DEV Community: Shagun Khandelwal

🚀 Why You Should Pick Auto Loader Over Structured Streaming in Azure Databricks (The Funny Truth)

Shagun Khandelwal — Thu, 18 Sep 2025 16:35:19 +0000

Okay Linkediners, let’s be real.

Every time we talk about Azure Databricks Structured Streaming, it feels like that old reliable friend — the one who shows up at the party, eats all your snacks, and then says: “bro, I’ll leave when you stop streaming events.”

But then came Auto Loader. And suddenly Structured Streaming feels like Internet Explorer in 2025.

So why should you switch? Let’s break it down in the only way developers actually learn these days: funny memes + real talk.

1. 🧹 Auto Loader Cleans Up the Mess (Schema Evolution FTW!)

Structured Streaming: “Wait… your schema changed? Nope. I quit. Fix it and call me again.”
Auto Loader: “Oh, new column? No problem, I’ll just evolve gracefully like Pokémon.”

👉 Schema drift is real. Business folks add columns randomly like “Discount_Code_2025_Final_v2”. Auto Loader doesn’t panic, it just adapts.

2. 🐌 Bye-Bye Full List Scans

Structured Streaming: “Cool, let’s scan your entire cloud storage again to see what’s new. 🐌”
Auto Loader: “Nah fam, I’ll just keep track of what I’ve already ingested. Ain’t nobody got time for full scans.”

👉 Translation: Faster file discovery, less cost, fewer grey hairs.

3. 📂 Handles Millions of Files Without Crying

Structured Streaming with 10 million files: “Bruh, why do you hate me?”
Auto Loader with 10 million files: “Light work. Pass me another terabyte.”
👉 Auto Loader uses scalable file notification services like Azure Event Grid under the hood. It’s built for BIG data, not “oh look I uploaded 3 CSVs.”

4. ☕ Simpler to Use = More Coffee Time

Structured Streaming code feels like:

spark.readStream.format("csv")...

and then 20 extra lines to handle options, schema, watermarks, checkpoints…

Auto Loader code feels like:

df = spark.readStream.format("cloudFiles")...

and you’re basically done.

👉 Less boilerplate, fewer bugs, more time to scroll memes during standup.

5. 💸 Wallet-Friendly (Because Cloud Bills Hurt)

Auto Loader reduces storage list operations. Meaning?
Structured Streaming: “Let me list ALL the files again… surprise, here’s a $500 bill!”

Auto Loader: “Nah, I’ll just check incrementally.”
👉 Your finance team will finally stop sending you ‘WHY IS THE CLOUD BILL SO HIGH?’ emails.

6. Perfect for Medallion Architecture

Bronze layer ingestion? Auto Loader is the 🐐.
Works best for batch files, landing zones, logs, IoT dumps, JSON chaos from hell.
👉 Structured Streaming is still cool for event-driven Kafka-y stuff, but when it comes to cloud file landslide ingestion, Auto Loader is the clear winner.

Structured Streaming = your old Nokia. Solid, reliable, but… outdated.
Auto Loader = your shiny new iPhone. Handles schema drift, scales, saves 💰, keeps life simple.

So next time your team asks: “Why Auto Loader?”
Just say: “Because I like sleeping peacefully at night without worrying about schema changes and insane storage bills.”

🚀Git + Databricks: Why Both Are Essential for Modern Data Engineering

Shagun Khandelwal — Tue, 09 Sep 2025 19:48:01 +0000

Not long ago, I was working on a PySpark pipeline inside Databricks.
It was smooth, fast, and collaborative — and I thought to myself: “Databricks has versioning, so why do we even need Git?”

But the deeper I went into real-world data projects, the more I realized this:
👉 Databricks versioning is powerful for notebooks, but Git is irreplaceable for software-grade collaboration.

Let’s dive in.

📌 The Magic of Git
When you’re part of a team, Git isn’t just “nice to have” — it’s your safety net.

