MicrosoftFabric Training From SQL School Training Institute

Microsoft Fabric is an all-in-one analytics solution for enterprises that covers everything from data movement to data science, Real-Time Analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. The platform is built on a foundation of Software as a Service (SaaS), which takes simplicity and integration to a whole new level.

Microsoft Fabric Training Plans

  Plan A Plan B Plan C
  1. Microsoft Fabric 1. Microsoft Fabric
2. Azure Data Engineer
1. Microsoft Fabric
2. Azure Data Engineer
3. Power BI
Total Duration 4 Weeks 11 Weeks 15 Weeks
Need for Microsoft Fabric?
Fabric Terminology
SaaS Implementations
Synapse Engineering
Lake House
Dataset Discovery
ADF : Azure Data Factory
ADF : Data Imports ETL
ADF : Data Flows Wrangling
ADF : Transformations ETL
Synapse: Configuration Loads
Synapse: ETL with ADF DWH
Synapse: Performance Tuning
Synapse: MPP cDWH DIUs
ADB : Azure Data Bricks
ADB : Architecture Data Loads
ADB : Run Spark Jobs Pools
ADB : Workspace Delta Tables
DP 203 Certification Guidance
Power BI: Report Design Visuals
Power BI: Power Query (M Lang) DAX
Power BI: Report Server Admin
Power BI: PL-300 Certification Guidance
DP 500 Guidance
Total Course Fee
( Payable in Installments)*
INR 20000
USD 300*
INR 45000
USD 565*
INR 58000
USD 725*

Trainer: Mr.Sai Phanindra (18+ Yrs of Exper)

Microsoft Fabric Weekend Schedules
S No Time (IST, Sat & Sun) Start Date  
1 7:30 AM - 9 AM Mar 9th Register
2 7:30 PM - 9 PM Mar 23rd Register
Power BI Training Schedules
S No Time (IST, Mon - Fri) Start Date  
1 8 AM - 9 AM Mar 5th Register
2 6 PM - 7 PM Mar 19th Register
3 8 PM - 9 PM Apr 9th Register
Azure Data Engineer Training Schedules
S No Time (IST, Mon - Fri) Start Date  
1 7 AM - 8 AM Feb 27th Register
2 7 PM - 8 PM Mar 5th Register

If above schedule does not work, opt for Microsoft Fabric Training Videos

Microsoft Fabric Training Highlights :

✔ Azure Fundamentals ✔ Azure AD
✔ Azure SQL Concepts ✔ Azure Migrations
✔ Azure AD ✔ Azure Key Vaults
✔ Azure Monitor ✔ Azure Notebooks
✔ Azure Data Factory ✔ Azure Synapse
✔ Azure Synapse ✔ Azure Strorage
✔ Data Lake Storage ✔ Data Lake Analytics
✔ Stream Analytics ✔ IoT, Event Hubs
✔ Azure Cosmos DB ✔ Azure Databricks
✔ Python, Scala ✔ Spark Clusters
✔✔ End to End Real-time Project @ Resume
 

Microsoft Fabric Training Course Contents:

Ch 1 : Fabrics Introduction

  • Big Data Analytics with Azure
  • Products and Services in Analytics
  • Microsoft Fabric - One Umbrella
  • Microsoft Fabric - Advantages
  • Unified Data Foundation
  • AI Powered Capabilities
  • OneLake: Storage
  • Synapse: Engineering, Warehouse
  • Synapse: Data Science, Analytics
  • Power BI: Business Intelligence
  • Action platform; Data Activator
  • Governance; Purview

Ch 6: Fabric Data Factory - 2

  • Fabric Studio: Copy Data Activity
  • Copy Assistant : Usage Options
  • Connections & Linked Services
  • serialization/deserialization
  • compression/decompression
  • Colum Mapping & Conversions
  • Activity Timeouts, Retries
  • Secure Input / Output Options
  • Column Mapping & Settings
  • Fabric Pipelines: Copy Methods
  • Existing Pipeline Edits, Publish
  • Pipeline Monitor & Logging

Ch 11: Synapse Warehouse - 2

  • Fabric Security Options
  • Warehouse Security Model
  • Warehouse Access Model
  • Fabric Workspace Roles
  • Item Permissions
  • Object Level Security
  • Sharing Warehouse
  • SQL Permissions
  • Read, Connect
  • SQL endpoint data
  • ReadAll & Build
  • Grant, Revoke, Deny

Ch 2 : Fabric Licenses, Capacity

  • Fabric Workspace : Licensing
  • Fabric Components & Tenant
  • Organizational Licenses
  • Capacity, PPU and SKUs
  • Individual Licenses: Free, Pro
  • SKU Types: Azure & Office 365
  • Fabric Workspace : Activation
  • GUI: Walk Thru Options
  • Data Factory, Synapse Options
  • Power BI & Streaming Objects
  • Creating Fabric Capacity
  • Pause / Resume Capacity

Ch 7: Fabric Data Factory - 3

  • Fabric Studio: Data Flow Activity
  • Spark Clusters : Automations
  • Spark Cluster Debugging
  • Spark Cluster Sizing, Capacity
  • Lakehouse Table: ETL Process
  • ADLS File Connectors
  • Power Query Transformations
  • Power Query Profiling
  • In-Memory Processing, M Lang
  • Data Flow Optimizations
  • Partition Options & Tuning
  • Broadcast Options & Tuning

Ch 12: Synapse Warehouse - 3

  • Zero Copy Clone in DWH
  • Table Clone in Synapse
  • Table Clone Inheritance
  • Create Table As Clone
  • Table Cloning Scenarios
  • Cloning : Limitations
  • Warehouse Performance
  • Statistics : Creation, Use
  • Leverage Stats in DWH
  • In-Memory & SSD Cache
  • Disk Cache, Important DMVs
  • Cold Cache & Management

