Snowflake Training: 100% Real-time, Practical

Complete Practical and Real-time Snowflake Training with Basic to Advanced Cloud Concepts, ETL (Extract, Transform and Load), DWH (DataWarehouse), Big Data Storage, Big Data Analytics, Incremental Loads, External Stages, SnowPipes, Failsafe Concepts, Data Recovery, Security Management, ETL Management, Administration, Azure Integrations, Power BI Integrations, External Data Connections and more..

This Snowflake Training Classes also includes Concept wise FAQs, Real-time Project, Project Solution, Project FAQs for your Job Interviews, Snowflake Core Certification Guidance with Real-Life Scenarios.

Snowflake Training Plans

  PLAN A PLAN B PLAN C
  1. Snowflake 1. SQL Server TSQL
2. Snowflake
1. SQL Server TSQL
2. Snowflake
3. Azure Data Engineer
No of Weeks 4 Weeks 7 Weeks 14 Weeks
Snowflake: DWH and ETL
Snowflake: Cloud Configurations
Snowflake: DWH Architecture
Snowflake: Database, Schemas
Snowflake: Constraints, SP, Tasks
Snowflake: Partitions & Tuning
Snowflake: Security Management
Snowflake: Automated Backups
Snowflake: SnowSQL Concepts
Snowflake: Variables & Tasks
Snowflake: File Formats, Stages
Snowflake: External Stages
Snowflake: Azure Integration
Snowflake: Snow Pipes & Upserts
Snowflake: Power BI Analytics
TSQL: Database Basics, T-SQL
TSQL : Constraints, Joins, Queries
TSQL: Views, Group By, Self Joins
TSQL: SPs, Udf, Transaction Basics
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 : Workspace, Delta Tables
DP 203 Certification Guidance
DP 500 Certification Guidance
Total Course Fee
( Payable in Installments)*
INR 15000
USD 200*
INR 20000
USD 300*
INR 44000
USD 500*

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

SQL Server & T-SQL Schedules
S No Time (IST, Mon - Fri) Start Date  
1 6 AM - 7 AM Feb 21st Register
2 8 AM - 9 AM Mar 27th Register
3 9 AM - 10 AM Mar 19th Register
4 6 PM - 7 PM Feb 26th Register
5 8 PM - 9 PM Mar 12th Register
6 9 PM - 10 PM Mar 5th Register
Snowflake Training Schedules
S No Time (IST, Mon - Fri) Start Date  
1 9 AM - 10 AM Feb 21st Register

Snowflake Training Highlights :

  • DWH & Snowflake Introduction
  • Snowflake Architecture
  • Snowflake Service Form
  • Snowflake Objects
  • Using SnowSQL CLI, Snowflake Data
  • Sequence in Snowflake
  • Snowflake Stream & Tasks
  • SnowSQL Operators, SnowSQL Concepts
  • Data Loads & Validations
  • Staging Operations with Snowflake
  • Unloading Data from Snowflake
  • Virtual Warehouse in Snowflake
  • Replication and Business Continuity
  • Security Management with Snowflake
  • Snowflake with Azure Data Factory
  • Managing Governance in Snowflake
  • End to End Real-time Project @ Resume
 

If none of the above schedules work for you, please opt for Snowflake Video Training

 

Snowflake Training Course Contents:

 

Ch 1: DATABASE INTRODUCTION

  • Databases Introduction & Purpose
  • Database Types : OLTP, DWH, OLAP
  • Microsoft SQL Server Advantages, Use
  • SQL Server Components and Usage
  • Microsoft SQL Server - Career Options
  • Developer, DBA, Data Engineer
  • Data Analyst, Data Scientist Careers
  • SQL : Purpose, Real-time Usage Options
  • SQL Versus Microsoft T-SQL [MSSQL]
  • Course Plan, Real-time Project, Resume
  • 24 x 7 Online Lab for Remote DB Access
  • Versions and Editions of SQL Server
  • SQL Server Pre-requisites : S/W, H/W
  • System Configuration Checker Tool

