#pyspark

Migrating Informatica PowerCenter to Azure Databricks: A Practical Playbook

A condensed field guide for engineers and architects migrating Informatica PowerCenter to Azure Databricks including architecture mapping…

🏗️Writing Production-Grade PySpark — Patterns That Scale

Most PySpark Code Works in Dev and Breaks in Production. Here Are the Patterns That Survive Both.

Why AWS Glue Local Image Could Not Use Login Credentials

Background

How to Handle Long-Running Jobs in Databricks: A Practical PySpark Playbook

By Kushal Vishwakarma

Spark 02: The Spark Architecture

Part 1 covered why Spark exists in the first place. But who decides what runs where? What happens when you hit “run” on your code? Who’s…

How I thought parallelizing parallelly running task..

I started with a simple question.

Understanding UDFs in PySpark

PySpark provides a rich set of built-in functions in pyspark.sql.functions for transforming DataFrames. In many cases, these functions are…

✨Spark UI Trek: The Klingon Attack

Spark UI: The Last Frontier. A chaotic telemetry matrix where cheerful progress bars pretend everything is fine, while red error logs…

SPARK LEARNING DAY 6 -Structured Streaming -My Learnings Notes from the “O’Reilly Spark: The…

The best thing about Structured Streaming isn’t the streaming part — it’s that you barely have to change any code to get there.

🚀 Python’s Biggest Shift in Years: Welcome to the Lazy Future- Every PySpark Developer Should Pay…

The latest evolution of Python 3.14 marks one of the most interesting shifts the language has seen in years.

Part 4: Advanced Data Engineering: Skew, Streams, and the Delta Lake Revolution

The advanced techniques that separate hobbyists from production experts.

Why Is Dataflow Gen2 So Slow?

A Guide for Microsoft Fabric Engineers

The PySpark Interview Question That Trips Up 90% of Senior Data Engineers (And How to Nail It)

Apache Spark Mastery (Part 4 of 6): Joins & Aggregations

Where tidy data finally turns into insight — combine it, then summarize it

How to Search and Extract Text in PySpark Using String Functions

Top 6 essential functions to find, locate and pull exactly what you need from text columns. · 6 mins read

Adaptive Query Execution (AQE): How Spark Re-Optimizes Queries at Runtime

Two Spark queries can return exactly the same result.

Customer Lifetime Value (CLV) & Churn Prediction using Survival Analysis with PySpark

This project simulates a real-world MNC-scale Customer Lifetime Value (CLV) platform using:

Abordagem Híbrida de Parsing Estruturado e (LLMs) para a Conversão de Pipelines de ETL Legados…

A modernização de sistemas legados de Extração, Transformação e Carga (ETL) representa um dos maiores gargalos na migração para…

df.show() vs df.collect(): The One-Letter Mistake That Crashes Spark Clusters

A junior data engineer adds one innocent line to “just peek at the data” before running a 200 GB pipeline:

Cost-Based Optimizer (CBO) in Spark: Statistics, Cardinality Estimation, and Join Selection

Two Spark queries can return exactly the same result.

Catalyst Optimizer in Apache Spark: The Compiler Behind Every DataFrame Query

Most Spark developers know Catalyst as:

25 PySpark DataFrame Operations Every Data Engineer Should Know

A practical hands on guide covering joins, aggregations, window functions, schema handling, Spark SQL, and real world transformations.

If AQE can create Broadcast Joins Automatically, why do we still need Broadcast Hints?

Before we dive into today’s topic, I highly recommend checking out my previous blogs on Spark Join internals if you haven’t already—

Apache Spark WTF??? 📈 Nothing Breaks Like A Chart 📉

Data engineering at scale is a surrealist act: we manipulate variables that don’t exist yet across a cloud of invisible computers. But for…

Feel the Distributed Group By in Spark

Hello Friends, welcome to another deep dive into Data Engineering concepts through the lens of Query Profiles!