Ch 3: Lakehouse Concepts

  • What is Lakehouse?
  • How to configure LakeHouse?
  • Lakehouse Explorer Tool
  • Interacting with Lakehouse Items
  • Pipelines and Notebooks
  • Spark Jobs and Dataflows Gen 2
  • Using Lakehouse Explorer
  • Main View and Ribbon Area
  • Table Section, File Section
  • Data Load Options to Lakehouse
  • SyMS and Unidenfied Area
  • Landing Zone, Data Processing

Ch 8: Fabrics & Spark Clusters

  • Apache Spark Configurations
  • Data Engineering/Science
  • Sark Compute & Capacity
  • Node Family Settings
  • Memory Optimized
  • Transaction Optimized
  • Runtime Versions, Scaling
  • Notebooks, Concurrency
  • Python Libraries in Spark
  • Dataframes & Realtime Use
  • Spark SQL Queries & Data Loads
  • Data Visualizations & Spark

Ch 13: Synapse Realtime Analytics

  • Realtime Analytics in Fabric
  • Streaming & Time Series Data
  • Capture, Transform & Route
  • Ingest, Load and Stream
  • Data Integration At Scale
  • Creating KQL Databases
  • KQL Tables, Queries
  • BLOB Data Ingestions
  • Data Loads & Fabric Studio
  • Command Viewer Options
  • Partiaial Data Preview
  • Data Exploration

Ch 4: Data Loads with Lakehouse

  • Large Scale Data Analytics
  • Onelake Metastore & Use
  • Workspace & LakeHouse
  • Lakehouse Explorer: Tables, Files
  • File Upload : Header, Data Types
  • Explorer Shortcuts in Lakehouse
  • File Data Load to Tables
  • SQL Queries in Tables
  • Lakehouse to SQL Endpoint
  • Transaction Audit: _delta_log
  • Aggregations, Visual Queries
  • Reports and Data Models

Ch 9: Fabric Notebooks

  • Spark Jobs for ETL
  • Spark Dataframes for ETL
  • Inferring & Explicit Schema
  • sql.types
  • sql.functions
  • Filter, Group Data Frames
  • Dataframe Storage, Partition
  • Spark Catalog : Objects
  • TempView in Spark Catalog
  • Spark SQL API & Visualizations
  • Graphic Packages: PyPlot
  • Big Data Analytics with Spark

Ch 14: OneLake Concepts

  • Unified Data Lake in Fabric
  • Warehouse and Lakehouse
  • One Lake Workspace Management
  • Azure HD Insight : Security
  • DFS API and Connections
  • Fabric Workloads & Tuning
  • Onelake File Explorer
  • One Copy of Data, Data Engines
  • Compute and Analytics
  • Uni Management & Governance
  • Creating Lakehouse with Onelake
  • Data Loads with Lakehouse

Ch 5: Fabric Data Factory - 1

  • Fabric Data Factory & ETL
  • Data Ingestions & Orchestrations
  • Dataflows and Pipeline Options
  • AI Based Transformations
  • Data Flows: Low Code Interface
  • Power Query : Advantages
  • Pipeline Design: Copy Data
  • Compute and Optimizations
  • Connections & Datasets
  • Pipeline Activity: Options
  • Column Mapping & ETL
  • Canvas : Debug & Monitoring

Ch 10: Synapse Warehouse - 1

  • Fabric Datawarehouse
  • DWH Creation Options
  • Sensivity Settings
  • Warehouse Compute
  • SSMS Connections
  • ADS Connections
  • Table Creations
  • Warehouse Sample
  • Data Load Options
  • Warehouse Query Options
  • Data Aggregations
  • Data Analytics

Ch 15: Microsoft Fabric with Power BI

  • Using Power BI with Fabric
  • OneLake Connections with Power BI
  • Power BI Desktop with Fabric
  • Power BI Desktop with OneLake
  • Power BI Desktop with LakeHouse
  • Power BI Desktop with Synapse
  • Power BI Cloud with OneLake
  • Power BI Datasets (LIVE)
  • Power BI Datamarts and Usage
  • Dashboards with Fabric Metrics
  • Unified Data Foundation : OneLake
  • End to end Implementation Plan

Part 1: Azure Data Factory, Synapse Analytics

Part 2: Data Lake Storage, Stream Analytics

Part 3: Databricks, Spark, Python

Chapter 1: Cloud Basics, Azure SQL

  • Cloud Introduction and Azure Basics
  • Azure Implementation: IaaS, PaaS, SaaS
  • Azure Data Engineer: Job Roles
  • Azure Storage Components
  • Azure ETL & Streaming Components
  • Need for Azure Data Factory (ADF)
  • Need for Azure Synapse Analytics
  • Azure Resources and Resource Types
  • Azure Account, Subscription (Free)
  • Azure SQL Server [Logical Server]
  • Firewall Rules and Azure Services
  • Azure SQL Database Deployment
  • Azure SQL Pool Deployment
  • Compute: DTU Versus DWU
  • Test Connections from SSMS

Chapter 1: Azure Fundamentals - Storage

  • Azure Resources: Storage Components
  • Storage Resources and Properties
  • Resource Groups & Subscriptions
  • Azure Storage : Files, Tables and ETL
  • Azure Storage Account & Use
  • Data Lake Storage Account (ADLS)
  • Advanced Options: HNS Property
  • Resource Location, Resource Group
  • Azure Portal: Deployment Verifications
  • Azure Portal: Deployment Verification
  • Storage Account : Basic Properties
  • Overview Page: Status, HNS State
  • Azure Storage : Access Options
  • Azure Storage Explorer Tool
  • Explorer Tool : Configuration