Here’s why:

1️⃣ Branching & Collaboration

Git allows multiple engineers to work on features simultaneously using branches.
Merge, compare, and resolve conflicts without breaking production code.
2️⃣ Code Reviews & Pull Requests

Databricks notebooks have version history, but they don’t provide the structured workflow of PRs, reviews, and approvals.
Git ensures that every line of code has accountability.
3️⃣ Integration with CI/CD

Git hooks into tools like GitHub Actions, Azure DevOps, or Jenkins.
That means your Databricks notebooks can become part of an automated testing and deployment pipeline.
4️⃣ Portability & Backup

With Git, your code isn’t locked inside Databricks.
You can clone, move, or share repositories across teams and organizations.
💡 In short: Git makes your project software-engineering ready.

📌 The Strength of Databricks
Now, let’s not underestimate what Databricks brings to the table:

1️⃣ Notebook Versioning

Every edit you make is saved — you can roll back to previous versions without fear.
2️⃣ Real-Time Collaboration

Think Google Docs for data pipelines. Multiple engineers can co-edit a notebook and see updates live.
3️⃣ Integrated Runtime & Execution

Unlike Git, Databricks doesn’t just track code — it actually executes it on clusters.
That means version history includes not only the code, but the runtime context.
4️⃣ UI for Data Teams

Not every data engineer is a Git wizard. Databricks versioning provides a low-barrier entry point for tracking changes.
🌟 The Best of Both Worlds
Here’s the truth:

Databricks versioning = great for quick collaboration and small changes.
Git = essential for large-scale projects, production pipelines, and enterprise-grade workflows.
Together, they create a workflow that’s both agile and reliable:

Experiment in Databricks notebooks with built-in versioning.
Push stable code to Git for collaboration, reviews, and CI/CD.
Deploy seamlessly with confidence.
Let me tell you something.

In one of my projects, we had 5+ engineers working on a single ETL pipeline.

Without Git, we kept overwriting each other’s changes inside notebooks. Chaos! 😅
Once we integrated Git, we could branch, review, and merge cleanly — while still enjoying Databricks’ notebook history for small fixes.
The result?
⚡ Faster collaboration
⚡ Fewer production bugs
⚡ A happier engineering team

So, why Git if Databricks already has versioning?
👉 Because Git brings discipline, structure, and scalability, while Databricks brings collaboration and execution power.

Think of it this way:

Databricks is your playground 🎢
Git is your safety harness 🛡️
Together, they ensure you can build, experiment, and scale with confidence.

💡 My advice: If you’re starting with Databricks, enjoy its versioning — but don’t skip Git. Master both, and you’ll be unstoppable in your data engineering career. 🚀

🚀 How PySpark Helps Handle Terabytes of Data Easily

Shagun Khandelwal — Sun, 07 Sep 2025 10:52:04 +0000

A few years back, data teams struggled whenever they faced huge datasets. Imagine trying to process terabytes of logs, transactions, or clickstream data with just traditional tools — slow, clunky, and often impossible within deadlines.

Back then, Hadoop’s MapReduce was the go-to option. It worked… but at a cost:

Lots of disk I/O (read → write → read again).
Complicated to code in Java.
Slow performance when you just needed quick insights.

Then came Apache Spark 🔥 — and with it, PySpark (the Python API for Spark).

🌟 Why PySpark Handles Big Data So Well

1️⃣ Distributed Computing
Instead of one machine crunching everything, Spark splits data across a cluster of machines, letting them work in parallel.

2️⃣ In-Memory Computation
Unlike MapReduce (which keeps writing intermediate results to disk), Spark keeps data in memory (RAM) whenever possible. This makes it 10–100x faster.

3️⃣ Python-Friendly
With PySpark, data engineers can write Spark jobs in Python, which is far simpler than old-school Java-based MapReduce code.

4️⃣ Partitioning for Scale
Big data is usually too large to fit on a single node. PySpark automatically partitions datasets across multiple machines. You can even control partitioning to optimize joins, shuffles, and data locality — which means more efficient resource usage.

5️⃣ Caching for Reuse
If you’re running multiple operations on the same dataset, PySpark allows you to cache or persist it in memory. Instead of re-reading and re-computing from scratch, Spark just pulls it directly from memory — saving massive time when working with terabytes of data.

