ChatGPT Image Jun 11, 2026, 03_11_21 PM

#ETL Admin

Cloud ETL Admin job roles are very prominent, high demand unique profile that involves End to End Database Management From OLTP to DWH Databases. This ETL Admin jobs are very much mandatory for Big Data, Data Science and AI Projects ! Practically speaking, we need DATA and every DATA Storage platform needs ADMIN.

Training Highlights

✅ ETL Architecture & Data Flows
✅ SSIS, Informatica, Talend, ADF
✅ Scheduling & Job Monitoring
✅ Error Handling & Logging
✅ Performance Optimization
✅ Validation & Quality Checks
✅ Incremental Loads & CDC
✅ Cloud ETL Integrations
✅ Real Time Project

Modules We Learn:

✅ Module 1: Database Concepts, SQL Server Basics
✅ Module 2: Azure ETL & DWH
✅ Module 3: Core SQL DBA
✅ Module 4: Azure SQL DBA
✅ Module 5: Real-Time Projects

Course Duration 4 Months

ETL Admin
Course Contents:

Brochure Download Request a Callback

Module 1: Database Concepts, SQL Server Basics

Ch 1: SQL Database Job Roles

Introduction to Databases
DBA Job Roles
Routine DBA Activities
Emergency DBA Activities
SQL DBA Job Scope, Job Growth

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server Connections, Authentications
Installation Issues, Solutions

Ch 3: SQL Basics

SQL Basics (DDL, DML, .)
Creating Databases
Creating Tables, Columns
Data Inserts (SQL)
SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop, Add, modify, .
DML: Insert, Update, Delete, select into, .
DQL: Fetch, .. Select, etc..
SQL Operations: LIKE, BETWEEN, IN, .
Special Operators

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

Data Imports with Excel
SQL Native Client
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 7: Schemas & Batches

Schemas: Creation, Usage
Schemas & Table Grouping
Real-world Banking Database
2 Part, 3 Part & 4 Part Naming
Batch Concept & “Go” Command

Ch 8: Constraints, Keys & RDBMS

Null, Not Null Constraints
Unique, Primary Key Constraints
Foreign Key & References
Default, Check Constraints
DB Diagrams & ER Models
Normal Forms

Ch 9: Joins & Audits

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 10: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 11: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations
Stored Procedures, Tuning

Ch 12: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 13: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 14: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 15: Linked Servers

QL Server Instances
Linked Server : Creation
Linked Server : Testing
Scripting Linked Servers
Realtime Usage @ DBA
Remote DB Access
Remote Joins

Ch 16: Server Architecture

Database Engine Components
Parser, Compiler & Optimizer
Protocols and Query Processing
MDAC and CLR Components
Parsing and Compilation
Memory Manager & IO Managers
SQL OS Components, MDAC

Ch 17: DB Architecture (VLDB)

Planning Large Databases
Primary, Secondary Data Files
Filegroups, Spacing and Sizing
Log File: Usage and Precautions
Creating Tables with Filegroups
Pages and Extents for Storage
VLF, MiniLSN & Checkpoint

Module 2: Azure ETL, DWH

Part 1: Azure Data Factory & Synapse
Ch 1: Azure Fundamentals

Cloud Introduction, Azure
Cloud Implementations
Azure Account, Subscription
Azure ETL & DWH Resources
Azure Storage, IoT Resources

Ch 2: Azure Storage & ADLS

Azure Storage Account
Azure Data Lake Storage
Azure BLOB Containers
Blob File Uploads
Azure Tables

Ch 3: Azure Deployments, Azure SQL

Azure SQL Server, SQL DB
Azure SQL Database (OLTP)
Azure Firewall Rules
Connections from SSMS Tool

Ch 4: Azure SQL DB Migrations

On-Premise SQL DB, bacpac
Azure SQL Deployment
Azure Storage from SSMS
Azure SQL DB Migration
Migration Verifications

Ch 5: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS (Data Movement Service)
Azure DWH Tables: Partitions
Distributions & MAXDOP

Ch 6: Azure Data Factory (ADF)

Need for ADF & Pipelines
Data Orchestration with IR
Integration Runtime Engine
Linked Services, Datasets
Pipelines: Copy Data Activity
Data Flow Activity with IR

