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#ETL Developer

ETL Developer is responsible for extracting data from various sources by designing, building and maintaining data pipelines. ETL Developer role is in high demand and offers excellent pay scale in the tech industry. They often use Azure and AWS cloud platforms to handle their operations.

Training Schedules

S NoTime (IST, Mon - Fri)Start Date
16 AM - 7 AMSep 2nd
210 AM - 11 AMAug 26th
36 PM - 7 PMSep 8th
48 PM -9 PMAug 19th
ETL Developer Training Highlights

ETL Developer 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: Python for ETL

Ch 1. Python Introduction

  • Need for Data Analytics
  • Python in Data Analysis
  • History of Python
  • Python Versions
  • Python Implementations
  • Python Installations
  • Python IDE & Usage
  • Jupyter Notebooks

Ch 2. Python Basics, Architecture

  • Python Scripting Options
  • Basic Operations in Python
  • Python Scripts, Print()
  • Single, Multiline Statements
  • Adding Cells, Saving Notebook
  • Single, Multi Line Comments
  • Python : Internal Architecture
  • Compiler Versus Interpreter

Ch 3. Data Types & Variables

  • Integer / Int Data Types
  • Float & String Data Types
  • Boolean, Binary Types
  • Sequence Types: List, Tuple
  • Range, Complex & memview
  • Retrieving Data Type: type()
  • Multi Assignments & Casting
  • Unpack Collection, Outputs

Ch 4. Python Operators

  • Arithmetic, Assignment Ops
  • Comparison Operators
  • Logical, Identity Operators
  • Member, Bitwise Operators
  • Operator Precedence
  • If … Else Statement, Pass
  • Short Hand If, OR, AND
  • ELIF and ELSE IF Statements
  • Expressions, Ternary OPs

Ch 5: Python Loops, Iterations

  • Python Loop & Realtime Use
  • Python While Loop Statement
  • Break and Continue Statement
  • Using Print with While()
  • Iterations & Conditions
  • Exit Conditions & For Loops
  • Break, Continue & Range
  • __iter__() and __next__()
  • iter() and Looping Options

Ch 6: Python Collections

  • Python Collections (Arrays)
  • list() Constructor, print()
  • Python Tuples, Tuple Items
  • tuple() Constructor, Usage
  • Python Sets : Syntax Rules
  • Duplicates, Types, Ordered
  • Python Dictionaries: Usage
  • Changeable, Ordered Data
  • Dictionary Construct, type()

Ch 7: Python Functions

  • Python Functions & Usage
  • Function Parameters
  • Arguments, **kwargs
  • Default & List Parameters
  • Python Lambda Functions
  • Anonymous Functions
  • Recursive Functions, Usage
  • Return & Print @ Lamdba

Ch 8: Python Classes & Arrays

  • Python Classes & Objects
  • __init__() Function
  • __str__() Function
  • Self Parameters & Objects
  • Python Inheritance & Classes
  • Parent & Child Classes
  • __init__() & super() Function
  • Polymorphism in Python

Ch 9: Python Modules

  • import Python Modules
  • Variables in Modules
  • Built In Modules & dir
  • datetime module in Python
  • Date Objections Creation
  • strftime Method & Usage
  • imports & datetime.now()
  • Using Python Constructors

Ch 10: : Python JSON & RegEx

  • JSON Concepts, Usage
  • Dictionary & import json
  • Python Objects into JSON
  • Formatting & Ordering
  • json.dumps, print options
  • Python Regular Expressions
  • RegEx Module & Function
  •  search() & span() , Strings
  • Using RegEx with JSON

Ch 11: Python User Inputs & TRY

  • Try Except, Exception Handling
  • NameError Resolution
  • Python Finally Block, Usage
  • Raise an exception method
  • TypeError, Scripting in Python
  • Python User Inputs
  • Python Index Numbers
  • Named Indexes, Usage
  • input() & raw_input()

Ch 12: Python File Handling

  • File Handling, Activities
  • r, a, w, x modes
  • t, b Operations
  • Read Only Parts
  • Loop, Write, Close Files
  • Appending, Overwriting
  • import os, path.exists
  • f.open, f.write
  • f.read, f.close