💻 A Quick Example

Here’s how the two approaches look in practice:

🔹 MapReduce (pseudo-code style)

map(String line):
    for word in line.split(" "):
        emit(word, 1)

reduce(String word, List<int> counts):
    emit(word, sum(counts))

🔹 PySpark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("WordCount").getOrCreate()
text = spark.read.text("big_dataset.txt")

word_counts = (
    text.rdd.flatMap(lambda line: line.value.split(" "))
        .map(lambda word: (word, 1))
        .reduceByKey(lambda a, b: a + b)
)

word_counts.collect()

🚀 Why It Matters for Data Engineers

Today’s world runs on huge datasets — think Netflix logs, Uber rides, Amazon orders. PySpark helps data engineers:

Process data at massive scale
Speed up workflows with caching
Optimize performance with partitioning
Deliver insights faster and cheaper

That’s why PySpark has become one of the core tools in modern Data Engineering.

If you’re aiming to work with big data, learning PySpark isn’t just useful — it’s essential. It’s the bridge between raw data and scalable, real-world insights.

🚀 The Future of Data Engineering: How AI and Automation are Changing the Game

Shagun Khandelwal — Tue, 02 Sep 2025 15:48:01 +0000

A few years back, most data engineers were busy writing long ETL scripts, scheduling nightly batch jobs, and ensuring data pipelines didn’t break. It was manual, repetitive, and often painful.

But fast forward to today — the world of Data Engineering is evolving at lightning speed, thanks to AI and Automation. 🚀

🔹 The Shift We’re Seeing

From Batch to Real-Time: Businesses no longer wait for yesterday’s reports; they want insights now. Spark Streaming, Kafka, and real-time ETL tools are rising.

From Manual ETL to Auto-ETL: Low-code/no-code platforms + AI-driven data pipelines are replacing hand-coded scripts.

From Data Lakes to Lakehouses: Storage + compute + ML integrated in one ecosystem (Databricks, Snowflake).

🔹 The Role of AI in Data Engineering

AI isn’t here to replace data engineers — it’s here to supercharge them:

Smart Data Cleaning → AI models detect anomalies, missing values, schema drifts.

Automated Schema Mapping → Tools suggest how tables should connect.

Intelligent Orchestration → Pipelines self-heal if something fails.

AI-Driven Monitoring → Instead of endless logging, AI highlights the real issue in seconds.

🔹 Why This Matters for the Future

Companies are producing unimaginable amounts of data — IoT, social media, transactions, AI models themselves. Managing this flood requires scalable, distributed, and intelligent systems.

This means:

Data Engineers are becoming more valuable than ever.

Demand is shifting from just “pipeline builders” → to data platform architects + AI-aware engineers.

🔹 What to Learn to Stay Ahead 🚀
If you’re preparing for this AI-powered future, here are must-have tools/skills:

PySpark / Apache Spark → In-memory big data processing.

Kafka → Streaming + event-driven pipelines.

Databricks / Snowflake → Modern cloud data platforms.

Airflow / Prefect → Workflow orchestration.

ML basics → To understand how AI fits into pipelines.

So here’s the thing…
The same way MapReduce gave way to Spark, traditional ETL is giving way to AI-powered data engineering.

If you’re a data engineer today, you’re not just building pipelines — you’re shaping the future of how businesses run.

🔄 ETL vs ELT: What’s the Difference and Why It Matters?

Shagun Khandelwal — Sun, 31 Aug 2025 10:53:09 +0000

When I first started learning data engineering, I kept hearing two terms: ETL and ELT. At first, they sounded almost the same — just a reshuffling of letters. But the more I dug in, the more I realized these three letters represent a big shift in how modern data engineering works.

Let me tell you the story.

📦 The Old Way: ETL

Back in the early days of big data, storage and compute were expensive. So the process looked like this:

Extract → Pull data from sources (databases, APIs, logs).

Transform → Clean, filter, and reshape data before loading.

Load → Store the processed data in a warehouse for analysis.

This is ETL (Extract → Transform → Load).

Imagine you’re moving into a new house. Before you carry boxes inside, you unpack everything, clean it, and arrange it nicely. Only then do you place it in your home.

It worked… but it was slow and limited. Transformations were often done with tools like Informatica, Talend, or Spark jobs outside the warehouse.

⚡ The Modern Way: ELT

Then came the rise of cloud data warehouses like Snowflake, BigQuery, and Redshift. These platforms were powerful, scalable, and cheap compared to traditional systems.

So the approach flipped:

Extract → Pull raw data from sources.

Load → Dump it straight into the warehouse, no waiting.