Chapter 1: Azure Intro, Azure Databricks

  • Azure Cloud : SaaS, PaaS, PaaS & IaaS
  • Azure Cloud : Storage, ETL Resources
  • Azure Databricks : Compute Resources
  • Need for Azure Databricks (ADB)
  • Azure Databricks : Purpose & Config
  • Azure Databricks Service Creation
  • Azure Databricks Components
  • Azure Databricks Workspace, Usage
  • Spark Cluster Configurations, Capacity
  • Driver Nodes, Worker Nodes in Spark
  • Cluster Types : Personal, Unrestricted
  • CPU, Memory & IO Resources
  • Virtual Machines (VM) for Clusters
  • Databricks : Runtime & DBFS Storage
  • DBFS : Files, Tables with Spark DB

Chapter 2: Synapse SQL Pools (DWH)

  • Dedicated SQL Pools in Azure
  • Data Warehouse with Synapse
  • Massively Parallel Processing (MPP)
  • Control Nodes and Compute Nodes
  • DMS: Data Movement Service
  • Start/Resume/Pause & Scaling
  • SQL Pool Config @ TSQL Scripts
  • Start/Resume/Pause, Scaling Options
  • Table Creations @ TSQL Scripts
  • Table Partitions: Left & Right
  • Distributions: Round Robin, Hash
  • Distributions: Replicate and Usage
  • Auto Indexing & Column Store
  • Planning for Big Data Loads
  • Need for ADF: Azure Data Factory

Chapter 2:  Azure Storage Operations

  • BLOB: Binary Large Objects
  • Storage Browser and Service Pages
  • Storage Browser: Container Creation
  • Storage Browser: Folder, File Uploads
  • Service Page: Container Creation
  • Service Page: Folder, File Uploads
  • Container, Folder, File Properties
  • Limitations with Storage Portal
  • Azure Data Explorer Tool : Usage
  • Contrainer: Creation, Properties
  • File Uploads, Edits and Access URLs
  • Azure Storage Explorer Tool Usage
  • Azure Account Options in Explorer
  • Directory Creation, File Operations
  • Limitations with Explorer Tool

Chapter 2: SparkDatabase, SQL Notebooks

  • DBFS : File Uploads from ON-Premise
  • Creating Spark Tables; Spark DB
  • Data Explorer: HIVE Metastore
  • Data Explorer: Spark Database, Tables
  • Notebooks: SQL, Python and Scala
  • Creating SQL Notebooks in Databricks
  • Creating User Defined Spark Databases
  • Connecting / Using Spark Databases
  • Spark SQL : Big Data Loads
  • Spark SQL : Database & Table List
  • Spark SQL : Data Aggregations, Jobs
  • Spark SQL : Data Analytics, Reports
  • Analytics: X, Y Axis, Group By
  • Notebooks : Export, Import, Clone
  • Notebooks : Storage & Versions

Chapter 3: Azure Data Factory, Pipelines

  • Azure Data Factory (ADF) Concepts
  • ADF Pipelines : Architecture
  • Integration Runtime (IR) & Use
  • Linked Services and Datasets
  • Pipeline Activities: Copy Data Tool
  • DIU : Data Integration Units
  • DTU Vs DWUs Vs DIU
  • ADF Pipeline with Copy Data Tool
  • Azure SQL DB to Synapse Data Loads
  • Multi Tables Data Loads with ADF
  • Bulk Insert, Data Copy Methods
  • ETL Staging: Storage Account
  • Staging Container Connections
  • DIU Allocations & Publish
  • ETL Pipeline Monitoring, Runs

Chapter 3:  Azure Storage Security, ACLs

  • Azure Data Lake Storage Security Options
  • Shared Access Keys: Primary, Secondary
  • SAS Key Generation: Container, Tables
  • SAS Key Permissions, Validation Options
  • Access Keys: Account Level Permissions
  • Azure Active Directory: Users, Groups
  • Azure AD Security: RBAC, IAM, ACLs
  • Owner Role, Contributor, Reader Role
  • Azure Data Lake Storage Security
  • ACL : Access Control Lists & Security
  • Azure BLOB Storage Containers & ACLs
  • Folder Level and File Level Security
  • ACL Permissions: Read, Write, Execute
  • Access Policy: Creation, Realtime Use
  • rwacdl; Azure Principals, CORS

Chapter 3: Python Intro,  Data Loads

  • Python : Introduction, Real-time Use
  • Python For ETL and DWH
  • Python For Azure: Data Engineer
  • Python Data Frames & Purpose
  • Python Dataframes - Pandas
  • Python with Spark Integrations
  • PySpark for DDL and ETL
  • PySpark Versus SQL Notebooks
  • Reading DBFS Data into Spark
  • Creating Dataframes for ETL
  • Temporary Views & Dataframes
  • Spark Temp Views: Aggregations
  • Spark Table Loads, HIVE Data
  • write.format() & overwrite
  • Parquet Tables with Spark DB

Chapter 4: OnPremise Data Loads, Upsert

  • Copy Data Tool : Incremental Loads
  • On-Premise Data Sources with Azure
  • Self Hosted Integration Runtime (IR)
  • Access Keys, Remote Linked Service
  • Synapse SQL Pool (DW), OnPremise
  • ETL Staging with Storage Account
  • Copy Method: Polybase - Tuning
  • Polybase : Big Data Loads
  • ETL Pipelines for Incremental Loads
  • Business Keys For Table Upsert
  • Pipeline Schedules with ADF
  • ETL Logging with Storage Account
  • Copy Method: UPSERT
  • DIU, DOCP & Publish
  • Manual Pipeline Executions in ADF