Ch 7: Azure SQL DB Loads

ADF: Author, Azure SQL DB Reads
Azure SQL Pool Writes
Synapse Analytics with IR
Pipeline Design, Validation
Pipeline Runs, Monitoring

Ch 8: File Incremental Loads

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
Initial, Incremental Loads
Schema & Data Changes

Ch 9: Pipeline Settings

ADF Pipeline Settings
Staging: Advantages
Reliable Logging
Best Effort Logging
DIU & DOCP with IR
Compressions, Health Check

Ch 10: Table Incremental Loads

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 11: ADF Data Flow – 1

Creating Data Flow Items
Using Multiple Sinks
Conditional Split Transformation
Select, Sort, Union, Loops
ADF Debug Options

Ch 12: ADF Data Flow – 2

Working with Multiple Tables
Join Transform, Broadcast
Surrogate Keys, Derived Cols
ETL Loads Dates, Sink Options
Aggregated Data Loads

Ch 13: ADF Data Flow – 3

Pivot Transformation
Group By & Pivot Keys
Column Pattern, Deduplicate
Lookup, Cached Lookup
Tuning Transformations
Tuning Data Flow, Spark

Ch 14: ADF Data Flow – 4

Get Metadata
IF & Lookup Transformation
Cache Lookup
Data Validations
Lookup Versus Joins

Ch 15: ADF Metrics, Alerts

Azure Insights
Azure Metrics for ADF
Azure Metrics for Synapse
CPU, Memory Metrics
Alerts and Notifications
Action Groups, Tuning Options

Ch 16: ADF with Azure Functions

Azure Functions
Function Activity in ADF
Linked Services
Pipeline Debug
ADF Activity Controls

Ch 17: ADF Optimizations

Synapse SQL Pool Partitions
ADF Partitions
Broadcast Options
Staging, Logging
DIU, DOCP
Spar Cluster Optimizations

Ch 18: ADF Parameters, Security

Linked Service Parameters
Creating Logins & Users
Schemas, ETL Permissions
Logins Parameters in ADF
Dynamic Linked Services

Ch 19: SCD & ETL with Control Tables

ADF Templates in Realtime
Incremental Loads (SCD)
Control Tables, Watermarks
Lookup Activity, Delta Queries
SP Activity & Parameters
Pipeline Parameters, SPs

Ch 20: Azure Key Vaults

Azure Key Vaults
Access Policies
Secret Management
Managed Identity
Key Vault Integrations

Ch 21: Synapse Analytics

Azure Synapse Analytics
Synapse Deployments
Synapse Configurations
ADLS Containers
Workspace Server Setup
Synapse Studio (GUI)

Ch 22: Synapse: Dedicated SQL Pools

Creating Dedicated SQL Pools
BLOB Data Imports
TSQL Queries, Data Imports
Big Data Analytics

Ch 23: Synapse: Serverless Pools

Serverless Pools, TSQL
Serverless Architecture
OPENROWSET Operations
Big Data Analytics

Ch 24: Synapse: Apache Spark Pools

Apache Spark Pools
Nodes and Executors
Big Data Analytics
Pipeline Integrations

Part 2: Databricks (Spark, PySpark, Big Data, Genie AI)
Ch 1: Databricks Introduction

Cloud ETL, DWH
Cloud Computing
Databricks Concepts
Databricks Account
Big Data in Cloud

Ch 2: Databricks Architecture

Unity Catalog, Volume
Spark Clusters
Apache Spark and Databricks
Apache Spark Ecosystem
Hadoop, MapReduce, Apache Spark

Ch 3: Unity Catalog

Unity Catalog Concepts
Databricks Workspace UI
Organizing Workspace Objects
Volumes, File Uploads
Managed & External Tables
Spark UI: Limitations

Ch 4: Spark SQL: Basics

Spark SQL Notebooks
Creating Catalog
Creating Schemas
Spark Data Types
PySpark API: SQL Queries
Notebooks: Exports, Clone

Ch 5: Spark SQL: Functions

Math, Sort Functions
String, DateTime Functions
SQL Expressions with expr()
Volume for our Data Assets
File Formats, Schema Inference
Spark SQL Aggregations