Transform → Use the warehouse’s computing power (SQL, dbt) to clean and reshape inside.

This is ELT (Extract → Load → Transform).

Now, you’re moving into a house but instead of unpacking outside, you just carry everything in and organize later. Since your house (data warehouse) is spacious and strong, it can handle the mess.

🔍 Why Does This Matter?

The shift from ETL → ELT changes a lot for data engineers:

Speed: ELT loads data faster since you don’t wait for transformations outside.
Scalability: Cloud warehouses can handle petabytes of data with ease.
Flexibility: You can re-transform data anytime without reloading it.
Cost Optimization: You pay for warehouse compute only when you use it.

ETL isn’t dead — it’s still useful when compliance requires cleaning before storage — but ELT has become the new standard for modern pipelines.

ETL Approach (transform before load):

# Transform data before loading
cleaned_data = []
for row in raw_data:
    if row["status"] == "active":
        cleaned_data.append(row)

# Load into warehouse
warehouse.load(cleaned_data)

ELT Approach (load first, transform later):

-- Load raw data into warehouse (no cleaning yet)
COPY INTO raw_table FROM 's3://bucket/raw/'

-- Transform inside warehouse
CREATE TABLE clean_table AS
SELECT * FROM raw_table WHERE status = 'active';

With ELT, you’re leveraging the warehouse’s powerful SQL engine instead of doing the heavy lifting outside.

When I first understood the difference, it clicked: ETL was built for the old world of limited compute, while ELT was made for the cloud-first era.

So if you’re starting in data engineering today, remember:
👉 Learn both, but master ELT. That’s where the industry is headed.

The letters may look similar, but the shift they represent is massive.

🌍 The Journey of Data: From Raw Logs to Insights

Shagun Khandelwal — Thu, 28 Aug 2025 15:26:30 +0000

When I first stepped into the world of data engineering, I thought working with data meant handling neat little rows in Excel or maybe a clean SQL table. Simple, right?

But reality hit me differently. One of my first projects involved terabytes of messy logs — clicks, transactions, random user events. It wasn’t data you could just load into Excel and make a chart out of. It was like staring at a huge pile of raw stones and being asked to build a palace out of it.

That’s when I realized: data has a journey.

📝 Step 1: Where Data Is Born

Imagine an e-commerce site. Every click, every search, every purchase is recorded. Add to that mobile app usage, payment transactions, and server logs.

This is raw data. Huge, unstructured, chaotic. Valuable? Yes. Ready to use? Absolutely not.

📥 Step 2: Collecting the Chaos

Now comes ingestion. Think of it as gathering all those scattered stones into one place. Tools like Kafka, Flume, or AWS Kinesis act like giant conveyor belts, moving raw data from different sources into a central system.

This is where data engineers ensure no piece is lost in transit.

🧹 Step 3: Refining the Gold

But raw stones aren’t enough. You need to refine them into gold.

This is where ETL/ELT pipelines enter the scene. Using PySpark, SQL, Airflow, we:

Remove duplicates
Fix errors
Standardize formats
Combine with other datasets

Suddenly, what looked like chaos starts forming into something meaningful.

🏗 Step 4: Giving It a Home

Once cleaned, data needs a permanent home:

Warehouses (Snowflake, BigQuery, Redshift) → for structured, query-ready data
Data Lakes (S3, Azure Data Lake) → for raw/unstructured data
Lakehouses (Databricks, Delta Lake) → the best of both worlds

Think of this as building a giant library. Each dataset is a book, neatly cataloged, ready to be read.

📊 Step 5: Turning Data Into Insights

Now comes the exciting part.

Analysts, scientists, and business teams use BI tools like Power BI, Tableau, Looker or ML models to transform that stored data into:

KPIs 📈
Dashboards
Predictions

And just like that, yesterday’s messy logs become today’s business insights.

What I learned is this: behind every dashboard, every “AI-powered recommendation,” every decision made in a boardroom, there’s a team of data engineers who built the pipelines, cleaned the mess, and made the data trustworthy.

We may not always be in the spotlight, but we’re the ones keeping the data world alive.

That’s the journey of data.

⏰ If You Haven’t Shifted to Data Engineering Yet… Wake Up!