Chapter 4:  SQL Database Migrations

  • OnPremise SQL DB to Azure Migration
  • SSMS Tool, SQL Database Installation
  • Source Database Scripts & Validations
  • BACPAC File Generation: SSMS Tool
  • Table Selection & Advanced Options
  • Azure Data Lake Storage, SSMS Access
  • Azure Storage Container, BACPAC Files
  • IAM and Account Key Authentication
  • Azure SQL Server Creation From Portal
  • Azure SQL Database Deployment
  • DTU : Data Transaction Units, Pricing
  • Azure Firewall Configuration, Security
  • Azure SQL Database Imports (bacpac)
  • Azure SQL Server with ADLS Containers
  • Azure SQL DB Migrations, Verification

Chapter 4: PySpark with ADLS

  • Azure Storage Account : Creation
  • Azure Data Lake Storage : HNS
  • Creating Containers in ADLS
  • BLOB File Uploads / Generation
  • Account Key : Access Key, SAS Key
  • BLOB Access URL for Databricks
  • WASBS URL for PySpark Notebook
  • Generating PySpark Script
  • PySpark Connection Variables
  • Databricks : Data Import Scripts
  • Config Options with ADLS, Spark
  • config (), Session Context
  • DataFrames with Temp Tables
  • Escape Sequence with SparkSQL
  • Data Explorer: HIVE & Spark DB

Chapter 5: File Incremental Loads in ADF

  • Incremental Loads with Files (BLOB)
  • ETL Schedules: Tumbling Window
  • Execution Retry and Delay Options
  • Binary Copy, Structural Data Loads
  • Incremental Loads Verification Tests
  • Incompatible Rows & Fault Tolerance
  • Pipeline Compression & Tuning
  • Pipeline Publish, Monitor Options
  • Azure Monitor Resource : Metrics
  • ADF Metrics and Pipeline Runs
  • ADF: Pipeline Monitoring and Alerts
  • Synapse: Storage Monitoring, Alerts
  • Conditions, Signal Rules and Metrics
  • Alerts & Action Groups: Emails
  • Email Notifications with Azure

Chapter 5:  Azure Tables & Replication

  • Azure Tables - SchemaLess Design
  • Azure Tables: Creation, Data Inserts
  • Tables, Entities, Properties Concepts
  • Structured, Relational Data Storage
  • Azure Tables: GUI, Data Types
  • Azure Tables: Big Data Imports
  • Data Edits, Queries, Delete Operations
  • Odata Options (REST API), End Points
  • Azure Storage: Replications, DR Options
  • LRS: Locally Redundant Storage
  • GRS: Globally Redundant Storage
  • ZRS: Zone Redundant Storage
  • Replication Options and Advantages
  • Replication Verification, Modifications
  • Storage Endpoints, Failover Partner

Chatper 5: PySpark Widgets

  • Widgets : Notebook Parameters
  • widget module : Text, Combo
  • Dropdown, Multi Select Parameters
  • dbutils help(), get() & remove()
  • Dataframes, Spark SQL @ Variables
  • Python Data Frames, Spark SQL
  • Reading Parameters Values
  • Parameters Versus Variables
  • Using Parameters For Temp Tables
  • Using Parameters for Spark Tables
  • Data Storage and HIVE Metastore
  • Reading Parameterized Data
  • Format Strings with PySpark
  • Dynamic Queries with Spark SQL
  • Aggregations and f Strings

Chapter 6: ADF Data Flow - 1

  • Data Flow Task, Data Flow Activity
  • Transformations with Data Flow
  • Spark Cluster For Debugging
  • Cluster Node Configurations
  • Spark Cluster Types & Sizing
  • Transaction Optimized - Capacity
  • Memory Optimized - Capacity
  • Data Cleansing with ADF
  • Data Orchestration with Data Flow
  • SELECT Transformation & Options
  • Conditional Split Transformation
  • UNION, SELECT Transformation
  • Spark Cluster For Pipeline Executions
  • Pipeline Monitoring & Run IDs
  • Adding Data Flow into Pipelines

Chapter 6: Azure Stream Analytics, IoT

  • Azure Stream Analytics Real-time Use
  • Real-time Data Processing, Events
  • Ingest, Deliver & Analysis Operations
  • Azure Stream Analytics Jobs Concept
  • Understanding Input, Output Options
  • SAQL Queries: Stream Analytics Jobs
  • IoT: Internet Of Things, Real-time Data
  • Need for IoT Hubs and Event Hubs
  • Conditional Split Transformation
  • Creating IoT Device for Data Inputs
  • Creating Azure Stream Analytics Job
  • Stream Analytics for Historical Data
  • Azure SQL Database for ASA Jobs
  • SAQL: Query Formatting, Validation
  • Historical Data Upload, ASA Jobs

Chapter 6: Architecture, Workflows

  • Driver Nodes, Worker Nodes, DBUs
  • RDD : Resilent Data Distribution
  • DAG : Directed Acyclic Graph
  • Hadoop HDES and Spot Instance
  • Cluster Manager, Master Node
  • RDDS, Worker, Excecutor & Slave
  • Hadoop HDES & Databricks Runtime
  • Databricks Optimization Techniques
  • Spot Instance, Photon Acceleration
  • All Purpose Cluster, Job Cluster
  • Databricks Jobs: Creation & Tasks
  • Jobs with Parameters, Executions
  • Task Dependency & Notifications
  • Continuous & Manual Schedules
  • Active Jobs, Recent Run Jobs, Monitor

