Hirekeyz | Data Engineering Masterclass

Topics Covered

From fundamentals to production-grade systems.

Overview of Modern Data Engineering on Azure
Lakehouse vs. Data Warehouse vs. Data Lake
End-to-End Data Pipeline Architecture (ADF → Databricks → Synapse)

ADF Fundamentals – Linked Services, Datasets, Pipelines
Copy Activity & Data Flows for ETL/ELT
Orchestrating Complex Workflows with Triggers & Parameters
Integration with On-premise & Cloud Data Sources
Case Study: Building a pipeline to ingest raw sales & customer data from multiple sources (SQL Server, Blob Storage, APIs) into Azure Data Lake

Databricks Basics – Clusters, Notebooks, Delta Lake
Data Cleaning & Transformation with PySpark
Incremental Data Loading & Handling Slowly Changing Dimensions (SCD)
Machine Learning Integration with Databricks MLlib
Cost Optimization in Databricks
Case Study: Transforming ingested sales data into curated, analytics-ready tables with business rules & aggregations

Synapse Overview – Dedicated SQL Pools, Serverless SQL, Pipelines
Data Modeling for Analytics (Star Schema, Snowflake)
Query Performance Optimization (Partitions, Materialized Views)
Security & Access Control in Synapse
Power BI Integration with Synapse
Case Study: Creating a unified sales & customer dashboard with Synapse + Power BI

Scenario: Retail Sales Analytics (or Finance/Healthcare, depending on audience)

Step 1: Ingest raw data (POS transactions, customer details, product catalog, IoT data) with ADF

Step 2: Clean, enrich, and transform data with Databricks

Step 3: Load curated data into Synapse for reporting & dashboards

Step 4: Build a Power BI dashboard for insights (sales trends, customer segmentation, revenue forecasting)

Lessons Learned & Best Practices

Data Lakehouse with Delta Lake + Synapse
Real-Time Data Pipelines (ADF + Event Hub + Databricks Structured Streaming)
DevOps for Data Engineering (CI/CD in ADF, Databricks, Synapse)
Monitoring, Logging & Cost Governance
Deliverables:

Hands-on demos with sample datasets

Architecture diagrams

End-to-end pipeline walkthrough

Benefits from the Masterclass

Curated per your PPT + inspired by the reference page.

Real Data Pipelines

Design batch & streaming pipelines with best practices (partitioning, orchestration, CI/CD).

Career Acceleration

Target roles like Data Engineer, Platform Engineer, or Analytics Engineer.

Portfolio Projects

Ship 3+ guided projects including a Lakehouse & event-driven pipeline.

Certificate

Shareable certificate on completion + interview prep resources.

Career Outcomes

Data Engineer • Platform Engineer • Analytics Engineer • Data Architect
Mock interviews, resume & LinkedIn review
Alumni network & job-posting channel

Talk to an Advisor

92%

Report higher confidence

3+

Portfolio projects

6

Intensive weeks

∞

Community access

About the Masterclass Speaker

Ex-FAANG • 10+ yrs in Data Platforms • Mentored 1k+ professionals

Our speaker brings 16 years of IT expertise across CPG, Retail, Healthcare, and Services. He has delivered several end-to-end data analytics projects and is highly skilled in Azure Cloud Data Engineering, SAP ERP, Data Science, Generative AI, and Databricks.
A Microsoft and Oracle certified professional in Data Engineering, Data Science, SAP, Oracle, and Gen AI, he is passionate about mentoring and has upskilled 200+ students in cloud and data technologies.

Reserve Your Seat

Upcoming Masterclasses

Live, instructor-led sessions — reserve your seat.

Oct 05 • 7:00 PM IST Live Online

Batch ETL with Spark + Delta

Build a medallion lakehouse with real datasets.

Transformations & partitioning
Delta Lake optimization

Oct 12 • 7:00 PM IST Live Online

Streaming Pipelines with Kafka

Event ingestion, processing & alerting in real time.

Producers/consumers
Exactly-once semantics

Oct 19 • 7:00 PM IST Live Online

Orchestrating DAGs with Airflow

Scheduling, sensors, retries & CI/CD for pipelines.

Production-grade DAGs
Observability & alerts

Frequently Asked Questions

Beginners to intermediate folks in software, analytics, QA, or students looking to pivot into data engineering.

No. Python familiarity helps. We start from fundamentals and quickly move to hands-on labs.

Yes, recordings are available to all participants along with resources and code.

First step is to enroll in our data engineering course and get hand on the best learning experience with live classes, projects and etc.

Topics Covered

1. Introduction & Architecture

2. Azure Data Factory (ADF) – Data Ingestion & Orchestration

3. Azure Databricks – Data Transformation & Advanced Analytics

4. Azure Synapse Analytics – Data Warehousing & BI

5. End-to-End Case Study: Retail/Finance/Healthcare Analytics

6. Advanced Topics