Ch 5: SQL Basics - 3, TSQL INTRO

  • Database Objects : Tables and Schemas
  • Schemas : Group Tables in Database
  • Schemas : Security Management Object
  • Creating Schemas & Batch Concept
  • Using Schemas for Table Creation
  • Data Storage in Tables with Schemas
  • Data Retrieval & Usage with Schemas
  • Table Migrations across Schemas
  • Import and Export Wizard in SSMS
  • Data Imports with Excel File Data
  • Performing Bulk Operations in SSMS
  • Temporary Tables : Real-time Use
  • Local and Global Temporary Tables
  • # and ## Prefix, Scope of Usage

Ch 9: Functions, Procedures Basics

  • Functions with SQL Server, TSQL
  • Scalar, Inline, Table Functions
  • Variables: Declare, Real-time Use
  • Creating, Executing Functions
  • Functions for Computations
  • Functions for Parameterized Joins
  • Procedures: Usage in Real-time
  • Using Parameters in SQL Server
  • Parameterized Joins in TSQL
  • Compilation with Stored Procedures
  • sp_help, sp_helptext, sp_helpindex
  • sp_helpdb, sp_rename, sp_recompile
  • System Views For Metadata Audits
  • DBID, DBName, ObjectID, ObjectName

Ch 2: SQL SERVER INSTALLATION

  • SQL Server & SSMS Installation Plan
  • SQL Server Pre-requisites : S/W, H/W
  • SQL Server 2022 & 2019 Installation
  • Database Engine Feature, OLTP
  • Instances : Types and Properties
  • Default Instance, Named Instances
  • Service and Service Account Use
  • Authentication Modes and Logins
  • Windows Logins and SQL Logins
  • SQL Server Management Studio
  • Server Connections with SSMS Tool
  • Local and Remote Connections
  • System Databases: Master and Model
  • MSDB, TempDB, Resource Databases

Ch 6: Constraints, Index Basics

  • Constraints and Keys - Data Integrity
  • NULL, NOT NULL Property on Tables
  • UNIQUE KEY Constraints: Importance
  • PRIMARY KEY Constraint: Importance
  • FOREIGN KEY Constraint: Importance
  • REFERENCES, CHECK & DEFAULT
  • Candidate Keys and Identity Property
  • Database Diagrams and ER Models
  • Relationships Verification and Links
  • Indexes : Basic Types and Creation
  • Index Sorting and Search Advantages
  • Clustered and NonClustered Indexes
  • Primary Key and Unique Key Indexes
  • Need for Indexes - working with Keys

Ch 10: TRIGGERS & TRANSACTIONS

  • Triggers - Purpose, Real-world Usage
  • FOR/AFTER Triggers - Real time Use
  • INSTEAD OF Triggers - Real time Use
  • INSERTED, DELETED Memory Tables
  • Using Triggers for Data Replication
  • Enable Triggers and Disable Triggers
  • Database Level, Server Level Triggers
  • Transactions : Types, ACID Properties
  • Transaction Types and AutoCommit
  • EXPLICIT & IMPLICIT Transactions
  • COMMIT and ROLLBACK Statements
  • Batch Concept and Go Statement
  • Open Transactions in Real-time
  • Using Conditional Commits, Rollbacks

Ch 3: SSMS Tool, SQL BASICS - 1

  • Creating Databases: Files [MDF, LDF]
  • Creating Tables in User Interface
  • Data Insertion & Report in User Interface
  • SQL : Purpose and Real-time Usage
  • SQL Versus T-SQL : Basic Differences
  • DDL, DML, SELECT, DCL and TCL
  • Creating SSMS Sessions : SPID
  • Create, Connect Databases using SQL
  • Creating Tables with INT, CHAR
  • Data Storage, Inserts - Basic Level
  • Table Data Verifications with Select
  • SELECT Statement for Table Retrieval
  • Identify Databases and Tables
  • Identify Sessions and Session ID

