PRAVEEN SINGH 🇮🇳’s Post

🚀 Speed Up Your PySpark Jobs with persist() and unpersist()! ⚡ When working with large datasets in PySpark, one of the best performance hacks is knowing when and how to persist your DataFrames. 🧠 Why Persist? Every time you perform an action in Spark, it may recompute the entire lineage of transformations — which can be time-consuming. By using persist(), you can store the DataFrame in memory (and optionally on disk) so Spark can access it quickly without recomputation. 🧩 Quick Example: df.persist(StorageLevel.MEMORY_AND_DISK) df.unpersist() ✅ Use persist() when: You reuse a DataFrame multiple times in the same job. Recomputing the DataFrame is expensive (like joins or aggregations). ♻️ Use unpersist() when: The dataset is no longer needed. You want to free up cluster memory for other jobs. Small optimizations like this can make a huge difference in performance — especially when working with big data pipelines! 💪 #PySpark #BigData #DataEngineering #ApacheSpark #PerformanceOptimization #DataAnalytics #SparkTips #LearningEveryday #DataScience #DataAnalytics

SQL vs PySpark — Quick Reference for Data Engineers ➡️ Mastering the transition from SQL to PySpark can feel tricky — but it’s all about understanding how SQL operations translate into DataFrame transformations. ✅ Select → df.select() ✅ Filter → df.filter() ✅ Rename → df.withColumnRenamed() ✅ Add column → df.withColumn() ✅ Group & aggregate → df.groupBy().count() ✅ Join → df.join() ✅ Union → df.union() Having this cheat sheet handy will save time while writing cleaner, optimized PySpark code. #PySpark #SQL #DataEngineering #BigData #ApacheSpark #ETL #DataFrame #SparkSQL #DataEngineer #Learning #CheatSheet #Databricks

SQL vs PySpark — Quick Reference for Data Engineers ➡️ Mastering the transition from SQL to PySpark can feel tricky — but it’s all about understanding how SQL operations translate into DataFrame transformations. ✅ Select → df.select() ✅ Filter → df.filter() ✅ Rename → df.withColumnRenamed() ✅ Add column → df.withColumn() ✅ Group & aggregate → df.groupBy().count() ✅ Join → df.join() ✅ Union → df.union() Having this cheat sheet handy will save time while writing cleaner, optimized PySpark code. #PySpark #SQL #DataEngineering #BigData #ApacheSpark #ETL #DataFrame #SparkSQL #DataEngineer #Learning #CheatSheet #Databricks

Bridging SQL and PySpark for Data Engineers! Whether you’re transitioning from traditional SQL workflows to big data processing with PySpark, having a quick reference of equivalent commands can save you tons of time and confusion. I’ve put together a simple table comparing the most common SQL operations with their PySpark DataFrame counterparts — perfect for daily use, debugging, and learning. Master both worlds to streamline your data pipelines and analytics workflows! #DataEngineering #PySpark #SQL #BigData #Spark #DataScience #TechTips #DataEngineerTools

🚀 Master PySpark Like a Pro! If you're stepping into the world of Data Engineering, mastering PySpark is an absolute must. From DataFrame operations to SQL queries and Window functions, this cheat sheet by Abhishek Agrawal packs everything you need to build efficient and scalable data pipelines. 💡 Key Highlights: ✅ DataFrame creation, joins & transformations ✅ SQL integration using Spark SQL ✅ Window functions (rank, lead, lag, etc.) ✅ RDD operations for advanced use cases ✅ Optimization tips for faster PySpark jobs Whether you're just starting out or looking to optimize your Spark workflows — this is your go-to quick reference. 📘 Resource: PySpark Cheat Sheet for Data Engineers 👨💻 Author: Abhishek Agrawal | Data Engineer #PySpark #DataEngineering #BigData #Spark #DataAnalytics #DataScience #MachineLearning #ETL #DataPipeline #CheatSheet

🔥 𝐀 𝐆𝐞𝐧𝐭𝐥𝐞 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠 𝐢𝐧 𝐒𝐩𝐚𝐫𝐤 Data processing tools like Pandas can handle small datasets efficiently, but when it comes to big data, problems arise: 💾 1️⃣ The data does not fit into the machine’s memory. 🐢 2️⃣ Even if it somehow fits, accessing and processing the data becomes much slower compared to big data processing tools like Apache Spark. 🚀 Spark solves these problems by splitting large datasets into smaller datasets. Each smaller dataset is processed by a specific executor’s core — and in this way, multiple smaller datasets are processed in parallel, depending on the availability of executors and cores in each executor. ✍️ The process of splitting a large dataset into smaller datasets is known as data partitioning, and each smaller dataset is called a data partition. ⚙️ Maximum data partitions processed in parallel = number of executors in a cluster × number of cores in each executor 🧩 The optimal number of data partitions depends on the data being partitioned, cluster configurations, and Spark configurations. 🗂️ Spark performs data partitioning automatically when creating an RDD or DataFrame, but these default partitions can be overridden using explicit APIs like repartition, coalesce, or custom partitioners. #Spark #Big_Data #Data_Engineering

