Madhu H R’s Post

PySpark Optimization Tips Every Data Engineer Should Know ⚡ Working with big data? Then performance is everything. Even the most elegant PySpark code can slow down if not optimized properly. Here are my go-to PySpark optimization techniques for real-world Databricks projects 👇 ✅ 1. Use Broadcast Joins When one dataset is small, use: from pyspark.sql.functions import broadcast result = large_df.join(broadcast(small_df), "ID") 💡 This avoids expensive shuffles across the cluster. ✅ 2. Cache or Persist Reused Data df.cache() Great when you reuse the same DataFrame multiple times in a workflow. ✅ 3. Optimize File Sizes Write data in optimal partitions: df.repartition(10).write.format("delta").save(path) ✅ 4. Push Down Filters Early Filter data as early as possible before joins or aggregations. ✅ 5. Use Delta Format Instead of Parquet Delta brings indexing, caching, and transaction benefits — perfect for high-performance reads. 💡 Small optimizations = big savings in execution time and cluster cost. 👉 What’s one Spark optimization trick you always use? #PySpark #Databricks #Azure #DataEngineering #SparkOptimization #DeltaLake #BigData

🚀 Why choose Parquet over CSV for your data pipelines? If you’re still storing or processing your large datasets in CSV — it might be time to switch gears! ⚙️ Here’s why Parquet is often a better choice 👇 1️⃣ Columnar Storage — Parquet stores data by columns, not rows. This means faster reads when you only need a few columns out of millions. 2️⃣ Compression & Encoding — It’s highly compressed (often 5–10x smaller than CSV), reducing both storage and I/O costs. 3️⃣ Schema Evolution — Parquet supports data types and schema evolution — something CSVs can’t handle natively. 4️⃣ Query Performance — Column pruning + predicate pushdown = blazing-fast analytics! ⚡ 5️⃣ Integration — Parquet is the native format for most big data tools like Spark, Hive, Snowflake, Athena, and Redshift Spectrum. 6️⃣ Data Integrity — Parquet maintains metadata and enforces consistent data types, unlike CSV where everything’s just text. 📊 In short: CSV is great for portability and simplicity. But Parquet is built for performance, scalability, and efficiency. 💡 Tip: If your workflow involves analytics, reporting, or machine learning — go Parquet all the way! #BigData #DataEngineering #DataScience #ApacheSpark #SQL #Python #reach #databricks #lakehouse #spark #dataengineer #data #datascientist #DataAnalytics #BigData #Snowflake #Databricks #DataLake #DataWarehouse #CloudComputing

🔥 5 PySpark Features Every Data Engineer Should Know (But Few Actually Use) When I first started with PySpark, I leaned heavily on the basics: select, filter, and groupBy. They worked fine, but my pipelines were verbose, slow, and hard to maintain. Over time, I discovered a handful of underrated features that made my workflows cleaner, faster, and much more reliable. Here are five that every data engineer should have in their toolkit. 1️⃣ WithColumnRenamed → Consistent Schemas Data coming from multiple sources often has inconsistent column names. Renaming columns early in your pipeline creates a standard schema that makes joins and downstream transformations far less error-prone. 2️⃣ Distinct → Faster Deduplication While dropDuplicates is often the go-to, distinct is a lighter and faster option when you simply want unique rows. It’s especially useful in staging layers where performance matters. 3️⃣ SQL Views on DataFrames → Cleaner Complex Queries Chaining too many DataFrame transformations quickly becomes messy. By creating a temporary SQL view from your DataFrame, you can write queries in SQL instead. This makes complex logic easier to read, maintain, and share across teams who are already fluent in SQL. 4️⃣ Lit → Add Constants Without Joins Sometimes pipelines overcomplicate things by joining small “constant” tables just to tag data. Using lit allows you to inject fixed values directly, such as adding lineage information or marking the dataset source. It’s a clean and efficient way to enrich your data. 5️⃣ Cache → Smarter Performance One of the hidden performance killers in Spark is recomputation. Without caching, Spark re-runs the entire transformation chain every time you reference the same DataFrame. By caching at the right points, you keep results in memory and cut down runtime dramatically.

