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The ADF (Azure Data Factory) and Power BI combo refers to the integration of Microsoft’s Azure Data Factory, a cloud-based data integration service, with Power BI, a business intelligence and data visualization tool. This combo is highly valuable for organizations seeking to streamline data integration processes, centralize data management, and derive actionable insights from their data. It ensures that data is efficiently moved, transformed, and visualized, facilitating data-driven decision-making and business intelligence.

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ADF & POWER BI COMBO Training in Pune/ Online

Courses Included:-

SQL +Azure Data Factory & Power BI

Duration of Training  :  4 months

Batch type  :  Weekdays/Weekends

Mode of Training  :  Classroom/Online/Corporate Training




For whom SQL is? :

IT or NON IT people can easily enter into leading Database Technology which is commonly used for all domain application software as back-end. There are so many reason to go ahead : 

  • Oracle SQL is basic learning platform to get in IT, which is easy to learn.
  • On completion Oracle SQL training you can attempt certification exam.
  • Certified candidate can take benefit as IT document and can get good opportunity in IT world.
  • Oracle SQL is easy for any support project for initial stage which get into IT carrier. In case you want to grow for development environment than it’s entry for programming.
  • Now a days SQL is basic skill set for any IT/NON IT folks, It’s always helpful for any functional, support and technical consultant.

WEEK-01 :  

Oracle 12c SQL :

SQL Basic 

[1] Introduction to Oracle DB 12c 

Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation. 

[2] Retrieving Data using SQL SELECT statement 

Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview. 

[3] Restricting & Sorting Data 

Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview 


WEEK-02 :

[4] Single Row Function & Customized Output 

SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview. 

[5] Conversion & Conditional Expressions 

Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview. 

[6] Reporting Aggregated Data using Group Function 

Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview. 

[7] Displaying Data from Multiple Tables using Join 

Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview. 


WEEK-03 : 

[8] Using Sub-queries to solve queries 

Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.


SQL Advanced : 

[9] Using Set Operator 

Set Operator Rules, Covering Set Operator as UNION, UNION ALL, INTERSECT, MINUS etc. Practice Overview. 

[10] Managing Tables using DML      Statements 

Data Manipulation Language, Covering INSERT, UPDATE, DELETE & DB Transaction control using COMMIT, ROLLBACK, SAVEPOINT. Use of For update clause in SELECT Statement. 

[11] Introduction to Data Definition Language 

Database Object, Naming Rules, Various Data Types, CREATE TABLE statement, Constraint guidelines, Defining constraints as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY & CHECK etc. ALTER TABLE, DROP TABLE Statement. Create Table using Sub-query. Practice Overview. 


WEEK-04 :

[12] Introduction to Data Dictionary Views

Data Dictionary Structure, How to use Data Dictionary, USER_OBJECTS, ALL_OBJECT, USER_CONSTRAININTS. TABLE Information, Column Information etc. Practice overview.

[13] Creating Sequence, Synonyms & Indexes 

Importance of Sequence, Synonyms & Indexes. Defining Sequence, Synonyms & Indexes & DROP statement etc. Practice overview. 

[14] Creating Views

What’s View? Advantage of Views, CREATE Simple & Complex views, Rules for performing DML operation on views. Modifying & Removing a view etc. Practice Overview. 

[15] Retrieving Data by using Sub-Query 

Multiple Column Sub-query, Column Comparison, Pair & Non Pair Sub-queries, Scalar  Sub queries, Co-related Sub-queries, Use of WITH clause. Practice Overview.




This Certification course has been designed by our certified professional for Beginners’ and Professionals. Want to become the master in Azure data Factory and would like to certify. This is a Real-time scenario based certification and Industry Endorsed course. A cloud-based data integration service allows creating data-driven workflows in the cloud. You can create and schedule data-driven workflows.

You will learn Introduction to Azure Data Factory, Basics of data Flows, Data flow scenarios, Data life cycle, Azure data lake storage, How to implement non-relational Data Stores, Data distribution and partitions, Data Lake Storage, Data Security with Azure Data Factory, Azure Databricks, how to Implement integration runtime, Mapping Data Flows, Azure Stream Analytics, Azure Log Analytics, how to Monitoring Data Factory pipelines, Azure Databricks, and Stream Analytics, Troubleshoot data partitioning bottlenecks, how to manage the data lifecycle.

