloud OData Integration from S/4HANA в хорошем качестве

Using OData Services in SAP Analytics Cloud | SAP Analytics Cloud OData Integration from S/4HANA

Many of you asked me during SAP Analytics Cloud Training about how to consume S/4HANA OData Services Both V2 and V4 in SAP Analytics Cloud. In this session we will explore about creating the connection using odata. we will use odata as an acquired data source, you can use any OData service which come from On-premise or On-cloud system to consume data. This tutorial shows connection to my custom OData Service. We will build this in 3 video series, which are going to help you end to end consumption. Please subscribe the channel for more free videos like this. Course Page https://anubhavtrainings.com/analytic... Join Our Telegram Channel : https://t.me/Anubhavlearningseries12 To join this course Email us on - [email protected] Part 1 : Consume OData in SAP Analytics Cloud - Create OData Connection    • Using OData Services in SAP Analytics...   Part 2 : Consume OData in SAP Analytics Cloud - Create Data Model from odata datasource    • How to create model in sap analytics ...   Part 3 : Consume OData in SAP Analytics Cloud - Create Story on OData Model    • Create Stories in SAP Analytics Cloud...   In this course of SAP Analytics Cloud You will be able to learn following topics unit wise as below UNIT-1: Overview and Positioning  Analytics Cloud Architecture Overview  SAC vs other BI tools  Benefits and core functionalities of SAC  Cloud vs On-Premise vs Hybrid  Analytics Cloud Client tools and Importance UNIT-2: Modelling  What is MODEL  Components of MODEL  Working with Dimension and Classification  Configuring Geo-Dimension  Working with Measures  Working with Transformations  Working with Variables  Data Blending UNIT-3: Business Intelligence  Designing SAC Stories  Working with Custom Templates  Working with Standard Templates  Working with Canvas-Responsive and Grid modes  Working with Designer (Builder panel, Styling Panel)  Filters in SAC  Query level filters  Story level filters  Page-level filters  Widget level filters  Advanced Filters  Linked Analysis  Hyperlinking  Conditional Formatting  Customizing Measures  Customizing Dimensions  Data blending  Working with Chart widget  Working with a Table widget  Working with Geo Map widget  R language basics  Generating R based Stories  Import data connection from Google drive UNIT-4 Augmented Analytics  What is Augmented Analytics  Smart Search  Smart Discovery  Smart Insights UNIT-5: Planning with SAC  How to develop planning data models in SAC  Understand measures, accounts, hierarchies, currency conversion  Manage versions of planning  Create planning stories  Planning functions – variance, forecast, version management  What if analysis  Allocations  Spreading and Distributions  Value Driver Tree- VDT  Data actions and insights  Live Data connection to BPC system  Collaboration UNIT-6: Analytics Designer  What is Analytics Designer  Difference between SAC Stories vs Analytics Designer  Analytics Designer overview and walkthrough  Outline, Designer, Error, and reference panels  Design mode vs Run mode vs View mode  Designing basic Analytic application  working with Container widgets  Implementing filters  working with Drop-down, Radio button, Checkbox components Analytics Cloud Training Technical https://www.anubhavtrainings.com/anal...  working with script variables  working with script objects  Configuring and implementing Dynamic Visibility  Implementing Hyperlinking and Explorer option  Using APIs to integration with Smart discovery, smart insights  Embedding the webpages inside the Analytic designer  Embedding SAC app inside other webpages  Live Data connection to Application Program ON HDB for integration  Close loop scenarios with OData Integration UNIT-7: Predictive Scenario  Predictive scenario overview  SAC Stories vs SAC Applications vs SAC Predictive  Working with Datasets, Variables  Understand Regression  Understand Logistic Regression, RoC Curves, AUC Curve  Model performance and Confusion Matrix  Profit Simulation for Classification  Implementing Classification Precative Model  Implementing Regression Predictive Model  Residual and MAPE Concept in Regression  Trend, Cycle, Residual and Variations concepts  Implementing a Time series Predictive Model  Generating predictive stories UNIT-8: Administration  SAC Administration Overview  Roles (Standard vs Custom)  Team  Users  Working with data loading and scheduling  Cloud connector  Analytics Cloud Agent UNIT-9: SAC Roadmap and Certification