Funny thing about working in Data ,no matter how advanced the tech gets, it always speaks SQL. We’ve got Spark clusters, Delta tables, Lakehouses, Pipelines, and AI-driven platforms crunching petabytes of data… But at the heart of it all , the one language that still quietly runs the show is SQL. Not because it’s old. Because it makes you think. SQL makes you reason through data. It teaches you to FILTER what matters, JOIN what’s meaningful, and GROUP what adds value. It forces you to slow down and truly understand the logic behind your transformations & not just write code that runs. When you understand SQL deeply, you start thinking in data. You visualize how tables CONNECT, how HIERARCHIES form, how FACTS and DIMENSIONS come together to tell a story. That’s when you stop coding pipelines and start building data systems that make sense. Even in Spark, every join, groupBy, and window operation is still SQL logic behind the scenes. The best PySpark developers aren’t the ones who know every function -they’re the ones who think like SQL. And when things break (because they always do 😄), SQL thinking is what saves you. You debug not by guessing but by slicing, filtering, and reasoning through data. SQL makes you platform-proof too. Tools will keep changing- Fabric, Snowflake, Databricks, BigQuery - but SQL remains the one constant language across them all. SQL isn’t just a query language. It’s a mindset. It’s how data engineers think. So yes, build your Spark skills, automate pipelines, explore new tools… But never forget: the engineers who think in SQL will always stand out. #DataEngineering #SQL #Spark #BigData #MicrosoftFabric #Databricks #ETL #DataModelling #DataMindset #CareerInData #LearningNeverStops

Beware of withColumn Pitfalls in PySpark! If you’ve worked with PySpark, chances are you’ve used .withColumn() to add or transform DataFrame columns. But did you know misusing it—especially inside loops or chaining too many times—can tank your Spark job’s performance? Here are common pitfalls to avoid: 1. Chaining multiple .withColumn() calls: Every call triggers a new logical transformation and can lead to complex, bloated execution plans. df = df.withColumn("col1", ...) df = df.withColumn("col2", ...) df = df.withColumn("col3", ...) ... 2. Using .withColumn() in a loop: Repeatedly calling it inside loops increases job runtime drastically and can even cause StackOverflow errors on large DataFrames. for c in columns: df = df.withColumn(c, ...) 3. Applying UDFs unnecessarily: User Defined Functions (UDFs) with .withColumn() often lead to serialization overhead and can slow things down. How to avoid these problems? 1. Batch all transformations using a single .select() / .selectExpr() statement df = df.select( "*", (col("price") * 1.18).alias("price_with_tax"), (col("price") > 1000).alias("is_expensive") ) df = df.selectExpr( "*", "price * 1.18 as price_with_tax", "price > 1000 alias is_expensive" ) 2. Batch all your column transformations using .withColumns(). df = df.withColumns({ "new_col1": expr1, "new_col2": expr2, # ... }) 3. Prefer Spark built-in functions over UDFs for transformations. 4. Always check your DataFrame plans with df.explain(). Remember: Smarter column operations lead to faster Spark jobs and happier data teams! Have you faced .withColumn() performance issues? Share your story or tips in the comments! #PySpark #DataEngineering #SparkPerformance #BigData

⚙️The next wave of data innovation isn’t written in Python. It’s written in SQL. While everyone’s chasing new frameworks and shiny stacks, SQL has quietly become the foundation of modern data engineering. Here’s why. → Every major platform — Snowflake, BigQuery, Databricks — runs on SQL-first architecture. → Data transformation tools like dbt are literally built around SQL logic. → Even Apache Spark and Airflow now treat SQL as a first-class citizen. → And the best part? It scales seamlessly from small analytics to massive production pipelines. 🚀 The truth? SQL isn’t just a query language anymore. It’s how data engineers: ↳ Define transformations ↳ Orchestrate ETL pipelines ↳ Build reproducible, testable data models ↳ Collaborate with analysts and scientists without friction 💡 Modern data engineering isn’t about replacing SQL. It’s about reimagining what SQL can do. If you can think in SQL, you can build anything. Do you think SQL will still dominate data engineering 5 years from now? #DataEngineering #SQL #BigData #DataAnalytics #DataScience #ETL #DataTransformation #CloudData #ModernDataStack #TechInnovation

🚀 Top 5 Data Formats Every Data Engineer Must Know CSV isn’t enough anymore. A smart data engineer knows when to use Parquet, Avro, ORC, JSON, or Delta. Because the right format can make or break your data pipeline. ⚙️ Let’s break it down 👇 1️⃣ CSV (Comma-Separated Values) 🧩 Simple & human-readable ⚠️ No schema enforcement, large file sizes ✅ Best for: Quick exports & small datasets 2️⃣ JSON (JavaScript Object Notation) 🌐 Flexible & supports nested data ⚠️ Larger size, slower for analytics ✅ Best for: APIs & semi-structured data 3️⃣ Parquet 📊 Columnar format → compresses & queries fast ⚡ Excellent for analytical workloads (Spark, Athena, Snowflake) ✅ Best for: Big data analytics 4️⃣ Avro 🧠 Row-based + schema evolution support ⚙️ Compact & great for serialization ✅ Best for: Streaming data & Kafka pipelines 5️⃣ ORC (Optimized Row Columnar) 💪 High compression & fast reads ⚡ Built for Hadoop ecosystem ✅ Best for: Hive & large-scale batch processing 💡 Bonus: Delta Format (from Databricks) adds versioning + ACID transactions on top of Parquet. Think of it as Parquet 2.0 for reliable data lakes. 🎯 Key takeaway: Don’t pick formats randomly — pick them based on usage 👇 🔍 Analytics → Parquet / ORC ⚡ Streaming → Avro 🌐 APIs → JSON 🧾 Exports → CSV 💬 What’s your go-to format in your data projects? #DataEngineering #BigData #ETL #Snowflake #Spark #Databricks #DataEngineer #DataFormats #Parquet #Avro