Ch 7: Joins Basics, TSQL Queries

  • JOINS - Table Comparisons Queries
  • INNER JOINS For Matching Data
  • OUTER JOINS For (non) Match Data
  • Join Queries with "ON" Conditions
  • Left Outer Joins - Example Queries
  • Right Outer Joins - Example Queries
  • FULL Outer Joins: Realtime Scenarios
  • CROSS JOIN and CROSS APPLY
  • One-way, Two way Data Comparisons
  • Using Table Aliases & Column Aliases
  • Optimizing Join Queries with Indexes
  • Choosing Correct Comparison Columns
  • Joining Unrelated Tables in TSQL
  • Self References, Self Joins in TSQL

Ch 11:  Normal Forms, Cursors

  • First Normal Form and Atomicity
  • Third Normal Form and MVD Property
  • Boycee-Codd Normal Form : BNCF
  • Fourth Normal Form : Advantages
  • Self Reference Keys and 4 NF Usage
  • 1:1, 1:M, M:1, M:M Relationship Types
  • Linked Servers Configurations, RPC
  • Linked Servers, Remote Joins in TSQL
  • 2 Part, 3 Part, 4 Part Naming Styles
  • Remote Joins Queries and Aliases
  • Cursors - Basics, Data Operations
  • Cursors - Life Cycle & Declaration
  • Cursors Types, FETCH Operations
  • Cursors - Deallocate, Real-world Use

Ch 4: SQL BASICS - 2

  • Creating Tables: VARCHAR, FLOAT
  • Single Row Inserts, Multi Row Inserts
  • Rules for Data Insertion Statements
  • SELECT with WHERE Conditions
  • AND and OR Operators Usage
  • IN Operator and NOT IN Operator
  • Between, Not Between Operators
  • LIKE and NOT LIKE Operators
  • ORDER BY, TOP & OFFSET
  • Basic Sub Queries with SELECT
  • UPDATE Statement & Conditions
  • DELETE & TRUNCATE Statements
  • ALTER, ADD COLUMN Statements
  • DROP Statements: Table, Database

Ch 8: Group By in TSQL, Views Basics

  • GROUP BY: Importance, Realtime Use
  • GROUP BY Queries and Aggregations
  • Group By Queries with Having Clause
  • Group By Queries with Where Clause
  • Using WHERE and HAVING in T-SQL
  • Group By with Joins in TSQL
  • Query Execution Order & Aliases
  • Joins with Sub Queries, Formatting
  • Database Objects: Overview & Usage
  • Views: Types, Usage in Real-time
  • Creating, Executing & Verifying Views
  • Storing Queries in Database Views
  • Excel Analytics - Joins & Views
  • Excel Office Data Connection Reports

Ch 12: TSQL Queries, SQL Analytics

  • IIF() Function with SELECT Query
  • WHEN..THEN..ELSE
  • WHEN MATCHED, NOT MATCHED
  • Incremental Loads, Upsert Statement
  • Stored Procedures: Merge Statement
  • UNION and UNION ALL Operator
  • Window Functions: Rank, Dense Rank
  • Row_Number, PartitionBy in TSQL
  • Duplicate Row Identification, Deletion
  • Grouping, Cube, Rollup, Lag, Lead
  • Data Types: Numerical, Date, Time
  • Data Types: Characters, Real, Float
  • Date & Time Functions, DateAdd
  • String Functions, Concat, SubString
Case Study 1: Database Design with Tables,
Constraints, Keys & Relations
Case Study 2: Joins with Group By,
Sub Queries, Views, Excel Analytics

