01
Cloud & pipelines
Scalable ETL/ELT pipelines and Azure-based solutions that move data from source to destination.
JAGADEESH C / AZURE DATA ENGINEER
Data, with
direction.*
I turn complex data into dependable
foundations for better decisions.
5 years in data engineering. A focus on Azure.
From the first pipeline to the final insight.
BUILT ON CURIOSITY. DRIVEN BY DATA.
THE BIG PICTUREFIG. 01
Raw dataSQL · APIs · Files
AZURE DATA PLATFORM
Clear insightsAnalytics-ready data
Complexity, made useful.Ingest → Transform → Understand
MY EVERYDAY TOOLKITAzureDatabricksPySparkData FactorySQL + PythonPower BI
01 / WHAT I WORK WITH
The whole data journey.
Connecting the dots between cloud platforms,
clean data, and meaningful analytics.
02
Transform & process
Big data processing and transformations that turn raw datasets into structured, usable information.
03
Insights & operations
Reporting, version control, and operational visibility across the data development workflow.
02 / ARCHITECTURE LAB
Follow the flow.
A closer look at a modern Azure data platform.
Select a stage to explore the thinking behind it.
STAGE 01 / INGEST
Start with a reliable arrival.
In this example, Azure Data Factory orchestrates ingestion from a relational database into a raw landing area. A watermark limits each run to new or changed records.
Incremental ingestionClear run boundaries
incremental_extract.sqlSQL
-- Example: bounded incremental extraction
SELECT order_id, customer_id,
order_date, amount, updated_at
FROM dbo.orders
WHERE updated_at > @last_watermark
AND updated_at <= @current_watermark;STAGE 02 / STORE
Give every dataset a place.
A data lake separates raw, refined, and business-ready data. Bronze preserves source history, Silver holds validated records, and Gold serves curated analytics.
Traceable data layersReusable datasets
lake_layout.txtDESIGN
# Illustrative data lake layout
bronze/ orders/ingestion_date=2026-10-04/
silver/ orders/
gold/ sales_daily/
raw source → validated records → analyticsSTAGE 03 / TRANSFORM
Make the data trustworthy.
PySpark transformations standardize types and validate key fields. In this example, records with missing order IDs or invalid amounts are routed for review.
Explicit validationInvalid records retained
validate_orders.pyPYSPARK
from pyspark.sql import functions as F
typed = raw.withColumn(
"amount", F.expr("try_cast(amount as decimal(18,2))")
)
valid = F.col("order_id").isNotNull() & F.col("amount").isNotNull()
clean, review = typed.filter(valid), typed.filter(~valid)STAGE 04 / SERVE
Finish with a useful answer.
Curated tables make reporting simpler. Here, a daily sales aggregate gives a Power BI report a consistent dataset for exploring trends.
Analytics-ready tablesClear reporting grain
daily_sales.sqlSQL
SELECT CAST(order_date AS DATE) AS sales_date,
COUNT(*) AS total_orders,
SUM(amount) AS revenue
FROM silver.orders
GROUP BY CAST(order_date AS DATE);
-- One row per day, ready for reportingThis lab demonstrates a sample design. It is not a client case study or a claim of project delivery.
A LITTLE ABOUT ME
Jagadeesh CAZURE DATA ENGINEER5YEARS IN DATA
03 / THE PERSON BEHIND THE PIPELINES
An engineer's mindset.
A learner's curiosity.
Hi, I'm Jagadeesh. I work at the intersection of cloud, code, and data.
My background spans 5 years in data engineering, with a focus on scalable ETL/ELT pipelines, big data processing, and Azure-based data solutions.
Tech Pedagogy is my space to bring that work and technical thinking together: make complex ideas easier to follow, and show how the pieces of a data platform connect.
MY SPECIALIZATIONAzure Data Engineering
MY CORE LANGUAGESPython · SQL · PySpark
04 / LET'S CONNECT
Good things start
with a conversation.
Have a data challenge, a collaboration idea,
or an Azure engineering opportunity? Let's talk.