Introduction to Cloud Computing and Azure data Factory :

  • Cloud Computing and cloud computing models like SaaS, PaaS and Iaas
  • Hybrid Cloud
  • Data Warehouse, ETL and Analytics with cloud
  • Microsoft Azure Data Factory
  • Azure Data Factory workflow and different services

Implement non-relational data stores :

  • Intro to non-relational data stores
  • Cosmos DB
  • Data Lake Storage
  • Blob Storage
  • Data Lake
  • Blob Storage
  • Data distribution and partitions
  • Consistency model in Cosmos DB
  • High Availability, disaster recovery and global distribution

 Implement relational data stores :

  • Relational database and data stores
  • Relational data stores
  • High availability, disaster recovery, and global distribution
  • Elastic pools in a data store
  • Geo-replication
  • Azure Synapse Analytics
  • Data distribution and partitions for Azure Synapse Analytics
  • Relevance of PolyBase

Manage Data Security :

  • Introduction to Data Security with Azure Data Factory
  • Data masking
  • Use of Data Masking 

Develop batch processing solutions :

  • Azure Databricks
  • Azure Data Factory
  • Batch processing solutions using Data Factory and Azure Databricks
  • Data ingestion
  • PolyBase and workflow of PolyBase
  • Ingest data using PolyBase
  • Implement integration runtime
  • Implement Copy Activity
  • Linked services and datasets
  • Pipelines and activities
  • Mapping Data Flows
  • Azure Databricks clusters, notebooks, jobs, and auto scaling
  • Ingest data into Azure Databricks

Develop streaming solutions :

  • Azure Stream Analytics
  • Configure input and output
  • Select the appropriate windowing functions
  • Implement event processing by using Stream Analytics

Monitor relational and non-relational data sources :

  • Introduction to monitoring in Azure Data Factory
  • Azure Monitor
  • Azure Log Analytics
  • Monitoring in Blob Storage and Data Lake Storage
  • Monitoring in SQL Database and Azure Synapse Analytics, and Cosmos DB
  • Configuring alerts in Azure Monitor
  • Auditing by using Azure Log Analytics

Monitor data processing :

Monitoring Data Factory pipelines

Azure Databricks

Stream Analytics

Configuring alerts in Azure Monitor

Auditing by using Azure Log Analytics

Optimize Azure data solutions :

  • Troubleshoot data partitioning bottlenecks
  • Optimize Data Lake Storage, Stream Analytics, Synapse Analytics, and SQL Database
  • Manage the data lifecycle




Data Transformations :

  • Introduction to Power BI Desktop
  • Changing Locale
  • Connecting to a Database
  • Basic Transformations
  • Managing Query Groups
  • Splitting Columns, Changing Data Types, Working with Dates
  • Removing and Reordering Columns
  • Conditional Columns
  • Merge Queries
  • Query Dependency View
  • Transforming Less Structured Data
  • Query Parameters

Data Modelling :

  • Managing Data Relationships
  • Creating Calculated Columns
  • Optimizing Models for Reporting
  • Creating Calculated Measures
  • Creating and Managing Hierarchies
  • Using Calculated Tables
  • Time Intelligence
  • Include and Exclude features
  • Grouping and Binning

Visualizing your Data :

  • Introduction to charts: Pie, Tree map, Combo charts, Map Visualizations, Scatter plot, Table, Matrix, Gauge, Card, Shapes, Textboxes, Images and KPI
  • Filter (Including TopN), Date Slicer
  • Coloring Charts
  • Page Layout, Positioning, Aligning, Sorting Visuals and Formatting
  • Visual Relationship
  • Custom Hierarchies

Working with PBI Service :

  • Overview of Dashboards and Service
  • Uploading to Power BI Service
  • Configuring a Dashboard
  • Dashboard Settings
  • In-Focus Mode
  • Pinning a Live Page
  • Custom URL and Title
  • Export to CSV and Excel
  • Power BI Notifications
  • Publishing to Web

Working with Excel :

  • Importing Excel Data using Simple Table
  • Connecting to Excel Workbook on OneDrive for Business
  • Pinning Excel Tables or Visuals

Organization Packs, Security and Groups :

  • Creating a Group
  • Creating, Using and Editing a Content Pack
  • Row Level Security
  • Data Classification
  • Creating and Using Custom Visuals

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