Power BI + SQL + ADF Course

  • Duration: 60 Hours

    (Power BI – 35 Hours
    SQL – 10 Hours
    ADF – 15 Hours)

  • Mode of Training: Online
  • Batches Available: Morning on Weekdays (Monday to Friday: Daily 1 Hour)
  • Trainer: MCT Certificated Professional Trainer

  • One sample Project on Power BI
  • One sample Project on ADF
  • Two more Projects dumps

In this course, students will learn about, and apply, the various methods and best practices that are in line with business and technical requirements for Preparing, modeling, visualizing, and analyzing data.

One will also learn about the management aspects of Power BI, including workspaces and datasets, and then learn how to share, distribute, and appropriately secure Power BI assets

  • Good understanding of data 
  •   Basic idea on data analysis and visualisation 

Topics Covered

  •  identify and connect to a data source
  •  change data source settings
  •  select a shared dataset or create a local dataset
  •  select a storage mode
  •  choose an appropriate query type
  •  identify query performance issues
  •  use Microsoft Dataverse
  •  use parameters
  •  use or create a PBIDS file
  •  use or create a data flow
  •  identify data anomalies
  •  examine data structures
  •  interrogate column properties
  •  interrogate data statistics
  • resolve inconsistencies, unexpected or null values, and data quality issues
  •  apply user-friendly value replacements
  •  identify and create appropriate keys for joins
  •  evaluate and transform column data types
  •  apply data shape transformations to table structures
  •  combine queries
  •  apply user-friendly naming conventions to columns and queries
  •  leverage Advanced Editor to modify Power Query M code
  •  configure data loading
  •  resolve data import errors
  • define the tables
  •  configure table and column properties
  •  define quick measures
  •  flatten out a parent-child hierarchy
  •  define role-playing dimensions
  •  define a relationship’s cardinality and cross-filter direction
  •  design the data model to meet performance requirements
  •  resolve many-to-many relationships
  •  create a common date table
  •  define the appropriate level of data granularity
  •  apply or change sensitivity labels
  • apply cross-filter direction and security filtering
  •  create calculated tables
  •  create hierarchies
  •  create calculated columns
  •  implement row-level security roles
  •  implement object-level security
  •  set up the Q&A feature
  • use DAX to build complex measures
  •  use CALCULATE to manipulate filters
  •  implement Time Intelligence using DAX
  •  replace numeric columns with measures
  •  use basic statistical functions to enhance data
  •  create semi-additive measures
  • remove unnecessary rows and columns
  •  identify poorly performing measures, relationships, and visuals
  •  improve cardinality levels by changing data types
  •  improve cardinality levels through summarization
  •  create and manage aggregations
  •  use Query Diagnostics
  • add visualization items to reports
  •  choose an appropriate visualization type
  •  format and configure visualizations
  •  import a custom visual
  •  configure conditional formatting
  •  configure small multiples
  •  apply slicing and filtering
  •  add an R or Python visual
  •  add a Smart Narrative visual
  •  configure the report page
  •  design and configure for accessibility
  •  configure automatic page refresh
  •  create a paginated report
  •  create a PivotTable from a Power BI dataset in Excel
  • set mobile view
  •  manage tiles on a dashboard
  •  configure data alerts
  •  use the Q&A feature
  •  add a dashboard theme
  •  pin a live report page to a dashboard
  • configure bookmarks
  •  create custom tooltips
  •  edit and configure interactions between visuals
  •  configure navigation for a report
  •  apply sorting
  •  configure Sync Slicers
  •  use the selection pane
  •  use drill through and cross filter
  •  drilldown into data using interactive visuals
  •  export report data
  •  apply conditional formatting
  •  apply slicers and filters
  •  perform top N analysis
  •  explore statistical summary
  •  use the Q&A visual
  •  add a Quick Insights result to a report
  •  create reference lines by using Analytics pane
  •  use the Play Axis feature of a visualization
  •  personalize visuals
  •  identify outliers
  •  conduct Time Series analysis
  •  use anomaly detection
  •  use groupings and binnings
  •  use the Key Influencers to explore dimensional variances
  •  use the decomposition tree visual to break down a measure
  •  apply AI Insights
  • configure a dataset scheduled refresh
  •  configure row-level security group membership
  •  provide access to datasets
  •  configure incremental refresh settings
  •  promote or certify Power BI datasets
  •  identify downstream dataset dependencies
  •  configure large dataset format
  • create and configure a workspace
  •  recommend a development lifecycle strategy
  •  assign workspace roles
  •  configure and update a workspace app
  •  publish, import, or update assets in a workspace
  •  apply sensitivity labels to workspace content
  •  use deployment pipelines
  •  configure subscriptions
  •  promote or certify Power BI content
  •  Introduction
  •  Work with Schemas
  •  Explore the structure of SQL Statements
  •  Examine the SELECT statements
  •  Work with data types
  •  Handle NULLs
  • Sort your results
  •  Limit the sorted results
  •  Page results
  •  Remove duplicates
  •  Filter data with predicates
  •  Understand joins concepts and syntax
  •  Use Inner joins
  •  Use Outer joins
  •  Use Cross joins
  •  Use Self joins
  • Understand Subqueries
  •  Use scalar or multi-valued subqueries
  •  Use self-contained or correlated subqueries
  • Categorize built-in functions
  •  Use aggregate functions – AVG SUM MIN MAX COUNT
  •  Use Mathematical functions – ABS, COS/SIN, ROUND RAND
  •  Use Ranking functions – RANK, DENDE-RANK
  •  Use Logical functions – CHOOSE, GREATEST, LEAST
  •  Summarize data with GROUP BY
  •  Filter groups with HAVING
  • Insert data
  •  Generate automatic values
  •  Update data
  •  Delete data
  •  Merge data based on multiple tables
  • Overview of Azure Data Factory and its key features