🚀 Bridging SQL and PySpark – Simplified! As data engineers, we often move between SQL and PySpark when building ETL pipelines or optimizing queries at scale. To make this transition smoother, I’ve created a simple and spreadsheet-style reference that maps common SQL operations to their PySpark equivalents. This quick guide covers: ✅ Database & table operations ✅ Data types and schema handling ✅ Filtering, joins, aggregations, and window functions ✅ File-based operations (Parquet, Delta, JSON, CSV, ORC) ✅ Conditional logic and performance-friendly transformations Just to add 2 difference for where and having in SQL with PySpark equivalent it’s missing in Attached sheet. Where filters rows before grouping — .filter() before .groupBy() Having filters after aggregation — .filter() after .agg() Whether you’re moving from SQL to PySpark or working in a hybrid environment, this equivalence sheet makes it easy to translate your logic across both worlds. 📘 #DataEngineering #ETL #SQL #PySpark #BigData #Spark #DataFrame #Analytics

🚀 Master PySpark Like a Pro! Whether you’re building massive data pipelines or optimizing ETL jobs, PySpark is a must-have skill for every Data Engineer. Here’s a quick rundown of what every PySpark pro should know 👇 🔥 Core Commands `df.show()` → Quickly inspect your data `df.select()` → Pick only the columns you need `df.filter()` → Slice data with conditions `df.groupBy()` → Summarize insights by category 🧩 Joins & Transformations Combine DataFrames efficiently using `join()` Repartition smartly for performance using `repartition()` Drop duplicates and clean data with ease ⚡ Optimization Tips ✅ Cache frequently used DataFrames ✅ Broadcast small tables for faster joins ✅ Be cautious with wide transformations like `groupBy()` ✅ Use Delta Lake for reliability and version control 💡 Small tweaks → Massive speedups in your Spark jobs! If you’re a data engineer or aspiring to be one, mastering these fundamentals will make your pipelines both efficient and scalable. #PySpark #DataEngineering #BigData #Spark #ETL #DataEngineer

Duplicate data flooding your data pipeline? Luckily, SQL’s ROW_NUMBER() can be that "Flex Tape" solution you need to quickly identify and handle duplicate data. Duplicate rows pouring into your pipelines, reports breaking, and queries grinding to a halt. 🔧 How does it help? By assigning a unique number to rows within partitions of your data, you can: 1. Identify duplicates 2. Retain only the "first occurrence" 3. Clean up your dataset and keep your data healthy Here’s an example: WITH RankedData AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY column_you_want_to_deduplicate ORDER BY created_at) AS row_num FROM your_table ) SELECT * FROM RankedData WHERE row_num = 1; How do you handle duplicate data in your pipelines? Share your favourite tricks below! 👇 #DataEngineer #DataScientist #MLEngineer #DataRoles #CareerDevelopment #DataScience #MachineLearning #TechSkills #CareerGrowth #Databricks #Azure #TechJobs #Datalake #DatawareHouse #DeltaLake #SQL #Python

🚀 Week 5 in PySpark: Diving into Higher-Level APIs & Spark SQL This week’s journey through PySpark covered some powerful concepts that elevate how we work with big data: 🔹 DataFrames Explored how PySpark DataFrames offer a high-level abstraction for structured data, enabling SQL-like operations with scalability and speed. 🔹 Managed vs External Tables Learned the difference between tables stored inside Spark’s warehouse (managed) vs those linked to external storage systems (external), and when to use each. 🔹 DataFrame Optimizations Covered techniques like predicate pushdown, caching, and partitioning to make DataFrame operations more efficient. 🔹 Spark SQL Used SQL queries directly on DataFrames and tables, bridging the gap between data engineering and analytics. 🔹 Spark Executors Understood how executors handle tasks in a distributed environment, and how tuning them impacts performance. Each topic builds toward mastering scalable data processing with PySpark. Looking forward to applying these in real-world scenarios! #PySpark #BigData #DataEngineering #SparkSQL #DataFrames #LinkedInLearning #Week5 #Trendytech