Sathiya Govindaraj’s Post

 Welcome to 𝗗𝗮𝘆 𝟭𝟬 of the #45DaysOfDataEngineering Challenge! 🎉 Today, we’re diving into Optimizing PySpark Performance and exploring key concepts that can boost your data processing pipelines: 💡 Understanding 𝗟𝗮𝘇𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 PySpark uses lazy evaluation, meaning transformations are not executed until an action (e.g., count() or show()) is triggered, allowing for optimized query planning and reduced unnecessary computation. 🔑 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 By caching or persisting DataFrames, you store intermediate results in memory or on disk, reducing the cost of repeated actions and speeding up iterative operations. 👉 Check out today’s article for more insights: https://lnkd.in/gaJj85t4 #DataEngineering #PySpark #LazyEvaluation #Caching #Optimization #BigData #DataProcessing

Hyy #People Recently I came across #ApacheSpark and was truly impressed by its powerful functionality and the significant impact it’s making across various industries. In today’s world, we have more data than ever, but it’s hard to find the insights we need. That’s where Apache Spark can help by processing large amounts of data quickly can help turn complex data into useful insights in less time. This inspired me to start a series of #articles where I’ll regularly share insights on Spark—how it works, its key features, and how it’s transforming the way businesses manage and process large-scale data. In this series, We'll explore Spark’s core components such as distributed processing, in-memory computation, and machine learning integrations. These capabilities are enabling faster data processing and smarter decision-making. Whether it's optimizing business analytics or powering real-time data applications, Spark is leading the way in #bigdata innovation. Follow along as we dive into the fascinating world of big data, and feel free to share your thoughts and suggestions!

 Welcome to 𝗗𝗮𝘆 𝟭𝟰 of the #45DaysOfDataEngineering Challenge 🎉 Today, we are exploring 𝗝𝗼𝗶𝗻𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸, one of the most powerful tools for combining datasets in distributed environments. A join operation combines rows from two DataFrames based on a related column or condition. PySpark has several types of joins, each suited to different use cases. 🔍 Key Join Types: 𝗜𝗻𝗻𝗲𝗿 𝗝𝗼𝗶𝗻: Combine matching rows from both datasets. 𝗟𝗲𝗳𝘁 𝗝𝗼𝗶𝗻: left join returns all rows from the left DataFrame, and matches from the right DataFrame where available. Unmatched rows will have NULL. 𝗥𝗶𝗴𝗵𝘁 𝗝𝗼𝗶𝗻: The right join returns all rows from the right DataFrame, and matches from the left DataFrame where available. 𝗙𝘂𝗹𝗹 𝗢𝘂𝘁𝗲𝗿 𝗝𝗼𝗶𝗻: Include all rows from both datasets, fill unmatched with NULL. 𝗦𝗲𝗺𝗶 𝗝𝗼𝗶𝗻: Retain only matching rows from the left dataset. 𝗔𝗻𝘁𝗶 𝗝𝗼𝗶𝗻: Keep non-matching rows from the left dataset. 👉 Explore the full article here: https://lnkd.in/gsNSJ_jT #DataEngineering #PySpark #BigData #Joins ##45DaysOfDataEngineering

🔥 Thrilled to share my latest Medium masterpiece! Join me on a journey through PySpark UDFs, where we'll unlock the secrets to efficient data processing and analysis. 💡 Together, we'll explore real-world examples and actionable insights to spark your data insights! 💥 #pyspark #dataengineering #dataanalysis #apachespark #datascience #analytics #mediumarticle

Hi Network! 🌟 I'm thrilled to share my latest article titled "Understanding Big Data Solutions #1: Apache Spark"! 🌟 In this piece, I dive into the mechanics of Apache Spark, compare it with other big data technologies, and explain where it shines bright. If you're passionate about big data, machine learning, or just improving your tech skills, I'd love to hear your thoughts on it. Feel free to comment and share, and let's drive more insightful discussions around powerful technologies like Spark! 👍 If you like it please give it some claps on Medium. #ApacheSpark #BigData #DataScience #MachineLearning https://lnkd.in/e9wA_Uwj