Chapter 7: ADF Data Flow - 2

  • ADF Pipelines For ETL Operations
  • Data Flow Tasks, Activities in Synapse
  • JOIN & EXISTS Transformations
  • Aggregate & Group By Transformations
  • Window Functions, Rank in Data Flow
  • Rank / DenseRank / Row Number
  • Derived Column Transformation
  • Lookup, Surrogate Key, Parse
  • Type Convert, Cast Transformations
  • Reusing Data Flow Tasks in Synapse
  • Pipeline Validations & Executions
  • Inline Datasets, Schema Drift
  • Data Deduplication with ADF
  • DFT Optimization Techniques
  • Data Flow Task - Staging, Logging

Chapter 7: Azure Event Hubs

  • Azure Stream Analytics For API Data
  • IoT Hubs, IoT Devices, Connection Strings
  • Rasberry APP Connections with IoT Hub
  • Azure Storage Account and Container
  • Creating Azure Stream Analytics Job
  • Configuring Input Aliases with IoT Hub
  • Output Aliases with ADLS Gen 2
  • SAQL Query, Job Executions; Monitoring
  • Azure Event Hubs and Event Instances
  • Event Hub Namespaces, Partition Counts
  • Access Policies, Permissions & Defaults
  • RootManageSharedAccessKey & Options
  • Connection Strings & Event Service Bus
  • Telco App : Executions & LIVE Data
  • On-Premise App Integration, ASA Jobs

Chapter 7: Databricks Security, Scala

  • Azure Databricks Security Operations
  • Azure Active Directory (Azure AD)
  • AD Users and RBAC with IAM
  • Owner, Contributor & Reader Roles
  • Workspace Admin Permissions
  • Notebook Permissions & Share
  • Workflow Security, HTTP Path
  • User Tokens & ServerName
  • Scala : Differences with PySpark
  • Scala : Variables Declaration, Usage
  • SparkSQL with Scala Notebooks
  • Temp Views with Scala Notebooks
  • Aggregations with Scala Notebooks
  • Visual Data Analytics with Scala
  • PySpark to Scala Conversions

Chapter 8: Azure Synapse Analytics

  • Azure Synapse Analytics Resource
  • Azure Synapse Analytics Workspace
  • Managed Resource Group, SQL Account
  • Synapse Workspace & Synapse Studio
  • Operations with Synapse Workspace
  • ADLS Gen 2 Storage Account, Container
  • Synapse Studio: Scripts & Pipelines
  • Dedicated SQL Pools : Creation, Use
  • Synapse Tables, Data Loads with TSQL
  • COPY INTO Statements with T-SQL
  • Row Terminator and Compressions
  • T-SQL Queries and Aggregations
  • Aggregation Data Loads in Synapse
  • Creating Synapse Pipelines with TSQL
  • Stored Procedure Activity & Triggers

Chapter 8: Storage Architecture, Queues

  • Azure Storage Account : Architecture
  • Etag: Replication & Encryption Use
  • BLOB Types: Block, Append & Page
  • Access Tiers: Hot, Cool, Cold Types
  • Archive Access Tier & Retention
  • Legal Hold & Time Bound Access
  • Pricing : HNS, Security, Encryption
  • EndPoint URL & Read-Only Use
  • Azure File Share Service (Files)
  • Mounting Files From On-Premise
  • SMB File Share : Hot, Optimized
  • Azure Queue Service & Messages
  • Message Queues : Operations
  • Storage Explorer Tool with Shares
  • Azure Storage Services: ETL Needs

Chapter 8: Scala with ADLS, Azure SQL

  • Data Imports with Azure SQL DB
  • Using Scala for Big Data Loads
  • Spark SQL Queries @ Temp Views
  • Variables, display(), read()
  • Scala Transformations, display()
  • JSON, AVRO and DBFS Mounts
  • azure.sas.container @ ADLS
  • write.jdbc() & JVM
  • JDBC Connection, DataframeWriter
  • Data Extraction, SQLContext
  • Spark Context and Spark Session
  • SQLServerDriver with Scala
  • ADLS with Scala Notebooks
  • Parameters (Widgets) with Scala
  • Compare Python with Scala

Chapter 9: Synapse Analytics with Spark

  • Synapse Pipelines: Performance Advantage
  • Pivot Transformation For Normalization
  • Generate Pivot Column, Aggregations
  • Pivot Transformation & Pivot Setting
  • Pivot Key Selection, Value and Nulls
  • Pivoted Columns and Column Pattern
  • Column Prefix, Help Graphic, Metadata
  • Denormalized Data and Aggregations
  • Apache Spark Pool in Azure Synapse
  • Spark Cluster Nodes: Vcores, Memory
  • Notebooks : Purpose, Usage Options
  • Python Notebooks For Remote Access
  • Creating Databases in Apache Spark Pool
  • Data Loads from Dedicated SQL Pools
  • PySpark Code for Data Operations, Writes

Chapter 9:  Monitoring & Key Vaults

  • Azure Monitor, Metrics & Activity Logs
  • Monitoring Azure Storage Namespaces
  • Add KQL Metrics; Account, Blob and File
  • Total Ingress and Egress Metrics: Charts
  • Average Latency, Transaction Count
  • Request Breakdowns, Signal Logic
  • Azure Alerts & Conditions, Notifications
  • Signal Logic Conditions and Emails
  • Key Vaults Types: Standard & Premium
  • Secret Page, Key Backups, Key Restores
  • Azure Key Vaults - Name and Vault URI
  • Inbuilt Managed Key and Azure Key Vault
  • Key Vaults Types: Standard & Premium
  • Secret Page, Key Backups, Key Restores
  • Managed Identity with ETL Process