    Understanding the data integration and data orchestration concepts

    Exploring the components and architecture of Azure Data Factory

    Setting up an Azure subscription and creating an Azure Data Factory instance

  • Configuring data movement activities in Azure Data Factory

    Working with different data sources and destinations

    Data ingestion techniques and considerations

    Implementing copy activities for data transfer

  • Introduction to data transformation in Azure Data Factory

    Working with data transformation activities like mapping, filtering, and         aggregating data

    Implementing data flow activities using Azure Data Factory Mapping Data Flows

    Understanding data wrangling and data profiling concepts

  • Exploring common data integration patterns and scenarios

    Designing efficient and scalable data integration workflows

    Implementing data partitioning and parallelism for optimal performance

    Security and compliance considerations in Azure Data Factory

  • Real-world use cases and case studies of Azure Data Factory implementation

    Examining best practices for data integration projects

    Real-time project 1

Apply to Power BI + SQL + ADF


The course is a comprehensive training program that covers Power BI for data visualization, SQL for database management, and Azure Data Factory for data integration and ETL (Extract, Transform, Load) processes.

While there are no strict prerequisites, a basic understanding of databases and familiarity with data concepts can be beneficial. Knowledge of SQL is helpful but not mandatory.

 The Power BI section covers data visualization, creating reports and dashboards, data modeling, DAX (Data Analysis Expressions), and connecting to various data sources.

Yes, the course is designed to accommodate learners at various skill levels, including beginners. It provides a strong foundation and gradually builds advanced skills.

The course includes hands-on projects and real-world scenarios that simulate practical industry situations, allowing participants to apply what they’ve learned.

The SQL section covers database design, querying data, data manipulation (INSERT, UPDATE, DELETE), stored procedures, and database management.

Yes, participants typically receive access to course materials, video recordings, and resources for future reference.

Azure Data Factory is a cloud-based ETL service. The course covers ADF concepts, data pipelines, data transformation, and connecting to various data sources.

Yes, the course includes practical exercises and projects related to Azure Data Factory, allowing participants to gain hands-on experience in creating data workflows.

Yes, the course offers guidance on resume preparation, interview tips, and include mock interview sessions.

Many training programs offer a course completion certificate, which can be a valuable addition to your resume.

Yes, most online courses include live sessions with instructors, Q&A opportunities, and forums for interaction with fellow participants.

Azure data engineering skills are in high demand in today’s job market. As more and more companies move their data to the cloud, the need for skilled data engineers who can design, implement, and manage data solutions on Azure is growing rapidly.

You can enroll by visiting the Growing Tree Technologies website and following the enrollment process or contacting the support team for guidance.

Post-training assistance includes access to updated course materials, resources, and support for any questions or challenges you may encounter while applying your skills in real-world scenarios.

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