ChatGPT Image Jun 2, 2026, 09_32_48 AM

Trainer Sai Phanindra

Meet Your Trainer – Mr. Sai Phanindra
Mr. Sai Phanindra is the Chief Trainer at SQL School with 20+ years of real-world IT experience in Data Engineering, Business Intelligence, and Database Technologies. He has successfully trained thousands of professionals and helped them build successful careers in leading MNCs.
He specializes in delivering 100% practical, project-based training in:

Microsoft Power BI
Azure Data Engineering
Fabric Data Engineer
Databricks Data Engineer
SQL Server (MSSQL & T-SQL)
SQL Server DBA (Administration)

His training focuses on real-time projects, industry best practices, interview preparation, certification guidance, and job-ready skills to help learners confidently succeed in today’s data-driven industry.

Trainer Profile

Training Highlights

✅ Cloud ETL, DWH with Big Data Analytics
✅ Azure Data Factory (ADF) for ETL
✅ Azure Synapse For DWH, Analytics
✅ Azure Stream Analytics For IoT, Insights
✅ Azure Key Vault, RBAC For Security
✅ Azure Databricks for ETL, ELT, Analytics

Modules We Learn
✅Module 1: SQL Server (MSSQL) & T-SQL
✅Module 2: Azure Data Engineering
✅Module 3: Integrations, DevOps
✅Module 4: End-to-End Industry Project
✅Module 5: Azure Certifications
✅Module 6: Microsoft Fabric

An Azure Data Engineer designs and manages modern data solutions on Microsoft Azure. At SQL School, you’ll master ADF, Azure Synapse, ADLS, Azure Databricks, Apache Spark, PySpark, and Microsoft Fabric through hands-on projects, expert-led training, certification guidance, and interview preparation to become job-ready.(sqlschool.com)

Who Should Join?
✅Freshers
✅SQL Developers
✅ETL Developers
✅BI Developers
✅Database Administrators
✅Data Analysts
✅Software Engineers

Prerequisites
✅No Azure experience required
✅SQL knowledge is helpful but not at all mandatory.
We start from the basics and build your skills step by step.

Azure Data Engineer
Course Contents:

Brochure Download Request a Callback

Module 1: SQL Server (MSSQL), TSQL

Ch 1: SQL Database Job Roles

Database Intro
OLTP, DWH, OLAP
DBMS Basics
Data Engineer Job Roles

Ch 2: Database Intro & Installations

SQL Server Installations
Instance & Collations
SSMS Tool Installation
Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

SQL Basics (DDL, DML, etc..)
Creating Databases, Tables
Data Inserts (GUI, SQL)
Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop
DML: Insert, Update, Delete
DQL: Select, Fetch
Add, Truncate Statements
SQL Operators

Ch 5: Data Types & Variables

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

Ch 6: Data Imports From Files

Data Imports with Excel
Data Imports with CSV
Auto Detection of DataTypes
OLE-DB Connections
Order By, TOP, OFFSET

Ch 7: Sub Queries

Basic Sub Queries
Aggregations
Combining Queries
UNION, UNION ALL

Ch 8: Schemas & Batches

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

Ch 9: Constraints, Keys & RDBMS

Null, Not Null Constraints
Unique Key & Check
Primary Key Constraint
Foreign Keys, Default
DB Diagrams & ER Models

Ch 10: Normal Forms & RDBMS

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
1:1, 1:M, M:1 Cardinality
Cascading Keys
Self Referencing Keys

Ch 11: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Self Joins & Aliases

Ch 12: Views & RLS

Views: Realtime Usage
DML, SELECT with Views
Excel Analytics with Views
Important System Views

Ch 13: Stored Procedures – 1

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Stored Procedures, Tuning

Ch 14: Stored Procedures – 2

Merge Statement (Upsert)
Merge with OLTP & DWH
Matched and Not Matched
Merge Statement inside SPs
SP Recompilations

Ch 15: User Defined Functions – 1

Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Variables & Parameters
Function Executions

Ch 16: User Defined Functions – 2

Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage
Window Functions (Rank)
Row_Number, DenseRank
Partition By & Order By