Chapter 9: DeltaLake Incr Loads, DWH

  • Azure DeltaLake Implementation
  • ACID Properties, Upsert Advantages
  • Delta Engine Optimizations & Uses
  • Pipeline Creation: JSON Files in DBFS
  • Delta Tables Creation, Data Loads
  • Spark Cluster Settings: Auto Optimize
  • Auto Compact, Delta Table Optimize
  • JSON Files, Delta Streaming Location
  • Joins and Merge with Delta Tables
  • Incremental Loads, Delta Tables
  • Create & Use DWH with Databricks
  • Upsert (Merge) with Spark Tables
  • Big Data & Jupyter Notebooks
  • Databricks with Data Factory (ADF)
  • End to End Implementations

Chapter 10: Synapse Security & Parameters

  • Azure Active Directory (AAD) Users, Groups
  • IAM: Identity & Access Management
  • Synapse Workspace Security with RBAC
  • ADF Security: RBAC, Owner, Contributor
  • Azure Synapse SQL Pool Security: Logins
  • Creating SQL Logins & Users : master
  • SQL Users in Azure SQL DB and SQL Pool
  • Grant, Control, Revoke: Security Roles
  • Parameters - Creation and Use in Pipelines
  • Dynamic Connections with Credentials
  • User Name and Password Connectivity
  • Dynamic Dataset Configurations
  • Pipeline Expressions with Parameters
  • Resource Classes and Usage with SQL Pool

Real-time Project (End to End)

  • Online Retail Database Data Source
  • Azure Migrations and ETL Concepts
  • Azure SQL Pool (Synapse DWH) Tables
  • Apache Spark Pool : Databases, Tables
  • Azure Data Lake Storage (ADLS Gen 2)
  • Handling Unstructured Data in ADF
  • End to End Workflows, Automations
  • Azure Logic Apps: Automated Workflows
  • Visual Designer & Prebuild Templates
  • Server Less Integrations in Azure
  • Workflow, Triggers and Actions
  • Managed Connectors, Integrations
  • ARM Template : Deployments
  • ARM Templates : ADF, ADLS
  • ADLS with Spark Databases
  • Aggregations with Big Data Loads
  • Parameterized ETL Sources
  • Parameterization & Workflows
  • Python Notebooks to Scala
  • Azure SQL DB Connections
  • ARM Templates & JSON
  • Project Requirement
  • Project Solution, FAQs
  • Concept wise FAQs
  • Resume Guidance
  • Mock Interviews (1 to 1)
  • DP 203 Certification Guidance
  • DP 203 Sample Papers (Latest)

Chapter 11:  Change Data Capture (CDC)

  • Change Data Capture (CDC) Data Loads
  • Incremental Loads with CDC Types
  • SQL Server CDC : ETL Load Dates
  • Pipeline Expression, Data Window
  • JSON Parameters, Pipeline Scheduling
  • ETL Optimization Techniques
  • Serverless Pool in Azure Synapse
  • Connections, Use with Serverless Pool
  • Using Azure OpenDatasets in Synapse
  • OPENROWSET and BULK Data Loads
  • Working with Parquet Files in Synapse
  • Python Notebooks (Pyspark) in Synapse

Azure Data Engineering with Power BI (For Power BI Registrations)

  • Power BI with Synapse SQL Pool
  • Power BI with Synapse Analytics
  • Get Data: Storage Modes
  • Direct Query, Performance Inspector
  • Aggregated Data Analytics
  • Data Gateways : Auto Refresh
  • Power BI with ADLS : Record Query
  • Power BI with ADLS : BLOB Data
  • Power BI with Spark DB : JDBC
  • Power BI with Spark DB : User Tken
  • Power BI with Spark DB : LIVE Data
  • Power BI with Spark DB : Refresh
  • Azure Purview : Data Governance
  • Unified SaaS for Multi Cloud
  • Data Mapping and Resilence
  • Automated Data Discovery
  • Sensitive Data Labels : SQL Server
  • Interactive Data Lineage
  • Trusted Data Discovery in Azure
  • Confidential Data & Trust
  • DataCatalog, Data Estate Insights
  • Azure Key Vaults, ADLS Security
  • Azure Passwords, Keys, Certificates
  • Azure Key Vaults - Name, Vault URI
  • Managed Key & ETL Connections
Part 1: Power BI Report Design
Part 2: Power Query, Cloud (Service)
Part 3: DAX & Report Server

Ch 1: POWER BI INTRODUCTION

  • Power BI : Introduction to Analytics
  • Power BI Tools Suite, Advantages
  • Power BI : Career Options, Plan
  • Power BI Developer Job Role
  • Microsoft Data Analyst Job Role
  • Big Data Analyst Job Role
  • Power BI Data Analyst (PL 300)
  • Data Engineer*, Power BI (DP 500 *)
  • Artificial Intelligence (AI) Visuals
  • AI Enabled Power BI Features
  • Course - Lab Plan with Design Tools
  • Need for Power Query & DAX
  • Power BI Licensing Types
  • Power BI Cloud - Advantages
  • Power BI Report Server Advantages

Ch 7: POWER QUERY LEVEL 1

  • Power Query M Language Purpose
  • Power Query Architecture and ETL
  • Data Types, Literals and Values
  • Power Query Transformation Types
  • Table & Column Transformations
  • Text & Number Transformations
  • Date, Time and Structured Data
  • let, source, in statements @ M Lang
  • Get Data, Table Creations and Edit
  • ETL Operations with Power Query
  • Merge Transformations in Power BI
  • Join Kinds: Inner, Outer & Apply
  • Union All Transformation & Appends
  • Power Query Editor, Step Edits
  • Close & Apply Options. Report Design