Mod 1: Snowflake Architecture, Basics

Mod 2: Snowflake Concepts, Admin

Mod 3: ETL, Stages & Pipes

Ch 1: Introduction to Cloud & Snowflake

  • Database Introduction & Database Types
  • OLTP, OLAP and Warehouse Databases
  • Need for Datawarehouses; Advantages
  • Popular Database Technologies
  • Popular Datawarehouse Technologies
  • Need for Cloud & Remote DWH Store
  • Cloud Implementation Types & Usage
  • IaaS: Infrastructure As A Service
  • PaaS: Platform As A Service
  • SaaS: Software As A Service
  • Snowflake Cloud: Introduction
  • Snowflake: Popular SaaS Platform
  • Cloud Datawarehouse Concepts
  • Advantages, Dependencies in Snowflake

Ch 7: Constraints and Data Types

  • Snowflake Constraints, Data Validations
  • NULL and NOT NULL Properties
  • Unique, Primary and Foreign Keys
  • Working with Named Constraints
  • Single Column, Multi Column Constraints
  • Inline and Out Of Line Constraints
  • Table Constraints Types, Real-time Use
  • Constraint Properties: ENFORCED
  • DEFERRED, IMMEDIATE Options
  • Numeric, String and Binary Data Types
  • Boolean Data Types and Usage
  • Date and Time Data Types
  • Semi Structured Data Types
  • Geospatial & Variant Data Types

Ch 13: Snowflake Tasks, Partitions

  • Snowflake Tasks and Real-time Use
  • Snowflake Serverless Compute Model
  • User Managed & Snowflake Managed
  • Tasks Tree: Root and Link Concepts
  • Directed Acyclic Graph (DAG)
  • Tasks Schedules and RESUME Options
  • CREATE, ALTER, DESCRIBE, SHOW
  • CREATE TASK … AFTER Statement
  • ALTER TASK … ADD AFTER Statement
  • CRON Syntax with Tasks, Procedures
  • Virtual Warehouse Concepts (DWH)
  • Multi Cluster Warehouse Config
  • Client Utilities, Drivers & Connectors
  • Auto Scale Options, Billing (Pricing)

Ch 2: Snowflake Account & Editions

  • Creating Snowflake Account (Cloud)
  • Snowflake Trail Account: Limitations, Uses
  • Snowflake Account Components
  • Cloud Platform: Azure, AWS, Google Cloud
  • Regions and Availability with Snowflake
  • Snowflake Editions, Credits; UI Usage
  • Default Accounts; Web UI & Snow Sight
  • Snowflake Storage: Ondemand, Capacity
  • Snowflake Editions and Comparisons
  • Standard Edition: Features, Advantages
  • Enterprise Edition: Features, Advantages
  • Business Critical Edition: Features, Usage
  • Virtual Private Edition (VPS) & Pricing
  • Snowflake Pricing: Ondemand Vs Capacity

Ch 8: Snowflake Cloning (Zero Copy)

  • Cloning Operations with Snowflake
  • Zero Copy and Schema Level Cloning
  • Real-time Uses: Cloning in Snowflake
  • Snapshot Concept, Metadata Options
  • Possible Objects for Snowflake Clone
  • Permissions for Snowflake Cloning
  • Accessing, Controlling Cloned Objects
  • Real-time Considerations @ Cloning
  • Storage Layer and Metadata Layer
  • Cloning with Foreign Key Constraints
  • Cloning Snowflake Databases
  • Cloning Snowflake Schemas, Tables
  • Security and MANAGE GRANTS
  • Cloning and COPY GRANTS Options

Ch 14: SnowSQL and Variables

  • SnowSQL: Concepts, Client Installation
  • SnowSQL Tool: Configuration Options
  • SnowSQL: Account Authorization
  • DDL, DML and SELECT Operations
  • Snowflake SQL Query Syntax Format
  • SnowSQL Command Line Options
  • Working with DB, Schema and Tables
  • Snowflake Variables, Batch Processing
  • Snowflake SQL Data Types, Usage
  • DECLARE, LET, BEGIN and END
  • EXECUTE IMMEDIATE, FOR, END FOR
  • Creating Warehouse, Database, Tables
  • Granting Permissions, Query Execution
  • Writing Output to Files (Win, MAC OS)