🚀 Partitioning vs Bucketing in PySpark — Optimizing Big Data the Smart Way! As data engineers, we often focus on writing complex transformations — but true performance tuning starts before you run your Spark job. It begins with how you organize your data. 💡 Let’s talk about two powerful techniques that can make your queries fly: 🔹 Partitioning Partitioning means splitting large datasets into smaller, more manageable chunks based on column values. 👉 Example: Partitioning by country or year This ensures Spark reads only the relevant partitions, avoiding a full dataset scan. ✅ Benefits: Faster query performance Efficient filtering (pruning) Better parallelism ⚠️ But beware: Too many small partitions = overhead Too few = poor parallelism 🔹 Bucketing Bucketing, on the other hand, distributes data within each partition into fixed-size buckets using a hash function on the bucketing column. 👉 Example: Bucketing by user_id into 8 buckets ✅ Benefits: Optimized joins (especially when both datasets are bucketed on the same key) Reduces shuffle during joins & aggregations ⚙️ Code Example: # Partitioning df.write.partitionBy("country", "year").parquet("/data/partitioned/") # Bucketing df.write.bucketBy(8, "user_id").sortBy("user_id").saveAsTable("bucketed_users") 💭 When to Use What? Use Partitioning for columns used in filters. Use Bucketing for columns used in joins or aggregations. Combine both for optimal performance in large-scale ETL pipelines. 🔥 Pro Tip: When working with Delta Lake, combining partitioning with Z-Ordering can further speed up queries! #DataEngineering #PySpark #BigData #SparkOptimization #DataPerformance #DeltaLake #AWS #Databricks

If you’re preparing for data engineering interviews, focus on real project-style batch processing scenarios — the kind that test your practical understanding of data pipelines, modeling, and performance optimization. Here are some questions that often come up 👇 1. Write an SQL query to find the second highest salary from an employee table. 2. How do you handle NULL values in SQL joins while ensuring data consistency? 3. Write an SQL query to calculate customer churn rate for the last 6 months. 4. Design a fact table for an e-commerce platform — what dimensions and measures would you include? 5. Explain the difference between star and snowflake schemas, and when to use each. 6. Write a Python script to validate data quality before loading into the warehouse. 7. In PySpark, how do you efficiently join two large DataFrames to prevent skew? 8. Write PySpark code to find the top 3 customers by revenue per region. 9. How do you implement Slowly Changing Dimensions (SCD Type 2) in your batch jobs? 10. Late-arriving data is found during a batch load — how do you maintain historical accuracy? 11. What’s your approach to building incremental loads in Azure Data Factory? 12. Explain how you design the Bronze, Silver, and Gold layers for batch data in Databricks. 13. How do you optimize table design and query performance in Synapse dedicated SQL pool? 14. What best practices do you follow for securing PII data in ADLS or Databricks? 15. Describe your end-to-end batch data architecture — from ingestion to Power BI reporting.

From 6 hours to 70 minutes — tuning PySpark joins for performance A few weeks ago, one of our ETL jobs started failing SLAs. The culprit? A PySpark pipeline joining 1.8 TB of customer transaction data daily. No new framework. No cluster upgrades. Just good old debugging and data understanding. Here’s what I did: 1️⃣ Checked Spark UI → Found that the majority of time was spent in the Shuffle Read stage. 2️⃣ Investigated join logic → Large table joined with a small reference table (but Spark didn’t broadcast it). 3️⃣ Added hints → Used broadcast() for small table (<100MB). 4️⃣ Optimized file reads → Enabled predicate pushdown and column pruning for Parquet sources. 5️⃣ Repartitioned output → Controlled partition size to balance parallelism. 🎯 Result: Runtime dropped from 6 hours → 70 minutes. Cluster cost reduced by ~60%. This experience reinforced something simple yet powerful — “You don’t need more compute, you need more insight.” 💡 Data engineering isn’t about writing code that runs — it’s about writing code that runs efficiently. A bit about me: I’m a Data Engineer passionate about building scalable pipelines using PySpark, AWS, and SQL. Currently exploring Delta Lake and CDC frameworks for real-time data movement. What’s your favorite PySpark optimization trick that made a big difference? #PySpark #DataEngineering #BigData #SparkOptimization #ETL #AWS