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Data Engineering Foundations

SQL, Python, ETL, Linux & Data Warehousing

Learn the core technical skills behind modern data engineering — from SQL and Python to ETL, Linux, Git and data warehousing.

FormatRecorded Program
Duration~40 Hours
AccessLifetime Access
PrerequisitesNone Required
Status: Currently not live
Overview

Start Your Data Engineering Journey With the Right Foundations

Data Engineering is built on strong fundamentals. Before working with large-scale processing, cloud platforms and modern data pipelines, you need to understand how data is queried, processed, moved and structured. This program builds that foundation through SQL, Python, ETL, Linux, Git and Data Warehousing — preparing you to progress into more advanced Data Engineering technologies.

Core Technology Scope
SQL Querying & Analytical PatternsPython for Data Processing & ScriptingETL & Data Workflows (CDC, Incremental, Idempotency)Linux Shell, Bash Scripting & GitData Warehousing, Star Schema & SCDs
Key Advantages

Learn the Foundations Behind Data Engineering

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SQL for Data Engineering

Develop the SQL skills needed to query, filter, aggregate, join, and transform complex relational datasets.

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Python for Data Processing

Learn Python specifically in the context of data manipulation, file handling, and API consumption rather than generic syntax.

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Understand Data Workflows

Learn how data moves through extraction, transformation, and loading processes including incremental loads and CDC.

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Learn the Engineering Environment

Build foundational familiarity with Linux navigation, shell commands, Bash scripting, cron automation, and Git version control.

✓

Understand Data Warehousing

Learn how analytical data is structured using fact tables, dimension tables, Star and Snowflake schemas, and SCDs.

Target Audience

Who Is This Program For?

  • Beginners entering Data Engineering from scratch
  • Students and fresh graduates building a career in data
  • Developers transitioning from web or software engineering toward data engineering
  • Working professionals looking to strengthen their SQL, Python, and data warehousing foundations
Prerequisites

Starting Requirements

No prior Data Engineering knowledge required. The program starts from foundational concepts.

Level: Beginner Friendly
Curriculum Breakdown

What You'll Learn

Structured modules designed to build competencies systematically.

•Database fundamentals, table structures, and data types
•SELECT, WHERE filtering, sorting, DISTINCT, and pagination
•Aggregations: COUNT, SUM, AVG, MIN, MAX, GROUP BY, and HAVING
•Joins: INNER, LEFT, RIGHT, FULL, self joins, and cross joins
•Subqueries, Common Table Expressions (CTEs), and recursive CTEs
•Window functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, and running totals
•Real-world SQL problem-solving drills for analytical reporting
Outcome: Write sophisticated SQL queries and apply analytical SQL patterns to complex data problems.
Real-World Applications

Practice As You Learn

Build and work with practical applications directly derived from our curricula.

SQL · PostgreSQL

SQL Analytical Drills

Hands-on SQL problem solving analyzing multi-table retail and financial datasets using window functions and CTEs.

Focus: Joins, aggregations, window functions, and query optimization.
Python · File I/O · JSON

CSV & JSON Ingestion Pipeline

Python-driven data extraction script parsing nested JSON and malformed CSV files with automated schema validation.

Focus: Exception handling, data cleansing, and structured transformation.
Python · Requests · ETL Logic

API Ingestion & Reusable ETL Script

A production-grade Python ETL script that calls external REST APIs, extracts payloads, cleans fields, and writes incremental records.

Focus: API pagination, rate limiting, incremental extraction, and idempotency.
Data Modeling · Star Schema · SCD2

Warehouse Dimensional Model

End-to-end data warehouse design project translating business requirements into Star and Snowflake schemas with SCD Type 2 tracking.

Focus: Fact-dimension modeling, surrogate keys, and historical tracking.
Product Architecture

The First Step in the Data Engineering Journey

This module is Module 01 of the 3-module Data Engineering Master Program.

CURRENT MODULEACTIVE

Module 1: Data Engineering Foundations

SQL · Python · ETL · Linux · Warehousing

MODULE
View Module

Module 2: Big Data & Pipeline Engineering

Spark · Databricks · Airflow · Kafka · Streaming

MODULE
View Module

Module 3: Modern Data Engineering with dbt & Architecture

dbt · AWS · ELT · Data Quality · Observability · Architecture

Want all three modules in one unified 12-week progression with Capstone?Explore Master Program
Package Breakdown

Everything You Need for the Journey

~40 Hours of foundational recorded curriculum
Lifetime access to all lectures and updates
Comprehensive training slides and reference notes
Practical exercises, drills, and problem-solving notebooks
Reusable script templates for Python ETL and Bash automation
TailorTech WhatsApp Community support
Instructor Experience

Learn From Industry Experience

Delivered by an industry professional with 12+ years of experience across enterprise data architectures, ETL engineering, and production databases.

Program Status

Inquire About Data Engineering Foundations

Register your interest or speak with our advisors to learn about upcoming cohorts and updates.

Status
Currently not live
Got Questions?

Frequently Asked Questions

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Big Data & Pipeline Engineering (Module 2)

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Data Engineering Master Program

The complete 12-week flagship journey combining Foundations, Scale, and Modern Data Architecture with Capstone.

View Program →

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