Shuffling in PySpark 1. What is Shuffling?   - A shuffle operation occurs during wide transformations in Spark. These transformations include operations       like join(), distinct(), groupBy(), and orderBy().   - During shuffling, data needs to be read from a source (either a previous stage or an external data source), processed, and then written back out.   - The performance impact arises from both the map side (where data is processed and written to disk) and the read side (where data is read back from disk and sent across the network between executors). 2. Types of Shuffles:   - Not all shuffles are the same. Here are some common shuffle operations:    - Distinct: Aggregates many records based on one or more keys and reduces duplicates to a single record.    - GroupBy / Count: Aggregates records based on a key and returns the count of that key.    - Join: Combines two datasets by a common key and produces one record for each matching combination.    - CrossJoin: Aggregates each dataset by a common key and produces a record for every possible combination (heavy and expensive). 3. Mitigating Performance Issues Caused by Shuffle:   - Reduce Network IO: Use fewer and larger workers to reduce network data movement. Larger machines and fewer nodes minimize data transfer between machines.   - Avoid Shuffling When Possible:    - Use operations that avoid shuffling, such as reduceByKey() and aggregateByKey(). These combine data locally on each partition before shuffling, reducing network transfers .    - Consider bucketing to pre-shuffle data. Bucketing ensures that keys are co-located, reducing expensive disk and network IO during joins .      - Cache intermediate results to keep them in memory and avoid unnecessary re-shuffling. #databricks #data #sql #dataengineering #deltalake #lakehouse #datawarehousing #optimization #etl

💡 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗦𝗽𝗮𝗿𝗸 𝗝𝗼𝗶𝗻𝘀 𝘄𝗶𝘁𝗵 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 💡 In Apache Spark, joining datasets is a fundamental operation that often requires navigating the challenges of data shuffling. Here’s an example inspired by real-world scenarios: 📂 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀: 1️⃣ 𝗦𝗮𝗹𝗲𝘀 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 (~2GB, 18 partitions): Contains columns like sale_id, sale_date, store_id, and total_amount. 2️⃣ 𝗦𝘁𝗼𝗿𝗲𝘀 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 (~500MB, 4 partitions): Includes store_id, store_name, location, manager, and more. 📌 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼: The task was to join these datasets on the 𝙨𝙩𝙤𝙧𝙚_𝙞𝙙 column to analyze sales by store and region. This required Spark to align rows with the same 𝙨𝙩𝙤𝙧𝙚_𝙞𝙙 from both datasets, which involved significant data shuffling across the network. This join operation resulted in a 𝙘𝙤𝙢𝙥𝙡𝙞𝙘𝙖𝙩𝙚𝙙 𝘿𝘼𝙂 with two stages: • Joins in Spark are 𝙬𝙞𝙙𝙚 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣𝙨 that redistribute data. • Shuffling a larger dataset like the Sales Dataset can lead to network bottlenecks, making performance a key concern. 🛠 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: • Shuffling the larger dataset to align with keys in the smaller dataset. • Managing the additional computation and data transfer required for wide transformations. 💡 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆:  This scenario highlights how 𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙙𝙖𝙩𝙖 𝙙𝙞𝙨𝙩𝙧𝙞𝙗𝙪𝙩𝙞𝙤𝙣 𝙖𝙣𝙙 𝙩𝙝𝙚 𝙣𝙖𝙩𝙪𝙧𝙚 𝙤𝙛 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙖𝙩𝙞𝙤𝙣𝙨 𝙞𝙣 𝙎𝙥𝙖𝙧𝙠 is key to successfully designing scalable solutions. Even with challenges like shuffling, careful planning ensures the smooth execution of data-intensive operations. #BigData #ApacheSpark #DataEngineering #SparkJoins #DataProcessing

Hello LinkedIn community, in today’s data-driven world, organizations are generating vast amounts of data at unprecedented rates. With all this data, the ability to process and analyze it efficiently has become a top priority. That’s where 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 comes in — a powerful tool that bridges the gap between Python’s simplicity and Apache Spark’s big data processing capabilities. PySpark’s optimization features, such as caching and broadcast joins, can drastically reduce execution times for data-heavy operations. Here are a few reasons why PySpark has become an indispensable tool for data engineers: 1️⃣ Scalability 2️⃣ Fast Processing 3️⃣ Seamless Integration with Big Data Ecosystems 4️⃣ Machine Learning and Analytics 5️⃣ Python-Friendly 6️⃣ Resilient Distributed Datasets (RDDs) & DataFrames In a world where data volumes continue to grow, PySpark’s ability to process and analyze large-scale datasets is more important than ever. Whether you're a data engineer or data scientist, mastering PySpark will give you the tools you need to handle the challenges of big data. Please refer the attached Pyspark guide to get more insights on how to handle the data in Pyspark! Guide Credits: Girish Gowda Follow Chitransh Saxena for such curated post on Data Engineering related topics! #DataEngineering #PySpark #BigData #ApacheSpark #MachineLearning #DataScience #DataProcessing #CloudComputing