Shagun Khandelwal — Tue, 26 Aug 2025 19:21:31 +0000

A few years ago, everyone wanted to become a Data Scientist. The hype was real — AI, ML, deep learning models. But quietly, another role was becoming just as important (and in some ways, even more critical): Data Engineer.

Fast forward to today, and guess what?
👉 Without data engineers, most data scientists wouldn’t even have clean, reliable data to work with.

🌍 The Market Status of Data Engineering

Right now, data is exploding. Every company — from startups to FAANG giants — is generating terabytes daily.

Reports show Data Engineer roles are growing faster than Data Scientist roles.
Companies are desperate for people who can build pipelines, manage big data, and ensure data quality.
And yes… the salaries are very rewarding. In fact, a skilled Data Engineer can often match or even surpass the pay of a Data Scientist. 💰

The demand is here, the future is bright — and it’s only going to grow.

🔧 What Tools Should You Learn?

If you want to ride this wave and become a Data Engineer, here are some tools you must know:

Programming: Python, SQL
Big Data Frameworks: PySpark, Apache Spark, Hadoop
Cloud Platforms: AWS, Azure, GCP
Data Warehousing: Snowflake, BigQuery, Redshift
Workflow Orchestration: Apache Airflow
Streaming: Kafka
ETL & Databases: SQL/NoSQL databases, Databricks

These aren’t just buzzwords — they’re the actual day-to-day tools used in top companies.

💡 My Advice

If you haven’t shifted into Data Engineering yet, don’t think it’s too late.
The market is booming, opportunities are everywhere, and the skill gap is real.

Start small — learn SQL, pick up PySpark, and get familiar with cloud tools. Build simple ETL pipelines, then grow from there.

In a few years, you’ll look back and thank yourself. 🚀

🚀 Final Words

The world is powered by data.
Data Engineers are the ones building the highways that carry it.

So if you’re still thinking about it… wake up. The future is now.

⚡ From MapReduce to Spark: Why In-Memory Beats Disk I/O

Shagun Khandelwal — Mon, 25 Aug 2025 19:05:14 +0000

Back when I first started learning about Big Data, the tool everyone kept mentioning was Hadoop MapReduce. At the time, it felt revolutionary — splitting big datasets into chunks, distributing them across machines, and combining the results.

But as I started working more with data, I quickly realized something:
👉 MapReduce was powerful, but it was also slow.

Why? Because it relied heavily on disk I/O operations. After every map step, results were written to disk. Then the reduce step would read them back again. On small datasets, this wasn’t too bad… but on terabytes of data, it became painfully slow.

That’s when I discovered Apache Spark.

Unlike MapReduce, Spark performs most computations in-memory, drastically reducing disk reads/writes. This one design choice made Spark almost 100x faster in certain workloads.

And the best part? Spark is an open-source distributed computing framework — meaning it can scale seamlessly across clusters, just like MapReduce, but with much better performance and flexibility.

For a data engineer, Spark felt less like an upgrade and more like a game-changer.

🧑‍💻 MapReduce vs Spark Syntax

Here’s what really drove the point home for me: the code difference.
👉 A simple word count in Hadoop MapReduce (Java) looked like this (simplified):

public static class TokenizerMapper
     extends Mapper<Object, Text, Text, IntWritable>{

  private final static IntWritable one = new IntWritable(1);
  private Text word = new Text();

  public void map(Object key, Text value, Context context
                  ) throws IOException, InterruptedException {
    StringTokenizer itr = new StringTokenizer(value.toString());
    while (itr.hasMoreTokens()) {
      word.set(itr.nextToken());
      context.write(word, one);
    }
  }
}

Then you’d need a Reducer class, boilerplate setup code, and job configuration… easily 100+ lines for something as simple as word count. 😓
👉 The same thing in PySpark (Python API for Spark)?

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("WordCount").getOrCreate()
sc = spark.sparkContext

text_file = sc.textFile("sample.txt")
counts = text_file.flatMap(lambda line: line.split(" ")) \
                  .map(lambda word: (word, 1)) \
                  .reduceByKey(lambda a, b: a + b)

counts.collect()

Less than 10 lines of code. Clean, readable, and still running on a distributed cluster.

That’s when I realized: Spark isn’t just faster — it makes big data engineering simpler.