Ch 3: Architecture, Warehouse (DWH)

  • Snowflake Architecture & Compute
  • Shared Disk, Shared Nothing Architecture
  • Cluster Nodes and Snowflake Clusters
  • CPU & Memory Resources in Clusters
  • Disk Storage and Network Communication
  • Storage Layer and Cloud Service Layer
  • Database Query Layer & Data Cycle
  • Snowflake Datawarehouse Architecture
  • MPP: Massively Parallel Processing
  • Compute and Storage Components
  • Column Store and Virtual Warehouse
  • Datawarehouse Creation in SnowSight
  • Classic UI with Snowflake; Navigations
  • Data Load and Billing; Auto Suspend

Ch 9: Snowflake Procedures & Views

  • Snowflake Procedures and Functions
  • Creating and Using Stored Procedures
  • Using CALL Command in Snowflake
  • SQL and JavaScript Options with SPs
  • Snowflake Deferred Name Resolution
  • Overloading with Snowflake SPs
  • Transactions & Injection
  • Variables & CALL Command
  • Using execute() with Procedures
  • sqlText:command, createStatement
  • Cursoring Operations
  • Dynamic DML Operations with SPs
  • Loops & next.scan()
  • RETURN and RETURNS Statements
  • Views & Query Storage
  • DML and SELECT Operations on Views
  • Regular Views, System Predefined Views
  • Recursive Views, Parameterized Views

Ch 15: Snowflake Partitions, Stages

  • Snowflake Partitions, Real-time use
  • Service and Storage Layer Concepts
  • Micro Partition with DML, CDC
  • Cluster Key: Usage, ReClusters
  • Depth and Overlap Properties
  • Internal Partition Types & Usage
  • List, Range and Hash Partitions
  • SYSTEM$CLUSTERING_INFORMATION
  • Snowflake Stages and Usage
  • Internal and External Stages
  • User Stages : Creation, Usage
  • Table Stages: Creation, Usage
  • Internal Named Stages, Usage
  • COPY Command, Bulk Data Loads

Ch 4: Snowflake Databases & Tables

  • Snowflake Databases and Data Storage
  • Need for Snowflake Warehouse, Compute
  • Database Objects and Hierarchy
  • Snowflake Worksheet Parameters
  • Database Creation with Snowflake UI
  • Retention Time, DB List & Connections
  • Permanent and Transient Databases
  • Snowflake Tables and their Usage
  • Permanent, Transient & Temp Tables
  • Creating Tables with SnowSight
  • Describe Options, Data Inserts
  • CREATE TABLE AS SELECT
  • Cloning Tables, Case Sensitivity Options
  • Collation, ALTER, DROP & UNDROP

Ch 10: Security Management

  • Security Management Concepts
  • Security Entities with Snowflake
  • Securable Objects, Users & Roles
  • Prevligies and Privilege Groups
  • Snowflake Security Hierarchy
  • Organization, Account, Users, Roles
  • Schema, Tables, Other DB Objects
  • Creating and Using Roles, Users
  • System Defined Roles and Usage
  • Role Hierarchy and Dependency
  • Creating and Working with Users
  • Auditing Users and Password Policy
  • RBAC: Role Based Access Control
  • DAC: Discretionary Access Control

Ch 16: Azure & External Storage

  • Working with Azure Storage
  • Azure Subscription, Resources
  • Create, Use Azure Storage Account
  • Storage Containers, BLOB Data
  • Using SnowSQL with Azure BLOB
  • SAS [Shared Access Signature]
  • Using SAS Key and FILE PATH
  • External Stages in Snowflake
  • File Formats: Creation, Usage
  • Creating & Using External Stages
  • Azure Storage with BLOB
  • COPY INTO Command Usage
  • Listing Stages with Snowflake
  • Snowflake Patterns & RegEx

