#Azure Databricks Course Training
✅ Unified Data Analytics
✅ Apache Spark Engine
✅ Notebook-Based Development
✅ Delta Lake Integration
✅ Real-Time Data Processing
✅ ML Model Training
✅ Collaborative Workspace Environment
✅ Azure Service Integration
✅ Apache Spark Engine
✅ Notebook-Based Development
✅ Delta Lake Integration
✅ Real-Time Data Processing
✅ ML Model Training
✅ Collaborative Workspace Environment
✅ Azure Service Integration
Trainer: Mr. Sai Phanindra Tholeti
www.linkedin.com/in/saiphanindra/
SQL Server & TSQL Schedules
Azure Databricks Course Schedules
Azure Data Engineer
Training Course Contents:
Module 1 : Microsoft SQL (TSQL)
Ch 1: SQL SERVER INTRODUCTION
- Database Introduction
- Types of Databases
- Need for & ETL, DWH
- BI Implementations
- SQL Server Advantages
- Version, Editions of MSSQL
- Data Analyst Job Roles
Ch 2: SQL SERVER INSTALLATIONS
- SQL Server 2019, 2017
- SSMS Tools Installation
- Database Engine (OLTP)
- SCM, Configuration Tools
- Instance Types, Uses
- Authentication Modes
- Collation, File Stream
Ch 3: SQL BASICS – 1
- Need for Databases, Tables
- Need for SQL Commands
- DDL, DML & DQL Statements
- Database Creation @ GUI
- Data Operations @ GUI
- Session ID, SQL Context
- DB, Tables, Data @ SQL
Ch 4: SQL BASICS – 2
- DDL Variants in MSSQL
- DML Variants in MSSQL
- INSERT & INSERT INTO
- SELECT & SELECT INTO
- Basic Operators in SQL
- Special Operators in MSSQL
- ALTER, ADD, TRUNCATE, DROP
Ch 5: Data Imports, Schemas
- Data Imports with Excel
- ORDER BY & UNION
- UNION ALL For Sorting Data
- Creating, Using Schemas
- Real-world Banking Database
- Table Migrations @ Schemas
- 2 Part, 3 Part & 4 Part Naming
Ch 6 : Constraints, Index Basics
- Need for Constraints, Keys
- NULL, NOT NULL, UNIQUE
- Primary Key & Foreign Key
- RDBMS and ER Models
- Identity Property, Default
- Clustered Index, Primary Key
- Non Clustered Index, Unique
Ch 7: Joins & Views Basics
- JOINS: Purpose. Inner Joins
- Left / Right / Full Outer Joins
- Cross Joins, Query Tuning
- Creating & Using Views
- DML, SELECT with Views
- RLS : WITH CHECK OPTION
- System Views & Metadata
Ch 8: Functions(UDF), Data Types
- Using Functions in MSSQL
- Scalar Value Functions
- Inline & Multiline Functions
- Date & Time Functions
- String, Aggregate Functions
- Data Types : Integer, Char, Bit
- SQL Variant, Timestamp, Date
Ch 9: Stored Procedures,Models
- Stored Procedures & Usage
- Creating, Testing Procedures
- Encryption, Deferred Names
- SPs for Validations, Analysis
- System SPs, Recompilation
- Normal Forms & Types
- Data Models, Self-References
Ch 10: Triggers, Temp Tables
- Need for Triggers
- DDL & DML Triggers
- Using Memory Tables
- Data Replication, Automation
- Local & Global Temp Tables
- Testing & Using Temp Tables
- SELECT .. INTO & Bulk Loads
Ch 11: DB Architecture, Locks
- Planning VLDBs : Files, Sizing
- Filegroups, Extents & Types
- Log Files : VLF, Mini LSN
- Table Location, Performance
- Schemas, Transfer, Synonyms
- Transactions Types, Lock Hint
- Query Blocking Scenarios
Ch 12 : Cursors & CTEs, Links
- Cursors : Realtime Use
- Fetch & Access Cursor Rows
- CTEs for SELECT, DML
- CTEs: Scenarios & Tuning
- Linked Servers, Remote Joins
- Linked Servers: MSDTC, RPC
- Tuning Remote Queries
Ch 13: Merge, Upsert & Rank
- Need for Merge in ETL
- Incremental Loads with SQL
- MERGE and RANK Functions
- Window Functions, Partition
- Identify, Remove Duplicates
Ch 14: Grouping & Cube
- Group By & HAVING
- Cube, Rollup & Grouping
- Joins with Group By
- 3 Table, 4 Table Joins
- Query Execution Order
Ch 15: Self Joins, Excel Analysis
- Self Joins & Self References