🌟 Why Spark Wins

Performance: In-memory computations crush disk I/O bottlenecks.

Simplicity: Fewer lines of code, especially with PySpark.

Flexibility: Supports SQL, streaming, ML, and graph processing out of the box.

Community: Open-source, widely adopted, and actively growing.

My Advice if I were at your place:-
If MapReduce was the first chapter of Big Data, then Spark is the sequel everyone was waiting for.

For me, learning Spark wasn’t just about speed — it was about writing cleaner, more expressive code while working at scale. And that’s why Spark has become the default choice for modern data engineers.

My Journey with PySpark: Why Every Data Engineer Should Learn It

Shagun Khandelwal — Sun, 24 Aug 2025 13:10:31 +0000

A few months back, I was staring at a dataset that was way too big for my laptop to handle. I tried the usual Python tricks — Pandas, NumPy — but everything crashed. That’s when I realized: if I want to be a real data engineer, I need something built for scale.

That’s when I stumbled upon PySpark. 🚀

🌟 What is PySpark?

At first, I thought Spark was just another “buzzword” tool. But soon I learned that PySpark is the Python API for Apache Spark, which means I could use my favorite language (Python) while leveraging Spark’s ability to process massive datasets across clusters.

It felt like magic ✨ — suddenly, I wasn’t limited by my machine’s memory anymore.

⚡ Why PySpark Changed the Game for Me

When I compared it to the old-school Hadoop MapReduce, the difference was night and day:

MapReduce kept writing things to disk → super slow.

Spark keeps most things in memory → blazing fast.

With PySpark, I could write simple, readable Python code, not 100 lines of complex MapReduce jobs.

That’s when I understood why companies like Netflix, Amazon, and Uber rely on it. If you’re a data engineer dealing with terabytes of data, PySpark feels less like a tool and more like a superpower. 💪

🧑‍💻 My First PySpark Code

I still remember the first time I ran this:

from pyspark.sql import SparkSession

# Start Spark
spark = SparkSession.builder.appName("FirstPySparkApp").getOrCreate()

# Sample data
data = [("Alice", 34), ("Bob", 45), ("Cathy", 29)]
columns = ["Name", "Age"]

# Create DataFrame
df = spark.createDataFrame(data, columns)

# Show Data
df.show()

# Simple transformation
df.filter(df.Age > 30).show()

And the output? A neat little table printed out in my terminal. Nothing fancy — but the realization that the same code could scale to billions of rows on a cluster… that gave me goosebumps.

🚀 Why You Should Learn PySpark Too

If you’re someone who dreams of building big data pipelines, cloud solutions, or ML workflows at scale, PySpark is your best friend.

It’s widely used in the industry.

It makes you stand out in data engineering interviews.

And honestly… it’s just fun to see huge datasets bend to your commands. 😎

So if you haven’t already, start experimenting with PySpark today. Trust me — your future self (and your resume) will thank you.

🚀 New Series on Data Engineering Tools & Platforms

Shagun Khandelwal — Sun, 24 Aug 2025 12:54:47 +0000

Hi everyone 👋,

I’m Shagun Khandelwal, currently working as a Big Data Engineer at Infosys with 3 years of experience in the data engineering space. Over the years, I’ve had the opportunity to work with large-scale data pipelines, distributed systems, and modern platforms that make handling terabytes of data efficient and impactful.

💡 Data engineering is the backbone of modern data-driven companies. While Data Science and AI get most of the spotlight, it’s the data engineering layer that ensures data is accessible, reliable, and scalable.

That’s why I’ve decided to start a series where I’ll be sharing my learnings, practical tips, and deep dives into different tools, frameworks, and platforms that every data engineer should know.

What you can expect in this series:

🔹 Basics of Data Engineering & its role in modern organizations

🔹 Hands-on insights into tools like PySpark, Kafka, Airflow, Hadoop, Databricks, and Cloud Platforms (Azure/GCP/AWS)

🔹 Best practices for scalability, optimization, and performance tuning

🔹 Real-world use cases from my professional experience

This series is meant for aspiring data engineers, data enthusiasts, and professionals who want to get practical knowledge of how things work in real-world projects.

Stay tuned — the first post will be dropping soon!
Follow me here so you don’t miss out 🙂

Let’s learn and grow together! 🌱

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