Ch 13: DAX Functions - Level 1

  • DAX : Importance in Real-time
  • DAX Data Types, Syntax Rules
  • DAX Measures and Columns
  • ROW Context and Filter Context
  • Operators, Special Characters
  • DAX Functions, Vertipaq Engine
  • DAX Cheat Sheet : Expressions
  • Data Analytics with DAX
  • DAX Measures : Expressions
  • ISBLANK, IF, IN, SUM
  • SUMX, AVG, AVERAGEX
  • Data Models: Fact, Dimensions
  • Detecting Relations for DAX
  • Star & Snowflake Schemas
  • Data Modeling Options in DAX

Ch 2: Basic Report Design

  • Power BI Eco System: Architecture
  • Data Sources & Types in Real-world
  • Report Types: Interactive, Paginated
  • Analytical Reports & Mobile Reports
  • Data Sources : File, Database, Web
  • Visualizations : Report Shapes
  • Power BI Design Tools, Requirements
  • Power BI Desktop Tool : Installation
  • Desktop Interface: Overview, Canvas
  • Get Data, Data View, Report View
  • In-Memory Xvelocity Database
  • Basic Visuals: Table, Tree Map
  • Data Labels, Legend, Category
  • Local Store: PBIX & PBIT Files
  • Data Points and Tooltips

Ch 8: POWER QUERY LEVEL 2

  • Query Duplicate, Query Reference
  • Group By and Advanced Options
  • Aggregations with Power Query
  • Transpose, Header Promotion
  • Reverse Rows and Row Count
  • Data Type Changes & Detection
  • Replace Columns: Text, NonText
  • Advanced Query Edit Options
  • Replace Nulls: Fill Up, Fill Down
  • Pivot, Unpivot Transformations
  • Move Column and Split Column
  • Date & Time Transformations
  • Derive Year, Quarter, Month, Day
  • Add Column : Query Expressions
  • Query Step Inserts and Step Edits

Ch 14: DAX Functions - Level 2

  • Quick Measures in Power BI
  • Average and Filtered Average
  • Running Totals, EARLIER( )
  • RELATED, COUNTROWS
  • CALCULATE Function Conditions
  • ALL Members Scope & IN
  • Account and Time Calculations
  • Star Rating, DAX Expressions
  • Data Modeling Options in DAX
  • 1:1, 1:M and M:1 Relations
  • Working with Facts & Measures
  • Modeling : Missing Relations
  • Relationships & Importance
  • Modeling : Relation Management
  • Modeling with Multiple Keys

Ch 3: Visual Interaction, Visual Sync

  • Visual Interaction with Data Points
  • Disabling / Enabling Interactions
  • Edit Interactions: Format Options
  • Spotlight and Focus Mode
  • Report Export to CSV, PDF
  • Tooltip Options and Usage
  • Working with Pages in PBI
  • Rename, Duplicate, Hide Pages
  • Slicer Visual : Real-time Usage
  • Orientation, Selection Properties
  • Slicer Settings : Tiles & Slider
  • Single & Multi Select, Header
  • Number, Text, Show Summary
  • Date Slicer and Value Selections
  • Slicer List, Dropdowns & Clear

Ch 9: POWER QUERY LEVEL 3

  • Big Data Loads : Parameter Queries
  • Creating Parameters in Power Query
  • Parameter Data Types, Default Lists
  • Static & Dynamic Lists: List Queries
  • Convert Tables to Lists, Use Cases
  • Linking Parameters to Queries
  • Testing Parameters with Canvas
  • Multi-Valued Parameter Lists
  • Creating Lists in Power Query
  • Converting Lists to Table Data
  • Invoke Function, Type Conversions
  • Function Query & Parameter List
  • Columns From Examples, Indexes
  • Conditional Columns, Expressions
  • Disable / Enable Data Loads

Ch 15: DAX Functions - Level 3

  • DAX : Variables and Expressions
  • Dynamic Expressions, RETURN
  • Current Value, Previous Value
  • SELECTED VALUE, Joins
  • FORMAT Function with DAX
  • RELATED, Joins in DAX
  • DAX Expressions with SQL DB
  • Time Intelligence Functions
  • Date Dimension : Generation
  • CALENDAR(), DATESYTD()
  • TOTALYTD, TOTALQTD
  • TODAY, DATE, DAY with DAX
  • SELECTEDVALUE, FORMAT
  • Date, Time and Text Functions

Ch 4: Grouping & Hierarchies

  • Grouping : Visuals with Pdf Sources
  • List Grouping and Binning Options
  • Grouping Static / Fixed Data Values
  • Grouping Dynamic / Changing Data
  • Bin Size and Bin Limits (Max, Min)
  • Bin Count and Grouping Options
  • Group with Bins & Clustering
  • Group, Layer with Selection Pane
  • Creating Hierarchies in Power BI
  • Independent, Dependant Drill-Down
  • Drill-Down with Interactive Reports
  • Conditional Drilldowns, Data Points
  • Drill Up Buttons and Operations
  • Expand & Show Next Level
  • Dynamic Data Drills Limitations

Ch 10: POWER BI CLOUD - 1

  • Power BI Cloud Components
  • App Workspaces, Report Publish
  • Reports & Related Datasets Cloud
  • Creating New Reports in Cloud
  • Report Publish, Report Uploads
  • Report Edits and New Reports
  • Report Actions: Downloads
  • Dataset Usage Options in Cloud
  • Dashboards Creation and Usage
  • Pining Visuals and Report Pages
  • Visual Pin Actions in Dashboards
  • Dashboard & LIVE Interactions
  • Media Tiles: Images, Custom Links
  • Q & A Option with Dashboards
  • Pin with Q & A; Standard Visuals