- UNION, UNION ALL
- Sub Queries with Joins
- IIF, CASE, EXISTS Statements
- Excel Analytics, Pivot Reports
Module 2: Azure Data Engineer
Ch 1: ETL, DWH Introduction
- Database Introduction Data Warehouse (DWH)
- Data Engineering Work Flow
- Cloud Concepts: IaaS, PaaS
- SaaS & Azure Cloud Concepts
- Azure Resources & Groups
- Storage, ETL, IoT Resources
Ch 2: Azure Intro, Azure SQL
- Azure SQL Server, SQL DBA
- Azure SQL Database (OLTP)
- Azure SQL Pool (DWH)
- Connections from SSMS Tool
- Connections from ADS Tool
- Pause / Resume SQL Pool
- Source Data Configurations
Ch 3: Azure Synapse (DWH)
- Synapse Pool Architecture
- Control Node, Compute Node
- DMS & Partitioned Tables
- Creating Tables with TSQL
- Distributions: RR, Hash, Repl
- Big Data Loads with TQL
- Important DMFs & DMVs
Ch 4: Azure Data Factory (ADF)
- Need for ADF & Pipelines
- Linked Services & IRs
- Datasets, Pipelines, Triggers
- Copy Data Activity & CDT
- Data Loads Pipelines, DTUs
- Pipeline Monitoring, Edits
Ch 5: ADF Incremental Loads – 1
- File Incremental Loads
- Storage Account, Data Lake
- Binary Copy, Schema Drift
- Staging Concept in ADF
- DOCP, Logging & Consistency
- Polybase Concept & Tuning
Ch 6: ADF Incremental Loads – 2
- Implement SCD with ADF
- Self-Hosted IR: Realtime Use
- On-premise Data: Incr Loads
- Copy Method: Upsert, Keys
- Staging & ADF Optimizations
- Pipeline Runs, Activity IDs
Ch 7: ADF Data Flow – 1
- Data Flow Transformations
- Spark Clusters for Debugging
- Optimized Clusters, Preview
- Conditional Split, SELECT
- Sort, Union Transformations
- Pipelines with Data Flow
Ch 8: ADF Data Flow – 2
- Working with Multiple Tables
- Join Transform, Broadcast
- Row Filters, Column Filters
- Surrogate Keys, Derived Cols
- ETL Loads Dates, Sink Options
- Aggregated Data Loads
Ch 9: ADF Data Flow – 3
- Pivot Transformation
- Group By & Pivot Keys
- Column Pattern, Deduplicate
- Lookup, Cached Lookup
- Tuning Transformations
- Tuning Data Flow, Spark
Ch 10: Synapse Analytics – 1
- Azure Synapse Analytics
- Dedicated SQL Pools
- TSQL: Stored Procedures
- Synapse Pipelines, Tuning
- SP Activity in Pipelines, Jobs
- Comparing ADF & Synapse
Ch 11: Synapse Analytics – 2
- Serverless Pools in Synapse
- TSQL Scripts with Serverless
- ADLS Data Imports & ELT
- Synapse Aggregation, Analytics
- Synapse Optimizations
- Synapse Security & Logins
Ch 12: Synapse Analytics – 3
- Apache Spark Pool & Usage
- Synapse Analytics with Pools
- PySpark Staging, Aggregations
- Spark Queries & Python ETL
- Python Notebooks, Pipelines
- Integrating Python with DWH
Ch 13: Parameters, SCD & ETL
- ADF Templates in Realtime
- Table Incremental Loads
- Control Tables, Watermarks
- Pipeline Parameters, SPs
- Dynamic Data Sets, SCD
Ch 14: CDC @ ETL, ELT & Tuning
- Using CDC in ADF
- Control Tables (CT): Upserts
- Handling Inserts, Updates
- SCD Type 1 & Type 2
- ADF, Synapse: Limitations
Ch 15: Azure Intro & Storage
- Storage, ETL, IoT Resources
- Azure Storage Components
- Azure Storage Account, HNS
- Azure Data Lake Storage
- Azure Storage Explorer Tool
- Storage Explorer Config
- Storage Account Properties
Ch 16: Azure Storage Operations
- BLOB Storage: Containers
- Storage Browser, Explorer
- File & Folder Uploads, Edits
- Azure Tables: Row Key
- Partition Key, Timestamp
- Use Cases of BLOB Storage
- Use Cases of Azure Tables
17: Azure Storage Security
- Realtime use of Keys
- Access Keys & Admin Access
- SAS Keys Generation, Ips
- Creating, Using Entra Users
- Azure AD Users, Groups
- IAM & RBAC with Entra Users
- ACLs and ADLS Security
Ch 18: Azure SQL DB Migrations
- On-Premise SQL DB bacpac
- Azure SQL Deployment