Ch 17: Triggers & Automations

Need for Triggers in Real-world
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Disabling DMLs Triggers

Ch 18: Group By Queries

Group By, Distinct
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage

Ch 19: Joins with Group By

3 Table, 4 Table Joins
Join Queries & WHERE
Join Queries & Group By
IIF(), CASE Statement
Query Execution Order

Ch 20: Transactions & ACID

Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Query Blocking
READPAST, LOCKHINT

Ch 21: Indexes Basics, Tuning

Clustered Index, Primary Key
Non Clustered Index
Query Optimizer
Tuning Join Queries
Tuning Group By Queries

Ch 22: CTEs & Tuning

Common Table Expression
CTEs for Data Retrieval
CTEs for DML Operations
CTEs for Data Cleansing
Using CTEs with Row Number

Ch 23: Cursors & Temp Tables

Cursors & Fetch
Scroll, Absolute Types
Local Temp Tables
Global Temp Tables
SELECT..INTO Statement

Ch 24: SQL Server Architecture

Network Protocols
Storage Engine
Query Processing Engine
Query Execution Order
SQL OS Components

Ch 25: Capstone Project (HealthCare Management System)

ECommerce Database
Data Validations
Query Writing
Query Tuning
Excel Analytics

Module 2: Azure Data Engineer

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 Deployments, Azure SQL

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

Ch 3: Azure Storage & ADLS

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

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

Databricks Runtime (DBR)
RDD & DAG
Databricks Lakehouse Architecture
Spark Compute Concepts
Workers & Drivers
Databricks Framework
Databricks APIs

Ch 3: Unity Catalog

Unity Catalog Concepts
Metastore
Catalogs & Schemas
Managed & External Tables
Storage Credentials
External Locations
Volumes
GRANT / REVOKE
Data Permissions
Lineage
Data Governance
Databricks Workspace UI
Spark Table Creations

Ch 4: Spark SQL: Basics

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

Ch 5: Spark SQL: Table Types

Delta Tables
Managed Tables
External Tables
Data Partitioning
Union, Views in Spark
External Volumes

Ch 6: Spark SQL: Functions

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

Ch 7: Spark SQL: Time Travel

Time Travel Concepts
Spark DB: Logical Architecture
Spark DB: Physical Store
Data File Store
Log File Store
Time Travel
DESCRIBE, EXTENDED
HISTORY
Version Numbers

Ch 8: Python: Introduction, Print

Python Introduction
Python Versions
Python Implementations
Python in Spark (PySpark)
Python Print()
Single, Multiline Statements

Ch 9: Python: Variables

Python Variables
Variable Declarations
Variable Values
Value Types
Multi Variable Values
Common Variable Values
Realtime use of Variables

Ch 10: Python: Operators

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

Ch 11: Python: Control Statements

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

Ch 12: Python: Data Types

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

Ch 13: Python: Modules & DataFrames

Python Modules
Pandas
NumPy
DataFrame Concepts
Handling Nulls
Data Cleansing Concepts
Python Functions

Ch 14: PySpark DataFrames & Transformations

Creating PySpark DataFrames
Reading Data Sources
select(), filter(), withColumn()
Handling NULL Values
Data Type Casting
Joins
union() / unionByName()
Aggregations
Window Functions
Writing DataFrames
DataFrame vs Spark SQL

Ch 15: Medallion Architecture – 1

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

Ch 16: Medallion Architecture – 2

Medallion Architecture
Azure SQL DB Connections
Joining Source Tables
Data Frames, Temp Views
Aggregated Data Loads
Gold Data Consumption
Raw Sources → Bronze → Silver → Gold → BI/Analytics

Ch 17: Delta Lake

Databricks Delta Lake
Schema Evolution
Azure SQL DB Connections
DataFrames, Temp Views
Delta Table API
Update, Delete Records
MERGE / Upsert Operations
Version History & Data Retention
Delta Transaction Log

Ch 18: PySpark: Widgets

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

Ch 19: Lake Flow Jobs

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

Ch 20: PySpark: Auto Loader – 1

Auto Loader Concept
Cloud Files Architecture
Checkpoint Configurations
Schema Location
Checkpoint Location
Initial Loads