Ch 6: Spark SQL: Time Travel

Time Travel Concepts
Spark DB: Logical Architecture
Spark DB: Physical Store
Time Travel, History
DESCRIBE, EXTENDED
Version Numbers

Ch 7: Python Introduction, Print

Python Introduction
Python Versions
print() & display()
Single, Multiline Statements

Ch 8: Python Variables

Python Variables
Declarations, Values
Multi Variable Values
Common Variable Values
Realtime use of Variables

Ch 9: Python Operators

Need for Operators
Arithmetic Operators
Assignment Operators
Comparison Operators
Operator Precedence
Operands in Python

Ch 10: Python Control Statements

Python Control Structures
If … Else Statement
Short Hand If
ELIF & ELSE IF Statements
OR, AND Concepts
Python Loops

Ch 11: Python Data Types

Python Data Types
Integer / Int Data Types
Float, String Data Types
List Data Type
Dictionary Data Type
Tuple Data Type

Ch 12: Python Modules & Dataframes

Pandas
NumPy
Dataframe Concepts
Handling Nulls
Data Cleansing Concepts
Pandas Series, arrays
Indexes, Indexed Lists

Ch 13: PySpark Concepts

Constructing Dataframes
List Dataframes
Pandas Dataframes
Contact & Union
Merge
Join Options with Dataframes

Ch 14: Medallion Architecture – 1

Medallion Architecture
Aggregated Data Loads
Broze, Silver and Gold
Temp Views
Spark Tables (Parquet)
Work with File Sources

Ch 15: Medallion Architecture – 2

Medallion Architecture
Azure SQL DB Connections
Joining Source Tables
Dataframes, Temp Views
Aggregated Data Loads
Gold Data Consumption

Ch 16: Delta Lake

Databricks DeltaLake
Schema Evolution
Dataframes, Temp Views
Delta Table API
Update, Delete Records
Merging Records
Old History Retention
Delta Transaction Log

Ch 17: PySpark: Widgets

PySpark Parameters
Text Widgets
User Parameters
Manual Executions
Automations
UI & JSON For Widgets

Ch 18: Lake Flow Jobs

Worksflows & CRON
Job Compute, Running Tasks
Parameters into Notebook Tasks
Parameters into Python Script Tasks
Concurrent Executions, Dependencies
Branching Control with the If-Else Task

Ch 19: Pyspark: Auto Loader – 1

AutoLoader Concept
Cloudfiles Architecture
Checkpoint Configurations
Creating Directories
Reading Databricks Cloud Sources
Initial Loads

Ch 20: PySpark: Auto Loader – 2

Reading Streams with Auto Loader
Reading a Data Stream
Manually Cancel your Data Streams
Writing to a Data Stream
Schema Evaluation Modes
Adding New Columns
Workspace Modules

Ch 21: Lake Flow Declarative Pipelines

SDP: Spark Declarative Pipelines
Delta LIVE Tables
Streaming Data Loads
Materialized Views
Pipeline Clusters
Databricks CLI
Data Quality Checks

Ch 22: Databricks Optimizations

Lazy Evaluation
Data Shuffling
Broadcast Joins
Data Skipping
Z Ordering
Liquid Clustering
VACUUM
OPTIMIZE

Ch 23: Databricks Security, AI

Overview of ACLs
Adding a New User to Workspace
Workspace Access Control
Cluster Access Control
Groups & LakeBridge
Access Keys (Tokens), Genie AI

Ch 24: Azure Databricks

Databricks Deployment Modes
Classic Deployments
Azure Databricks Workspace
Databricks Compute
Scaling & Tuning & AI
Open Source Databricks Vs Azure Databricks

Ch 25: GitHub Concepts

Creating Github Account
GIT Project Concept
GIT Project Creation
GIT: Main, Branches
GIT Credentials
Connecting with ADF
Connecting with Databricks

Part 3: Data Engineer Project (For Resume)
ECommerce Platform
Project Objective:

Build an end-to-end Azure Data Engineering solution to process, transform, and analyze ecommerce business data from multiple sources.

