- 4.7
- 5.0
Official Learning Partner
Azure Data Engineer Course: Comprehensive Training
Our Azure Data Engineer Course offers practical, real-time training designed to equip you with essential data engineering skills. This job-oriented program covers a wide range of topics, from basic to advanced data engineering activities.
Course Overview
This course provides a complete practical and real-time learning experience, meticulously designed to equip you with the skills required for a successful career in data engineering. It includes basic to advanced data engineering activities, including ETL (Extract, Transform, Load), Data Warehousing (DWH), Spark, Python for ETL, PySpark, Pipelines with Azure Data Factory and Synapse Analytics, Notebooks with Azure Databricks, Jobs with Azure Stream Analytics and IoT, Security with Azure Key Vaults, IAM, RBAC, and more.
Key Components of the Course
- ETL Processes: Master the fundamentals of Extract, Transform, and Load (ETL) operations, essential for data integration and migration tasks.
- Data Warehousing: Gain in-depth knowledge of Data Warehouse (DWH) concepts, enabling you to design and manage large-scale data storage solutions.
- Big Data Technologies: Learn to work with Apache Spark and Python for ETL processes, including hands-on experience with PySpark for large-scale data processing.
- Azure Data Services: Develop proficiency in building data pipelines using Azure Data Factory and Synapse Analytics, and create interactive notebooks with Azure Databricks.
- Real-Time Analytics: Acquire skills to set up jobs using Azure Stream Analytics and integrate Internet of Things (IoT) data streams.
- Security Practices: Understand security implementations with Azure Key Vaults, Identity and Access Management (IAM), and Role-Based Access Control (RBAC) to safeguard your data solutions.
Real-Time Project and Certification Guidance
This Azure Data Engineer Course includes a real-time project to enhance your resume and practical experience. Additionally, we provide guidance for the DP-203 Microsoft Certification, ensuring you’re well-prepared for the exam.
Course Highlights
- Azure Fundamentals: Build a strong foundation in Azure services.
- Azure SQL Concepts: Dive deep into SQL Server and T-SQL queries.
- Azure Active Directory: Learn about identity and access management.
- Azure Monitor: Monitor and optimize your data solutions.
- Data Integration Tools: Master Azure Data Factory and Synapse.
- Data Storage Solutions: Explore Data Lake Storage and Cosmos DB.
- Real-Time Processing: Implement Stream Analytics and IoT solutions.
- Programming Skills: Enhance your Python and Scala capabilities.
- Security Practices: Understand Azure Key Vaults and RBAC.
- Big Data Processing: Work with Azure Databricks and Spark clusters.
Why Choose Our Azure Data Engineer Course?
- ISO Certified Training Institute: We adhere to high-quality education standards.
- Hands-On Experience: Emphasis on practical learning with real-world projects.
- Expert Instruction: Learn from industry professionals with extensive experience.
- Flexible Learning Options: Choose between self-paced and live online training modes.
For a visual overview of our training offerings, consider watching this introductory video
Training Modes:
Self Paced VideosLIVE Online
Trainer: Mr. Sai Phanindra Tholeti
Profile: https://www.linkedin.com/in/saiphanindra/
Training Highlights:
- Azure Fundamentals
- Azure SQL Concepts
- Azure AD
- Azure Monitor
- Azure Data Factory
- Azure Synapse
- End to End Real-time Project @ Resume
- Data Lake Storage
- Stream Analytics
- Azure Cosmos DB
- Python, Scala
- Azure Synapse
- Azure AD
- Azure Migrations
- Azure Key Vaults
- Azure Notebooks
- Azure Storage
- Data Lake Analytics
- IoT, Event Hubs
- Azure Databricks
- Spark Clusters
Azure Data Engineer
Training Course Contents:
Module 1: SQL Server & T-SQL Queries Training Content
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 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 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 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 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 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 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 8: Group By, Views & Excel
- 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 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 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 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
- Computed Columns, Variant Type
- 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 12: TSQL Merge, Cursors
- 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
Module 2: Azure Data Engineer Training Content
Part 1: Azure Data Factory, Synapse Analytics
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 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 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 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 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 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 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 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 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 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
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
Part 2: Data Lake Storage, Stream Analytics
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 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 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 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 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
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 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 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 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
Real-time Project (Azure Data Engineer)
- 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
Part 3: Databricks, Spark, Python
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: 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: 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: 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
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: 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: 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: 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: 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
Real-time Project (Azure Data Engineer)
- 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)
Module 3: Power BI Training Content
Part 1: Power BI Report Design
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 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 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 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 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 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
Part 2: Power Query, Cloud (Service)
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 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 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 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 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 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
Part 3: DAX & Report Server
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 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 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 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 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 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
Azure Data Engineer Training Plans
Plan A1. Azure Data Engineer | Plan B1. SQL Server TSQL | Plan C1. SQL Server TSQL | |
|---|---|---|---|
| Total Duration | 7 Weeks | 10 Weeks | 14 Weeks |
| 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 | ✔ | ✔ | ✔ |
| SQL: Database Basics, T-SQL | ✖ | ✔ | ✔ |
| TSQL : Constraints, Joins, Queries | ✖ | ✔ | ✔ |
| TSQL : Queries, SProcs, Lock Hints | ✖ | ✔ | ✔ |
| TSQL : Views, Group By, Self Joins | ✖ | ✔ | ✔ |
| TSQL : Excel Analytics, Query Tuning | ✖ | ✔ | ✔ |
| Power BI: Report Design, Visuals | ✖ | ✖ | ✔ |
| Power BI: Power Query (M Lang), DAX | ✖ | ✖ | ✔ |
| Power BI: Report Server, Admin | ✖ | ✖ | ✔ |
| Power BI: Dashboards, RLS, Analytics | ✖ | ✖ | ✔ |
| Power BI: PL-300 Certification Guidance | ✖ | ✖ | ✔ |
| Total Course Fee ( Payable in Installments) | INR 25000USD 350 | INR 30000USD 400 | INR 42000USD 500 |
SQL Server & TSQL Schedules
Azure Data Engineer Training Schedules
Power BI Training Schedules
SQL SCHOOL
24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.
Technical FAQs
Who is SQL School? How far you have been in the training services ?
SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL Server, T-SQL, SQL Server DBA and MSBI (SSIS, SSAS, SSRS) Courses. All our training services are completely practical and real-time.CREDITS of SQL School Training Center
- We are Microsoft Partner. ID# 4338151
- ISO Certified Training Center
- Completely dedicated to Microsoft SQL Server
- All trainings delivered by our Certified Trainers only
- One of the few institutes consistently delivering the trainings for more than 19+ Years online as inhouse
- Real-time projects in
- Healthcare
- Banking
- Insurance
- Retail Sales
- Telecom
- ECommerce
I registered for the Demo but did not get any response?
Make sure you provide all the required information. Upon Approval, you should be receiving an email containing the information on how to join for the demo session. Approval process usually takes minutes to few hours. Please do monitor your spam emails also.
Why you need our Contact Number and Full Name for Demo/Training Registration?
This is to make sure we are connected to the authenticated / trusted attendees as we need to share our Bank Details / Other Payment Information once you are happy with our Training Procedure and demo session. Your contact information is maintained completely confidential as per our Privacy Policy. Payment Receipt(s) and Course Completion Certificate(s) would be furnished with the same details.
What is the Training Registration & Confirmation Process?
Upon submitting demo registration form and attending LIVE demo session, we need to receive your email confirmation on joining for the training. Only then, payment details would be sent and slot would be allocated subject to availability of seats. We have the required tools for ensuring interactivity and quality of our services.
Please Note: Slot Confirmation Subject to Availability Of Seats.
Will you provide the Software required for the Training and Practice?
Yes, during the free demo session itself.
How am I assured quality of the services?
We have been providing the Trainings – Online, Video and Classroom for the last 19+ years – effectively and efficiently for more than 100000 (1 lakh) students and professionals across USA, India, UK, Australia and other countries. We are dedicated to offer realtime and practical project oriented trainings exclusively on SQL Server and related technologies. We do provide 24×7 Lab and Assistance with Job Support – even after the course! To make sure you are gaining confidence on our trainings, participans are requested to attend for a free LIVE demo based on the schedules posted @ Register. Alternatively, participants may request for video demo by mailing us to contact@sqlschool.com Registration process to take place once you are happy with the demo session. Further, payments accepted in installments (via Paypal / Online Banking) to ensure trusted services from SQL School™
YES, We use Enterprise Edition Evaluation Editions (Full Version with complete feature support valid for SIX months) for our trainings. Software and Installation Guidance would be provided for T-SQL, SQL DBA and MSBI / DW courses.
Why Choose SQL School
- 100% Real-Time and Practical
- ISO 9001:2008 Certified
- Concept wise FAQs
- TWO Real-time Case Studies, One Project
- Weekly Mock Interviews
- 24/7 LIVE Server Access
- Realtime Project FAQs
- Course Completion Certificate
- Placement Assistance
- Job Support
- Realtime Project Solution
- MS Certification Guidance