Ch 21: PySpark: Auto Loader – 2

Reading Streams
Manually Cancel your Data Streams
Writing to a Data Stream
Schema Evaluation Modes
Workspace Modules
Incremental File Ingestion
Schema Evolution
Rescued Data Column
Trigger Options
Exactly-once processing concepts

Ch 22: LakeFlow Declarative Pipelines

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

Ch 23: Databricks Optimizations

Lazy Evaluation
Explain Plan
Caching
Data Shuffling
Broadcast Joins
Partitions
Data Skipping
Liquid Clustering
VACUUM
OPTIMIZE, Z Ordering

Ch 24: Databricks Security, AI

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

Ch 25: GitHub Concepts

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

Part 3: Data Engineer Projects (For Resume)
Project 1: 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

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

Project 2: Retail Platform

Retail Sales Analytics Platform using Databricks (Medallion Architecture)

Project Overview

Students will build a production-style data platform using Lakehouse Architecture (Bronze, Silver,
and Gold), implementing modern ETL practices, data quality validation, incremental processing,
orchestration, optimization, and reporting.

Business Scenario
A multinational retail company receives daily data from multiple operational systems. The
company wants to build a centralized analytics platform to:

Consolidate data from different business domains
Improve data quality and consistency
Track sales performance across regions
Analyse customer purchasing behaviour
Monitor inventory levels
Generate business KPIs for decision-makers
Deliver Power BI dashboards with near real-time insights

Technologies Covered

Apache Spark
PySpark
Spark SQL
Delta Lake
Unity Catalog
Databricks Workflows
Git Integration

Part 4: Microsoft Fabric Integrations

Need for Microsoft Fabric
Fabric Workspace
Fabric DWH Items
Fabric ETL Items
Fabric Workspace Creation
Fabric Warehouse Operations
Fabric Data Factory
Fabric ETL Pipelines
Azure to Fabric Data Factory
Azure Synapse to Fabric Migrations

Module 3: Power BI With AI, Co-Pilot

Ch 1: Power BI Intro, Installation

Power BI & Data Analysis
Power BI Eco System
Power BI Design Tools
PBI Hosting Solutions
Power BI Installation

Ch 2: Report Design Concepts

Basic Report Design (PBIX)
Get Data, Canvas (Design)
Data View, Data Models
Data Points, Spotlight
Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

Visual Interactions & Edits
Limitations with Visual Edits
Creating Power BI Templates
CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

Creating Groups: Lists
Creating Groups: Bins
List Items & Group Edits
Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

Slicer Visual in Power BI
Slicer: Format Options
Single Select, Multi Select
Slicer: Select All On / Off
Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

Hierarchies: Creation, Use
Hierarchies: Advantages
Drill Up, Drill Down
Conditional Drill Down
Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

Power BI Filters
Basic, Top & Advanced
Visual Filters, Page Filters
Report Level Filters, Clear Filter
Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

Power BI Bookmarks
Images: Actions, Bookmarks
Buttons: Actions, Bookmarks
Page to Page Navigations
Score Cards, Master Pages

Ch 9: SQL DB Access & Big Data

SQL DB Access, Queries
Storage Modes: Direct Query
Formatting & Date Time
Storage Modes in Power BI
Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

Charts, Bars, Lines, Area
TreeMaps & HeatMaps
Funnel, Card, Multrow Card
PieCharts & Waterfall
Scatter Chart, Play Axis
Infographics, Classifications

Ch 11: Power Query Introduction

Power Query (Mashup)
ETL Transformations in PBI
Table Combine Options
Merge, Union All Options
Missing Values, Duplicate Records
Wrong Data Types, Outliers
Close, Apply & Visualize

Ch 12: Power Query: Table Transformations

Table Duplicate, Header Promotion
Group By Transformation
Aggregate, Pivot Operation
Reverse Rows, Count Rows
Advanced Power Query Mode
Data Cleaning, Null Handling

Ch 13: Power Query: Column Transformations

Any Column Transformations
Data Type Detection, Change
Rename, Replace, Move
Fill Up, Fil Down
Step Edits & Rollbacks

Ch 14: Power Query: Text, Date Transformations

String / Text Transformations
Split, Merge, Extract, Format
Numeric and Date Time
Add Column & Expressions
Column From Examples