Ch 16: DAX Functions - Level 4

  • RLS: Row Level Security
  • Data Models in Power BI Desktop
  • DAX Roles Creation and Testing
  • DAX Expressions & Operators
  • PBIX Uploads: Power BI Cloud
  • Dataset Security with DAX Roles
  • Entity Sets and Slicing in DAX
  • Dataflows with Power BI
  • Analytical Reports - DAX Usage
  • Creating Data Models with DAX
  • Datasets in Excel and Dashboards
  • Using Excel Analyzer in Power BI
  • Power BI Data Source in Excel
  • Connection Strings and Refresh
  • Analytical Reports - Limitations

Ch 5: Filters & Bookmarks

  • Filters : Types and Usage in Real-time
  • Visual Filter, Page Filter, Report Filter
  • Basic, Advanced and TOP N Filters
  • Category and Summary Level Filters
  • Data / Drill Options, DrillThru Filters
  • Keep All Filters" Options in DrillThru
  • CrossReport Filters, Include, Exclude
  • Drill-thru Filters, Page Navigations
  • Bookmarks : Report Navigations
  • Buttons, Images with Actions
  • Selection Pane, Actions, Text URLs
  • Show Data and See Records
  • Custom Tooltips, Table Visual
  • Table Vs Matrix : Drill-downs
  • Styles, Cell Properties, Databars
  • Conditional Formatting, Divergent

Ch 11: POWER BI CLOUD - 2

  • Report Actions : Share, Subscribe
  • Report Actions : Lineage, Embed
  • Report Actions : Export Options
  • Report Actions : Public User Access
  • Dashboard Actions : Share, Subscribe
  • Dashboard Actions : Themes, Lineage
  • Dashboard Actions : Share, Subscribe
  • Favorite, Insights, Embed Code
  • Gateways Configuration, PBI Service
  • Gateway Types, Cloud Connections
  • Gateway Cluster, Add Data Sources
  • Data Refresh : Manual, Scheduled
  • Power Query Parameters, Gateways
  • DataFlows, Power Query in Cloud
  • Lineage, Share, Subscribe, Insights
  • Performance Inspector& Gateways

Ch 17: Power BI Report Server

  • Power BI Report Server Config
  • SQL Server Instance Verifications
  • Report Server DB, Temp Database
  • WebService & WebPortal URL
  • Uploading Interactive Reports
  • End User Report Share (pdf)
  • Power BI Desktop RS Tool
  • Interactive Reports: Report Server
  • Mobile Reports : Design Options
  • Mobile Reports : Grids, Elements
  • Mobile Reports : Uploads, Edits
  • Paginated Reports : Deployments
  • Paginated Vs Interactive Reports
  • Paginated Vs Analytical Reports
  • Paginated Vs Mobile Reports
  • Power BI Report Server Vs Cloud

Ch 6: Big Data Access, Visuals

  • OLTP Databases, Big Data Sources
  • Azure Database Access, Reports
  • Import, Direct Query & Dual Mode
  • Data Modeling: Do Not Summarize
  • Data Modeling: Currency, Relations
  • Power BI Archtiecture, Eco System
  • Power BI Interface for Reports
  • Stacked Chart, Clustered Chart
  • Line Chart, Area Chart, Bar Chart
  • 100% Stacked Bar & Column Chart
  • Map Visuals: Tree, Filled, Bubble
  • Small Multiples, Legends, Axis
  • Cards, Funnel, Table, Matrix
  • Scatter Chart : Play Axis, Labels
  • Waterfall Chart, Multi Row Cards

Ch 12: POWER BI CLOUD - 3

  • Workbooks : Excel Online & Pins
  • Power BI Apps: Creation & Usage
  • Power BI Segments, Content
  • Navigation Screens, Audience
  • App Publish, Verification & Edits
  • Export, Share & Subscribe
  • List View & Lineage View Options
  • Power BI Scorecards: Realtime Use
  • Paginated Reports - Design & Usage
  • Power BI Report Builder Tool
  • Microsoft Report Builder Tool
  • Report Builder : Datasets, Charts
  • Report Builder : Bar Charts, Fields
  • Report Builder : Creating RDL Files
  • Paginated Reports : Deployments

Ch 18: Power BI Admin & AI

  • Power BI Cloud Management
  • Power BI Admin : Alerts
  • Workspace Management, Users
  • Security: Report, Dataset Levels
  • Security: Dataset, App Levels
  • Security: Workspace Options
  • PBI Performance Inspector
  • Power BI & Artificial Intelligence
  • Power BI & CoPilot Add-Ins
  • AI Visuals & Big Data Analytics
  • Smart Narrative and Q & A
  • Infographics, Icons and Labels
  • Key Influencer Visual in Power BI
  • Metrics Visual, Performance
  • Paginated Reports Visual

Power BI : Realtime Project (Sales - Retail)

Phase 1 : Basic Report Design

  • Project Requirement Analysis
  • Requirement Gathering, FSA
  • Report Design with Excel
  • Basic Data Modelling
  • Infographics, Histograms
  • Analytics and Formating

Phase 2 : SME Level

  • Report Design with SQL DB
  • SQL Database : Joins, Views
  • Dual Storage Mode, SQL Queries
  • Data Modeling, Power Query
  • Dynamic Connections, Azure DB
  • Parameters and M Lang Scripts

Phase 3: Deployments (Cloud, Server)

  • DAX Requriements, Analysis
  • Cloud and Report Server
  • Custom Visualizations
  • 3party Visuals & REST API *
  • Project FAQs and Solutions
  • One - One Resume, Mock Interview
 
 

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