Ch 5: Time Travel & Transient Tables

  • Time Travel Feature in Snowflake
  • DML Operations and Silent Audits
  • Continuous Data Protection Life Cycle
  • Invoking Time Travel Feature in Snowflake
  • Timestamp, Offset & Query ID Options
  • Time Travel using Offset Feature
  • Time Travel using Query ID Feature
  • Data Recovery using TIMESTAMP
  • Using OFFSET options in Real-world
  • Fail Safe and UNDROP Operations
  • Transient Tables and Real-time Usage
  • Permanent Table and Real-time Usage
  • Restrictions with Permanent Tables
  • Identical Names and Naming Conflicts

Ch 11: Snowflake Transactions

  • Working with Transactions (ACID)
  • Atomicity, Consistency, Isolation
  • Durability and Data Storage Options
  • Transaction Types with Snowflake
  • Implicit, Explicit and Auto Commit
  • BEGIN TRANSACTION & COMMIT
  • DDL Statements and Transactions
  • BEGIN WORK Versus START
  • current_transaction() and Usage
  • to_timestamp_ltz and Usage
  • Failed Transactions with SPs
  • Batches Versus Transactions
  • Transactions with Stored Procedures
  • Scoped and INNER Transactions

Ch 17: SnowPipes & Incremental Loads

  • Snowflake SnowPipes: Incremental Loads & SnowPipe
  • Creating Azure Account For BLOB
  • Creating Container, Generate SAS Key
  • Creating Azure Queue and Event Grid
  • Notification Integration in Snowflake
  • Integrations: Show, Describe, Use
  • Azure Active Directory & IAM
  • Linking Azure AD with Snowflake
  • Enterprise Application, Authentication
  • File Formats with Regular Expr
  • External Data Stages
  • Data Unloading Concepts
  • PUT and GET with SnowSQL
  • Bulk Unloads; Data Preparation & Stages
  • Data Unloading to User Stages
  • Data Unloading to Table Stages

Ch 6: Schemas and Session Context

  • Schemas: Creation, Real-time Usage
  • Permanant and Transient Schemas
  • Managed Schemas in Real-time Usage
  • Verifying and Cloning Snowflake Schemas
  • Invoking Schemas & Cloning Operations
  • ALTER SCHEMA.. IF EXISTS Options
  • Creating, Working with Managed Schema
  • Snowflake Sessions (Workspaces)
  • Session Context: Role and Warehouse
  • Session Context: Database & Schema
  • Working with Fully Qualified Names
  • CTAS: Create Table As Select
  • Data Loading with GUI, SQL Scripts
  • Using Query and History Tab in GUI

Ch 12: Snowflake Streams & Audits

  • Working with Snowflake Streams
  • Stream Object and DML Auditing
  • Snapshot Creation, Offset Options
  • METADATA$ACTION Parameter
  • METADATA$ISUPDATE Parameter
  • METADATA$ROW_ID Parameter
  • Stream Types: Standard Stream
  • Append Only Stream & Usage
  • Insert Only Stream & Usage
  • Data Flow with Snowflake Streams
  • Auditing INSERT, UPDATE, DELETE
  • show streams; desc streams
  • Streams on Transient Tables
  • Time Travel; Using Stream Tables

Ch 18 : REAL-TIME PROJECT

  • Phase 1:
  • Legacy Data Systems
  • Heterogenous Data Platforms
  • File Data Sources
  • Phase 2
  • Desining DWH in Snowflake
  • Incremental Loads
  • Capacity Planning
  • External Data, Integrations
  • SnowPipes and Azure BLOB
  • Phase 3
  • Data Visualizations, Analytics
  • End to End Implementations
  • Project Solution & FAQs
  • Resume & Certification Guidance

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
 
 
 
 

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