Ch 15: Power Query: Parameters, Expressions

Parameters in Power Query
Static Parameters, Defaults
Dynamic Dropdowns, Lists
Linking with Table Queries
Step Edits, Type Conversions
API & Web Data Sources

Ch 16: Power BI Cloud & Fabric

Power BI Cloud, Microsoft Fabric
Microsoft Fabric Concepts
Fabric One Lake (DWH, LH, etc.)
Microsoft Fabric Workspace
Power BI Desktop Connections
Report Uploads (PBIX)
Report Edits, Semantic Models

Ch 17: Power BI Cloud Dashboards

Power BI Dashboards
Dashboard Creation, Usage
Pin Visuals, Pin LIVE Pages
Add Image, Video Tiles
Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

Report Shares, Alerts
Subscriptions, Exploration
Downloads & Edits
Report Cloning in Cloud
QR Codes, Web Publish
Lineage & Metrics

Ch 19: Power BI Cloud Gateways

Data Gateways, Data Refresh
Install, Configure Gateways
Data Refresh & Scheduling
Gateway Optimizations
Incremental Refresh
Large Dataset Optimization

Ch 20: Power BI Cloud Apps

Power BI Apps: Creation
App Sections & Content
Audience & App Security
App Updates, Favorites
App URL, End User Access

Ch 21: Power BI Report Server

SQL Server 2025
Power BI Report Server
RS Config Tool Options
Report Database, TempDB
Web Service & Server URL

Ch 22: Paginated Reports

Report Builder Tool
Paginated Report (RDL)
Report Expressions (RDL)
Tablix, Chart Wizards
Fields & Drill-Down
RDL Report Publish

Ch 23: DAX Concepts (Basics)

DAX Concepts: Intro & Realtime Need
DAX Columns: Creation, Use
DAX Measures: Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions

Ch 24: DAX Quick Measures

Quick Measures in Power BI
Running Totals
Star Rating Calculations
DAX Measures in Data View
DAX in Cloud Reports

Ch 25: Data Modelling, DAX

Dimensions Tables
Fact Tables & DAX Measures
Data Models & DDAX Joins
Star & Snowflake Schemas
Many-to-Many Relationships
Calculation Groups

Ch 26: DAX Joins, Variables

CALCULATEX & Variables
COUNT, COUNTA, etc..
SUM, SUMX, etc..
SELECTED MEMEBER
Filter Context, RETURN

Ch 27: DAX Models & Calculations

VAR, SWITCH, SUMMARIZE
TREATAS, USERELATIONSHIP
CROSSFILTER, GENERATE
RANKX, TOPN, WINDOW
OFFSET, INDEX

Ch 28: DAX Time Intelligence

Need for Time Intelligence
Date Table Generation
Time Intelligence with DAX
PARALLELPERIOD, DATE
CALENDAR, Total Functions
YTD, QTD, MTD with DAX

Ch 29: DAX – Row Level Security

RLS: Row Level Security
Data Modelling & Roles
Add Cloud Users & KPIs
CoPilot with DAX

Ch 30: Analytical Reports

Analytical Report Concepts
Excel with Power BI Cloud
SQL, AVRO, JSON Sources
Analyze in Excel
Excel Pivot Reports

Ch 31: PL 300 Exam Guidance

PL 300 Exam Guidance
Exam Samples
Exam Scenarios

Realtime Project 3 (Health Care Platform) – For Your Resume
Project Objective

Design and implement a modern Healthcare Data Platform using Power BI Analytics and Reporting services
to process patient, hospital, clinical, and operational data for reporting, analytics, and decision-making.
Technologies Used

SQL Server
Azure SQL Database
Azure Data Lake
Azure Synapse
Databricks API
Power BI Service
Power Pivot
CoPilot

What is the Azure Data Engineer course and who should join this program?

This course is designed for Data Engineers, Developers, Analysts, Architects, and anyone who wants to build end-to-end data pipelines using Azure services like ADF, Databricks, Data Lake, Synapse, and Power BI. It covers complete ETL, ELT, DWH, Big Data, and Analytics workflows.