Direct Acyclic Graph (DAG) in Pyspark: ============================== In PySpark, Directed Acyclic Graphs (DAGs) are the foundation of its data processing model, transforming how we handle large-scale data. Here’s what makes DAGs so powerful: 1. Lazy Evaluation & Optimized Execution PySpark doesn’t execute transformations like .filter(), .map(), or .groupBy() immediately. Instead, it builds a logical DAG of these operations. This DAG is executed only when an action (e.g., .collect(), .count(), .save()) is called, allowing Spark to optimize the entire data flow before running. 2. Stages and Parallelism When an action is triggered, Spark breaks the DAG into stages—group of operations that can be executed in parallel without additional data shuffling. Each stage is processed independently and in parallel across the cluster, optimizing the usage of computing resources and minimizing data transfer, which is key to Spark’s speed. 3. Fault Tolerance with Lineage The DAG tracks the lineage of every transformation. If data is lost due to a node failure, Spark can recompute only the affected partition by replaying the transformations, rather than restarting the entire job. This ensures resilience in handling massive datasets. 4. Smart Resource Management With DAGs, Spark efficiently manages cluster resources by reordering, optimizing, and caching operations when necessary, ensuring only the required computations are executed. Why DAGs Matter for Data Engineers: Efficiency: DAGs allow Spark to plan and streamline jobs for optimal performance, handling large-scale data in less time. Scalability: The breakdown of DAGs into stages supports parallelism, enabling Spark to scale seamlessly across clusters. Resilience: Fault-tolerant data processing is made possible through DAG lineage, a crucial feature in distributed environments. Understanding DAGs is essential for data engineers working with PySpark. It’s the key to unlocking Spark’s full potential for fast, reliable, and scalable big data processing! 🌐 #PySpark #DataEngineering #BigData #DAG

𝗦𝗽𝗮𝗿𝗸 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗦𝗲𝗿𝗶𝗲𝘀 𝗣𝗮𝗿𝘁 𝟯: 𝗦𝗽𝗮𝗿𝗸 𝗮𝗻𝗱 𝗗𝗲𝗹𝘁𝗮 𝗭-𝗢𝗿𝗱𝗲𝗿𝗶𝗻𝗴 𝗨𝗻𝗹𝗲𝗮𝘀𝗵𝗲𝘀 𝗦𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆, 𝗣𝗼𝘄𝗲𝗿 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 After exploring the Catalyst Optimizer in 𝗣𝗮𝗿𝘁 𝟮, we’re elevating our exploration with another transformative technique in Apache Spark: 𝗭-𝗼𝗿𝗱𝗲𝗿𝗶𝗻𝗴. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗖𝗼𝗹𝘂𝗺𝗻 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: We began by pinpointing crucial columns for our queries using the `ZORDER BY` clause, optimizing our data storage and retrieval to focus on the most impactful subsets. This careful planning sets the stage for the next steps. 🔍 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗼𝘂𝘁: Z-ordering restructures data in storage, enabling queries to bypass vast amounts of unnecessary data, significantly speeding up processing times. 🚀 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮-𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴: With Delta Lake’s advanced data-skipping algorithms, we maximize the efficiency of Z-ordering by reducing unnecessary data reads, further enhancing query speed. ⚙️ 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀: Z-ordering is most effective when applied judiciously. We strategically use it at the end of batch jobs on selected non-partition columns to avoid the dilution of its benefits and prevent the creation of small files. 📊 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: This optimized approach allows our Spark setups to transform complex datasets into insightful analytics swiftly, empowering our decision-making processes with robust data support. 🏆 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Explore these in-depth resources to learn more about Z-ordering: 🖼️ Prashant Kumar Pandey Course link. https://lnkd.in/dqb_-MHV 🖼️ Raja's Data Engineering Video link. 🔗https://lnkd.in/diytCbaG 🖼️ Advancing Analytics Video link. 🔗https://lnkd.in/dUZBX7P6 🖼️Databricks 🔗https://lnkd.in/dduNKeM6 🖼️ Delta Lake Website link. 🔗https://delta.io/ Catch up on 𝗣𝗮𝗿𝘁𝘀 𝟭 & 𝟮 if you missed our earlier discussions. Stay tuned for more insights in our Spark Optimization Series. Let’s explore the endless possibilities together! 🌌 Join the conversation and share your experiences below! #DataEngineering #ApacheSpark #Zordering #BigData #Techlogix #DataProcessing #SparkOptimizationSeries