- Azure Storage from SSMS
- Azure SQL DB Migration
- Migration Verifications
- Testing Migrations in SQL
Ch 19: Azure Stream Analytics
- Azure IoT Hubs & Devices
- APIs with Connection Strings
- Azure Steam Analytic Jobs
- Inputs, Outputs, SAQL Query
- LIVE Feed: JSON, AVRO Files
- Watermark & LIVE Stats
Ch 20: Azure Stream Analytics
- Azure IoT Hubs & Devices
- APIs with Connection Strings
- Azure Steam Analytic Jobs
- Inputs, Outputs, SAQL Query
- LIVE Feed: JSON, AVRO Files
- Watermark & LIVE Stats
Ch 21: Azure Key Vaults, Alerts
- Azure Encryptions @ REST
- Azure Key Vaults & Keys
- SMK & CMK Encryptions
- Azure Metrics: Ingress
- Egress, E2E Latency Issues
- Performance Tuning Options
Ch 22: Azure Storage Optimization
- BLOB Types & Content Types
- Hot, Cool, Cold, Archive Types
- Creating, Using Access Policies
- Immutable Storage, Rotation
- Containerization, Indexing
- Replication: LRS, ZRS, RA-GRS
Ch 23: Azure Pricing, Functions
- Azure Logic Apps: Usage
- Log Apps Usage in ETL
- Snapshots, Azure Functions
- Azure Functions Realtime Use
- ETL & DWH with Functions
- Azure Resource Pricing
Ch 24: Azure Big Data & Spark
- Azure Big Data & Spark
- Azure ETL & DWH Databases
- Azure Spark, HIVE Metastore
- Azure Databricks Service
- Spark Cluster (Personal)
- Unity Catalog & Azure VM
Ch 25: Spark Cluster Operations
- DBFS: Flat File Imports
- Table Conversions using GUI
- Spark Clusters: Table Creations
- Basic Transformations in Spark
- SQL Notebooks: Creation
- Default DB Queries, Cloning
Ch 26: Python & PySpark, ETL
- Python Fundamentals
- Python Data frames: ETL
- Python for Big Data, Pandas
- Python Notebooks, Views
- Aggregated Loads to Spark
- Spark DB Creations, Tables
Ch 27: PySpark & ADLS, Widgets
- Creating Spark Databases
- Spark Tables, Catalog Info
- PySpark with ADLS Storage
- Using Widgets for ADLS Keys
- PySpark Variables & Widgets
- Using Variables in Functions
- Spark SQL with Control Text
- Using Variables in Spark SQL
Ch 28: ADB Jobs, Delta Tables
- Azure Databrick Jobs
- Azure Workflows & Tasks
- Notebook Schedule Options
- Continuous Jobs, Notifications
- Delta Tables & Data Cleansing
- SCD (Merge Into), Contact, etc.
- Creating, Using Data frames
- Multi Data frame Joins
Ch 29: Scala Notebooks & ETL
- Scala Notebooks: Purpose
- Aggregated Data Loads
- Incremental Data Loads
- Widgets & Jobs with Scala
- Python Versus Scala
- Converting Python to Scala
- JVM Benefits, SQL DB Conn”
- SQL DB Loads with Scala
Ch 30: Databricks Architecture
- Azure Databricks Services
- Cluster Components & DBFS
- RDD, DAG, Photon, Spotlight
- Spark Partitioned Tables
- Cluster Manager: Spark Jobs
- Databricks Runtime (DBR)
- Databricks Security
- Workspace Security
- Notebook & Job Security
Ch 31: Medallion Architecture
- Medallion Architecture in ETL
- DWH Data Loads & Incr Loads
- Bronze, Silver & Gold Data
- Processing Raw Data Files
- Data Cleansing, Formatting
- Aggregation Advantages
- DBES & Node Architecture
- Unity Catalog Concept
- LUNs and Unity Catalog
Ch 32: Delta LIVE Tables (DLT)
- Creating Delta LIVE Tables
- DLT Pipelines in ETL, DWH
- Automated Incr Loads
- Control Tables, Timestamp
- SCD Type 1 with DLT
- SCD Type 2 with DLT
- Automated Merge Into Stmt
- Delta Tables Vs DLT
- Merge Into Vs DLT Pipeline
Module 3: Power BI
Ch 1 : Power BI Introduction
- Reporting Basics & Types
- Interactive,Analytical Reports
- Paginated Reports (RDL)
- Power BI Eco System
- Power BI Tools,Service,Server
- Need for Power Query (M)
- Need for DAX & Cloud
Ch 2: Power BI Basic Reports
- Power BI Desktop Installation
- Basic Report Design (PBIX)