Skills Gained:

Data Ingestion & ETL Development
Azure Data Factory Pipelines
Data Orchestration (End to End)
Data Lake Architecture, RBAC
Real-Time Industry Experience
Azure IoT, Stream Analytics
Azure Databricks with Data Factory

Components For Project (From Resume Perspective):

ADF Pipelines
Databricks Notebooks
Synapse Analytics
Apache Spark Schemas
Power BI Reporting
Monitoring & Alerts
CI/CD Deployment
End to End Integrations
IAM & Managed Identity

Module 3: Core SQL DBA

Ch 1: Backups

Database Backups & Types
DB, Filegroup, File Backups
Copy Only Backups, Usage
Partial Backups, Split Backups
Mirror Backups
Backup Verifications
Checksum, ContinueOnError

Ch 2: Restores

DB, Filegroups, File Restores
GUI Restores: Limitations
Restoring & Online States
FILELISTONLY
MOVE Options with Restores
Standby Mode Restores
Point-In-Time (PITR) Restores

Ch 3: Jobs, Maintenance

SQL Server Agent Service
Job Steps & Schedules
MSDB: Job History
DB Maintenance Plans
Backup Maintenance
Job Verifications, Scripts
Common Errors, Solutions

Ch 4: DB Mail & Alerts

SMTP Concepts
Creating Email Profile
Default, Public Profiles
Creating SMTP Accounts
Alert System : Agent Settings
Creating Operators
Job Failures & Notifications j

Ch 5: Security Management

Security Objects, Logins
Users, Roles & Principals
Schema Level Security
Object, Column Level Security
Security Audit, Logon Failures
Keys & Certificates
Encryptions & Data Security

Ch 6: DB Migrations

Creating Credentials
Creating SSIS Proxies
Creating CDW Packages
Database Detach – Attach
SMO Migration Method
DB Migration Schedules
Detect, Fix Orphan Users

Ch 7: Tuning: Audits, AM Tool

Activity Monitor Tool
Perfmon Tool & Counters
Query Audits: DMVs, DMFs
Plan Handle, Execution Time
Long Running Queries
Query Store & Buffer Cache
Data Flush, Stats Collection

Ch 8: Tuning : Indexes

Indexes : Sort Locations
Clustered & Online Indexes
Non Clustered, Column Store
Included Indexes in Realtime
Filtered Indexes & Usage
Covering Index & Selectivity
Indexed Views (Materialized)

Ch 9: Tuning: Partitions

Partitions: Performance Tuning
Partition Functions & Schemes
Partition Un-partitioned Tables
Compressions: ROW, PAGE
Auditing Partitions
Partitions Limitations in OLTP
Partitions with DWH

Ch 10: Statistics & Tuning

Statistics: Realtime Usage
Index & Column Statistics
Statistics & Key Purpose
Verifying, Using Stats
Statistics Versus Indexes
Stats Updates on Tables
Stats Updates on Views

Ch 11: Index Management

Index Management Options
Index Rebuilds, Re-Organize
Database Maintenance Plans
Page Count and Index Conditions
Degree Of Parallelism Settings
Resumable & Online Indexes
PAUSE, RESUME in Rebuilds

Ch 12: Tuning Tools

Tuning Tools: Workload Files
Profiler Tuning, Events
DTA, Profiler Options
Physical Design Structures
PDS Recommendations
Query Execution Cache
Tuning Tools: Precautions

Ch 13: Execution Plans

Execution Plan Analysis
IO Cost and CPU Cost
SubTree & Operator Cost
NUMA Nodes, Processor Affinity
Thread Count, DOP
Table & Index Scan, Index Seek
Index Selectivity & Tuning

Ch 14: Lock Management

Open Transactions in Realtime
Open Transaction, Blocking
LOCKS: Types & Audits
S, X, IX, U and MD Locks
Sch-M and Sch-S Locks
SP_WHO2, SP_LOCK
sysprocesses & Lock Waits

Ch 15: Isolation Levels

Lock Hints and Isolation Levels
Read Committed, Uncommitted
Serializable, Repeatable Read
Snapshot Isolation, Versioning
Read Committed Snapshot
sysprocesses & sp_who2
Choosing Correct Isolation Level