Apache Spark WTF??? 😈 Vices & Virtues 😇

Welcome, brothers and sisters of the bit, to the cathedral of Spark Data Sources V2.

Your Spark Tests Aren’t Flaky. They’re Starving.

A wall of ConnectionRefusedError in CI, one real OOM, and two lines that fixed it. Here’s the debugging chain that led there.

Apache Spark WTF??? ❤️ What You Need Is Log 🪵

Yesterday, all my troubles seemed so far away. Then Spark failed. Now it looks as though the logs are here to stay…

SCD Type 2 in PySpark — Comparing the Join and Union + Lead Approaches

If you have ever needed slowly changing dimension (SCD) Type 2 logic in PySpark and reached for a homegrown solution, you have probably…

Cracking the Ultimate PySpark Interview Question: How to Flatten Dynamic JSON Without Hardcoding…

How to flatten a completely dynamic, semi-structured JSON column in PySpark without hardcoding a single schema or melting your cluster

Mastering plotly.graph_objects: How to plot pandas and pyspark data without plotly express

Content

Apache Spark WTF??? 🙏 Losing My Precision 🧮

Oh life, it’s bigger. It’s bigger than DoubleType.

Databricks Interview Question : 200 Jobs, Massive Bills: How to Refactor Databricks Compute to…

“We run 200 jobs a day. Each one takes 3 minutes. Why is our monthly cloud bill looking like a mortgage payment?”

Spark, dbt and Airflow — A Practical Walkthrough

Building a medallion architecture pipeline from scratch

Databricks MERGE Was 5x Slower Than an Overwrite — The Hidden Mistake That Was Killing Our SLA

Databricks · Delta Lake · PySpark · Performance

Narrow vs. Wide Transformations no PySpark

No artigo anterior vimos que transformations são instruções que descrevem como o Spark deve transformar um DataFrame, e que graças ao lazy…

Deep Dive into Delta Lake Transaction Log (_delta_log)

Part of series: Databricks Complete Guide (Beginner to Advanced)

Mastering Failure Handling in PySpark: Building Data Pipelines That Survive the Real World

In the world of big data, failures aren’t exceptions — they’re the default. Networks drop, executors crash, data arrives corrupted, and…

Why Spark Uses Lazy Evaluation: How Spark Waits, Plans, and Executes Smarter

Understanding why Spark does not execute transformations immediately and how this helps optimize real-world data pipelines.

Understanding Spark Transformations and Actions Through Source Code

RDD is the core abstraction for working with distributed data in Apache Spark. We process data by applying operations such as…

Partitioning Techniques That Turbocharge PySpark & Databricks SQL Queries (Real Interview Examples)

Imagine waiting hours for a simple SQL query on a massive dataset, only to watch your Databricks cluster burn cash while spinning its…

SQL ou PySpark no Databricks: a resposta que eu gostaria de ter recebido no começo

Quando comecei a trabalhar com Databricks, uma dúvida aparecia com frequência:

What happens internally when you run a Spark Submit command?

Lets dive right into it!

Pandas vs PySpark Syntax — Why the Syntax Is Different (And Why It Actually Matters)

Many professionals transitioning from Python analytics into Big Data engineering often ask:

I Stopped Building Dashboard Projects and Tried Solving a Real-Time Problem Instead

Most student data projects follow the same pattern.

Small PySpark Experiments That Helped Me Understand Spark Internals

When learning PySpark, I found some interesting behavior related to partitions and parallel execution.

What Surprised Me Most About Learning Databricks

I started learning Databricks expecting a complex setup with multiple configurations, but the biggest surprise for me was how…

Decode Data Engineer Interviews — Real Question Explained

Understanding Lazy Evaluation in PySpark with Real-world Analogy

Transformações no PySpark: o que você precisa saber

Transformações no PySpark

Building a Production-Grade Data Lakehouse on Labour Force Data — Without a Cloud Bill

A walkthrough of how modern data pipelines actually work, built end-to-end on a laptop.