- Data View, Data Models
- Data Points, Aggregations
- Focus Mode, Spotlight, Exports
- ToolTip, PBIX and PBIT
- Visual Interactions & Edits
Ch 3 : Grouping, Hierarchies
- Creating Groups in Power BI
- Groups : Creation & Usage
- Group Edits Options
- Bins & Bin Size, Bin Count
- Hierarchies: Creation, Use
- Drill Down, Drill Up
- Conditional Drill Down
Ch 4 : Visual Sync, Filters
- Slicer & Single Select
- Multi Select Options
- Integer, Character Slicers
- Visual Sync with Slicers
- Filters: Visual, Page, Report
- Drill Thru Filters & Usage
- Basic, Top & Advanced
- Clear Filter Options, Resets
Ch 5 : Bookmarks, Big Data
- Bookmarks Creation & Usage
- Visual Interactions, Bookmarks
- Images : Actions, Bookmarks
- Big Data Access with Power BI
- Storage Modes: Direct Query
- Import & Performance Impact
- Formatting & Data Refresh
- Summary, Date Time Formats
Ch 6 : Power BI Visualizations
- Chart and Bar Visuals
- Line and Area Charts
- Maps, TreeMaps, HeatMaps
- Funnel, Card, Multrow Card
- PieCharts & Settings
- Waterfall, Sentiment Colors
- Scatter Chart, Play Axis
- Infographics, Classifications
Ch 7 : Power Query Level 1
- Power Query (Mashup)
- ETL Transformations in PBI
- Power Query Expressions
- Table Combine Options
- Merge, Union All Options
- Table Transformations
Ch 8 : POWER QUERY LEVEL 2
- Any Column Transformations
- String / Text Transformations
- Numeric Analytics & Mashup
- Date Time Transformations
- Add Column Transformations
- Expressions and New Columns
Ch 9 : POWER QUERY LEVEL 3
- Parameters in Power Query
- Static Parameters, Defaults
- Dynamic Dropdowns, Lists
- Linking with Table Queries
- Column From Examples
- Step Edits, Type Conversions
Ch 10 : Power BI Cloud – 1
- Power BI Cloud Concepts
- Workspace Creation, Usag
- Report Publish & Edits
- Semantic Models in Realtime
- Dashboard Creation, Usage
- Clone, Share, Subscribe
- Q&A, Lineage, Settings
Ch 11 : Power BI Cloud – 2
- Data Gateways, Data Refresh
- Data Source Configurations
- Data Refresh & Scheduling
- Gateway Optimizations
- Semantic Model Optimizations
- Report Optimizations
- Dashboard Optimizations
Ch 12 : Power BI Cloud – 3
- Power BI Apps, Shares
- App Sections & Options
- App Updates, Security
- Excel Analytics
- Data Explorer Option
- Sharing, Subscriptions
- Alerts, Metrics, Insights
Ch 13 : Report Server & DAX
- Power BI Report Server
- Report Database, TempDB
- Web Service & Server URL
- Paginated Reports (RDL)
- Report Builder Tool Usage
- DAX : Purpose, Realtime Use
Ch 14: DAX Level 2
- DAX Measures Creation, Use
- DAX Functions: IIF, ISBLANK
- SUM, CALCULATE Functions
- DAX Cheat Sheet : Examples
- Quick Measures in Power BI
- Running Totals, Filters
Ch 15 : DAX Level 3
- Star Rating Calculations
- Data Models & DAX
- Star & Snowflake Schemas
- Dimensions, Fact Tables
- DAX Expressions & Joins
- DAX Variables, Usage
Ch 16 : DAX Level 4
- Dynamic Report with DAX
- SELECTED MEMEBER
- Time Intelligence with DAX
- PARALLELPERIOD, DATE
- DAX with Big Data
- Big Data Analytics
Ch 17 : Realtime Project Phase 1
- Project Requirement Spec
- Understanding Data, Formats
- Report Pattern Design
- Report Design & Modelling
- Power Query, DAX, Insights
- Analytical Reports in Cloud
Ch 18 : Realtime Project Phase 2
- Complete Project Solution
- Project FAQs, Key Roles
- Real-world Considerations
- Power BI Admin Concepts
- Resume Points, FAQs
- PL 300 Exam Guidance

SQL SCHOOL
24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.
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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
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