Ch 16: Deadlocks, LIVE Locks

Deadlocks in Real-world
Profiler Tool & Deadlocks
Lock Management: Deadlocks
Deadlock Graphs & Events
Deadlock Avoidance, Prevention
Deadlock Prevention
sysprocesses & sp_who2

Ch 17: Recovery Models

Recovery Models
Database Properties
Full Recovery Model
Simple & Bulk Logged
Realtime Uses
Backups Versus Recovery Models

Ch 18: HA DR @ Replication – 1

Replication : Realtime Usage
Distributor Configurations
Publisher and Subscriber
Replication Types: Snapshot
Transactional Replication
Log Reader Agent & Usage
Replication Monitor, Alerts

Ch 19: HA DR @ Replication – 2

Merge Replication & Usage
Peer – Peer Replication
Push & Pull Subscriptions
Conflict Detection, Avoidance
Scripting Replication Agents
Limitations with Replication
Common Errors, Solutions

Ch 20: HA DR @ Log Shipping

Log Shipping Configurations
Primary, Secondary Servers
Working with Network Shares
Jobs: Backup, Copy, Restore
NORECOVERY, STANDBY
Manual Failover Process
Common Errors, Solutions

Ch 21: HA DR @ DB Mirroring

Database Mirroring Concepts
Configuring Principal, Mirror
Configure Witness, EndPoint
Synchronous, Asynchronous
Automated Failover Process
Manual Failover, Monitoring
Common Errors, Solutions

Ch 22: DB Health Checks, Repairs

DBCC Commands in Realtime
DBCC for Audits, Repairs
MSDB Suspect Pages
User Database Repairs
Single User Mode
Emergency Mode
Recovery & Restoring Modes

Ch 23: Issues & Solutions

Log Space Issues, Solutions
TempDB Issues, Solutions
Memory Issues, Solutions
Health Check Strategies
Healthy Backups
Maintenance Plans
Database States

Ch 24: Updates (Patches)

Planning for Updates
Pre-Maintenance Checklist
Edition Checks
Updates Process
Edition Comparisons
History Tracking
Updates Rollback

Ch 25: Upgrades, Licensing

Planning for Upgrades
Pre-Maintenance Checklist
Upgrades Process (In-Place)
Cautions & Maintenance
Version Comparisons
History Tracking
Upgrades Rollbacks

Ch 26: System DB Rebuilds, DAC

Command Line Installations
System DB Rebuilds
Server Down Issues
Service Startup Issues
DAC: Dedicated Admin Console
Login Failure Issues
SQL CMD : Secondary Logins

Ch 27: SQL DBA Project

Routine DBA Activities
Emergency DBA Activities
Maintenance DBA Activities
SLA – OLA Process
Ticketing Tools
3rd Party Tools
Common Errors & Solutions

Ch 28: SQL DBA Project

Login Failure Errors
 Slow Database Issues
 Network Errors
 Security Strategies
 HA DR Strategies
 Common Error Codes
 Need for Azure Cloud

Module 4: Azure SQL DBA

Ch 1: Cloud Basics, Azure Funda

Cloud Fundamentals
Cloud Concepts, Benefits
IaaS, PaaS, SaaS Cloud Types
Azure Cloud Concepts
Azure Resources & Usage
Azure Services & Purpose
Azure Account & Subscription

Ch 2: Azure SQL Deployments

Azure SQL Services
Azure SQL Server Creation
Azure SQL Databases
Azure Firewall : Rules
Test Connections from SSMS
SSMS Tool : Test Connections
ADS Tool : Installation, use

Ch 3: Azure SQL DB Licensing

Azure SQL DB Licensing
Per Database Licensing
DTUs: Basic, Standard Types
VPU and Plan Types
DTU Versus VPU Licensing
Elastic DTUs (eDTU) Usage
Elastic Query Processing

Ch 4: Azure SQL DB Migrations

SQL DB Migration Options
Data Migration Assistant: DMA
DMA Tool, Migration Options
On-Premises DB Export
Azure SQL Database Import
Azure Storage Account
Linking SSMS with Azure

Ch 5: Azure SQL DB Metrics

Azure SQL DB Metrics
CPU, Memory, Log Metrics
Data File Metrics, Alerts
Action Groups & Emails
Query Performance Insight
Automated Tuning Options
Query Recommendations

