DP 203 Certification Training

Complete Practical and Real-time Training on DP-203 Training . This Certification Course includes: 1. Azure Fundamentals, 2. Azure Active Directory, 3. Azure SQL Databases, 4. Azure Migrations, 5. Azure Data Factory, 6. Azure SQL Pools (Synapse), 7. Azure Synapse Analytics, 8. Azure Storage, 9. Azure Data Lake Storage, 10. Azure Stream Analytics, 11. Azure Databricks, 12. SparkSQL, 13. PySpark, and 14. End to Implementation with a Real-time Project for your resume & job work. This extensive, 100% practical DP 203 Training coures also includes End to End Real-time Project with Power BI Integrations and complete guidance for DP 203 Microsoft Certification Examination. Register Today for a Free Demo



Azure Data Engineer Training Schedules
S No Time (IST, Mon - Fri) Start Date  
1 7 AM - 8 AM Feb 27th Register
3 7 PM - 8 PM Mar 5th Register

If above schedule does not work, opt for Azure Data Engineer Training Videos

DP 203 Training Highlights :

✔ Azure Fundamentals ✔ Azure AD
✔ Azure SQL Concepts ✔ Azure Migrations
✔ Azure AD ✔ Azure Key Vaults
✔ Azure Monitor ✔ Azure Functions
✔ 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
✔ Azure Notebooks ✔ U-SQL & NoSQL
✔ Python, Scala ✔ Spark Clusters
✔✔ End to End Real-time Project @ Resume
 

DP 203 Training Course Contents:

Mod 1: Azure Data Factory & Synapse Analytics

Mod 2: Azure Data Lake Storage & Stream Analytics

Mod 3: Azure 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
  • Azure Subscription : Filter Options

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()
  • 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 Server 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 & Spark

  • 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
  • Stream Analytics Job Monitoring

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)
  • Pipelines with Databricks Notebooks
  • 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

Above course curriculum applicable for registrations from Sep 1st, 2023

SQL Server T-SQL, Azure SQL, Azure DBA, Azure BI, Azure Data Engineer, Power BI Training

 
 
 
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