Ch 13: Data Analytics – Pandas

  • Python Modules & Pandas
  • Pandas Codebase & Usage
  • Installation of Pandas
  • import pandas.DataFrame
  • Checking Pandas Version
  • Pandas Series, arrays
  • Labels : Creation, Use
  • series(), print()

Ch 14: Data Analytics – DataFrames

  • Indexes & Named Options
  • Locate Row and Load Rows
  • Row Index & Index Lists
  • Load Files Into a DataFrame
  • pd.read_csv() Function
  • pd.options.display.max_rows
  • df.to_string() Function
  • tail() & null() Function

Ch 15: Data Analytics – Pandas

  • Pandas – Cleaning Data
  • Replace, Transform Columns
  • Data Discovery & Column Fill
  • Identify & Remove Duplicates
  • dropna(), fillna() Functions
  • Pandas – Data Correlations
  • Good & Bad Correlation
  • Data Plotting & matlib Lib

Ch 16: : SQL DB & Python – 1

  • SQL & Databases
  • Azure Data Studio Tool
  • sp_execute_external_script
  • Input Data & Result Sets
  • DDL & DML with Python
  • SQL_out, SQL_in
  • Variables & Parameters
  • Versions, Package List
  • WITH RESULT SETS Options

Ch 17: SQL & Python – 2

  • pandas.Series with SQL DBs
  • Indexing Methods in Realtime
  • Convert series to data frame
  • Output values into data.frame
  • pymssql package in SQL Server
  • pip list & Package Manager
  • Python runtime, Py Package
  • pymssql.connect & Usage
  • Cursor Variables & Usage

Ch 18: Realtime Case Study

SQL SCHOOL

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

ETL Developer Training FAQ's

What is ETL Developer Job Role?

An ETL Developer (Extract, Transform, Load Developer) is responsible for designing and managing data pipelines that move data from multiple sources into a centralized system, typically a data warehouse, for reporting and analysis.

🔹 Key Responsibilities:

  1. Data Extraction – Pull data from various sources (databases, APIs, flat files).

  2. Data Transformation – Clean, format, and convert data to match business requirements.

  3. Data Loading – Load processed data into target systems like data warehouses or cloud storage.

  4. Workflow Design – Build automated ETL workflows using tools like Informatica, Talend, ADF, SSIS, etc.

  5. Performance Optimization – Tune data pipelines for faster load times and reduced failures.

  6. Data Quality Checks – Implement validation rules and error handling.

  7. Collaboration – Work closely with data analysts, engineers, and business teams.

  8. Documentation – Maintain clear documentation of ETL processes and data flows.

  9. Scheduling Jobs – Set up and manage job schedulers like Airflow or Control-M.

  10. Monitoring & Troubleshooting – Continuously monitor ETL jobs and resolve issues promptly and more..!

What are the Job Roles of an ETL Developer?

💼 Top Job Roles:

  • ✅ Data Pipeline Developer
  • ✅ Data Integration Specialist
  • ✅ Data Quality Analyst
  • ✅ ETL Workflow Designer
  • ✅ Database Developer
  • ✅ Performance Tuner
  • ✅ Error Handling Expert
  • ✅ Business Data Analyst Support
  • ✅ ETL Tool Expert
  • ✅ Cloud Data Engineer and more..!

What does our ETL Developer Training course contains?

The course is carefully curated with below module:
👉🏻Module 1: Microsoft SQL (TSQL)
👉🏻Module 2: Azure Data Engineer
👉🏻Module 3: ETL Python

Who can join this course?

  • Freshers looking to start a career in data or analytics

  • Working professionals wanting to shift to Python, Data Science, or ETL roles

  • Students from any background interested in tech and data

  • IT and Non-IT professionals aiming to upskill

  • Anyone with basic computer knowledge and a passion for learning

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1:        LIVE Online Training  (100% Interactive, step by step, assignments)

Option 2:        Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for ETL Developer training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice.  Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

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