Ch 6: Azure SQL DB Tuning, AI

Server Level Tuning
Database Level Tuning
Built-In Intelligence
AI Search Service, Tuning
AI Indexes, Practical Use
Watermark Columns

Ch 7: Azure Backups, Restores

Azure SQL DB Backups
Backup Retention Options
LTR Backups, Log Backups
Automated Backups
LTR, PITR Restores
Backup Versus Export
Restore Versus Import

Ch 8: HA DR @ Replication

Azure HA DR Mechanisms
Geo Replication
Primary & Secondary
Manual Failover
Automated Failover
DMVs For Replication
Testing Read Only Databases

Ch 9: HA DR @ Server Failover

Multi Database Replication
Configure Server HADR
Adding Replica Databases
Replica DB: Capacity
Replica DB: Status
Automated Failover
Manual Failover

Ch 10: Azure Security

Logins & Users in Azure
Grant, With Grant
Dynamic RLS
Dynamic Data Masking
Creating AD Users & IAM
Implementing RBAC
MFA Authentication

Ch 11: Azure Virtual Machine

Azure IaaS Implementation
Configure Disk, Network
Configure SQL Security
CPU, Processor & IO
IP & RDP Connections
Testing SSMS Connections
Migrating SQL 2025 to VM

Ch 12: Azure VM Clusters

Azure Active Directory
Azure AD DS Services
Azure Networking Options
Availability Groups, Usage
Listeners and Realtime Use
FSW: File Share Witness

Ch 13: SQL Clusters, Always-On

Creating SQL Clusters
Testing SQL Clusters
Configure Always-On
Testing, Using Always-On
AOAG Implementation
AOAG Monitoring
Comparing Paas & IaaS

Ch 14: PowerShell

Azure PowerShell
Az and get Commands
Creating Resources
Lists & Validations
Password Resets
Firewall Config
Resource Cleanups

Ch 15: Azure Managed Instance

Azure MI Implementation
Network Entities for MI
Capacity Entities
Cloud Azure MI Access
Azure MI Implementation
Azure MI Migrations

Ch 16: DP 300 Exam Guidance

Resume Guidance (1:1)
Mock Interview, DP 300 Exam Guidance

Module 5: Real-Time Projects

Project 1: Enterprise ETL Administration & Monitoring
Manage end-to-end ETL environments using SQL Server and Azure ETL tools. Work on job
scheduling, monitoring, failure handling, restart strategies, logging, alerts, package deployment,
security, and production support scenarios.

Project 2: Azure Data Warehouse & ETL Performance Optimization
Build and administer a complete ETL pipeline from multiple data sources into an Azure Data
Warehouse. Focus on data loading, incremental loads, performance tuning, indexing,
partitioning, query optimization, troubleshooting, and cost optimization

What is the ETL Admin Training?

This course trains you on complete ETL Administration including SQL, SQL DBA, Azure SQL DBA, Azure ETL, Data Warehouse Admin, ADF, Synapse, Streaming, and Real-Time Project Implementation.

What are the objectives of this ETL Admin course?

Objectives By the end of this course, you’ll be able to:

* List the four key product lines available from Databricks.

* Describe how each product line serves its respective audience.

* Navigate the Databricks Workspace UI to locate key features and functionalities.

* Describe the primary responsibilities and core skills of a data engineer

* Explain the stages of a traditional data engineering architecture, from data ingestion to processing, storage, and consumption

* Define the core components of the Databricks Lakeflow offering and describe how it benefits data engineering practitioners

* Describe the Medallion Architecture for data transformation in Databricks.

* Describe how Lakeflow Declarative Pipelines and Lakeflow Jobs facilitate unified orchestration in Databricks.

Prerequisites
A basic understanding of data engineering principles and topics such as data collection, extraction, ingestion, and transformation.

SQL SCHOOL vs Other Institutes

SQL SCHOOL vs Other Institute Comparistion image

Training Modes

LIVE Online Training

Instructor Led

Self Paced Videos

On-Demand

Corporate Training

With 100% Hands-On

ETL Admin Certificate of Completion – SQL School Hyderabad | MSME Certified Training with Real-Time Projects

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

A man smiling and giving a thumbs up while holding a notebook.

Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance