Aditya Chandak

🌟𝐖𝐞'𝐫𝐞 𝐇𝐢𝐫𝐢𝐧𝐠: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈 + 𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐬𝐞𝐚𝐫𝐜𝐡 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬! TekPillar®🌟 🔧𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 : Python API+ Elastic Search 📍𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧 : Bangalore  📅𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 : 5+ Years      #KeyResponsibilities: 👉Develop custom search and analytics applications using FastAPI and Elasticsearch. 👉Index data from various sources, including big data systems like Databricks and Snowflake. 👉Query Elasticsearch data and create robust REST APIs. Implement AI/ML use cases and optimize Elasticsearch performance. 👉Create reusable libraries for index templates, custom analyzers, and scripts. 👉Collaborate with data engineers on pipelines and ETL jobs. 👉Guide developers on search, filtering, and aggregation requirements. 👉Research and prototype new Elasticsearch features. 🔗 𝐀𝐩𝐩𝐥𝐲 𝐇𝐞𝐫𝐞:  📞8799447189 | ✉️ [email protected] #Python #Elasticsearch #FastAPI #BigData #Azure #TechJobs #Bangalore #Hiring #tekpillarjobs

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Day 12 - 30-Day Data Engineering Interview Preparation Series PySpark (#Day12): 20 PySpark interview questions that can be relevant for a data engineering role 👇 📍 What is PySpark, and how does it differ from Apache Spark? 📍 Explain the architecture of Spark. 📍 How do you initialize a SparkSession in PySpark? 📍 What are RDDs in PySpark, and how do they work? 📍 What are the transformations and actions available in RDDs? 📍 How do you create RDDs in PySpark? 📍 What are DataFrames in PySpark, and how do they differ from RDDs? 📍 How do you perform operations on DataFrames? 📍 Explain the concept of a Dataset in PySpark. 📍 How do you work with SparkSQL in PySpark? 📍 Explain how to perform SQL operations on DataFrames. 📍 What are the advantages of using SparkSQL? 📍 How do you read and write data in various formats (CSV, JSON, Parquet) using PySpark? 📍 Explain the process of handling schema evolution in PySpark. 📍 What is Catalyst Optimizer in PySpark? 📍 How do you optimize a PySpark application? 📍 Explain the concept of lazy evaluation in PySpark. 📍 What is the significance of caching and persistence in PySpark? 📍 How do you handle skewed data in PySpark? 📍 What are broadcast variables and accumulators in PySpark? 🚨 We have launched our most affordable and industry-oriented "Complete Data Engineering 3.0 With Azure" bootcamp and ADMISSIONS ARE OPEN! 🔥 Only 7 days left and 80 seats remaining! 🚀 Enrollment link is in the first comment 👇 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar 🙂 #DataEngineering #SQL #Hive #Hadoop #Kafka #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure

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💡 Understanding Garbage Collection in Apache Spark: Managing Memory Like a Pro 💡 One key factor that can make or break your Spark job is memory management. Specifically, let's talk about Garbage Collection (GC) in Spark and how to avoid memory pitfalls like memory leaks that can lead to job failures. 🚨 What is Garbage Collection in Spark? Garbage Collection is the process by which the Java Virtual Machine (JVM) reclaims memory that’s no longer being used by your Spark applications. It kicks in when memory usage grows too high, trying to clear out space for new data. However, in Spark’s distributed environment, inefficient GC can cause long pauses and drastically slow down performance. 🚀 What Causes Excessive GC or Memory Leaks? Too many small objects: Spark works on RDDs/DataFrames that may contain millions of small objects. If these are not properly released, they stay in memory longer than needed, triggering frequent garbage collection. Large shuffles: When wide transformations (like groupByKey() or join()) occur, Spark has to shuffle data across partitions, consuming a lot of memory and causing excessive garbage collection. Long-living objects: Variables held in memory (like broadcast variables or accumulators) can also contribute to memory leaks if not cleaned up after their usage. Inefficient caching: If RDDs/DataFrames are cached in memory but not properly managed (or not cleared when no longer needed), they can hog memory space unnecessarily. 🔥 Best Practices to Manage Memory and Avoid GC Issues 🔥 Restart your cluster occasionally to clear out hidden cached objects that refuse to exit memory Write optimized code and leverage Photon Engine in Databricks Avoid memory sapping objects like pandas dataframe when working with large data in Spark Use serialization efficiently: Opt for Kryo serialization, which is faster and more memory-efficient than Java serialization. Tune your GC settings: You can customize the JVM's garbage collection settings (e.g., using the G1 garbage collector) and adjust memory allocation to make GC more efficient for your specific workload. Use persist() smartly: When caching RDDs or DataFrames, specify the right storage level (e.g., MEMORY_ONLY, MEMORY_AND_DISK). Ensure that cached data is released when not needed using unpersist(). Avoid large shuffles: Try to reduce wide transformations and repartition data efficiently to prevent excessive shuffling, which can spike memory usage. Monitor and Optimize: Leverage tools like the Spark UI and metrics like GC time to identify when memory pressure is too high and optimize accordingly.

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What is SparkSession and Sparkcontext? SparkContext:- Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. Syntax for SparkContext: from pyspark import SparkContext --Create a SparkContext object with local mode and specifying the number of worker threads sc = SparkContext("local[*]", "example") --Now you can use 'sc' to create RDDs and perform operations on them SparkSession:- It’s a unified object to perform all the Spark operations. In the earlier version of the Spark 1.x there were separate objects like SparkContext, SQLContext, HiveContext, SparkConf, and StreamingContext. However with Spark 2.x all these different objects combine into one i.e. the SparkSession. You can perform all those operations using the SparkSession object itself. This unison of all the objects has made life simpler for the Spark Developers. Syntax for SparkSession from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .appName("Name") \ .getOrCreate() Why should use SparkSession over SparkContext? from Spark 2.0, SparkSession provides a common entry point for a Spark application. Instead of SparkContext, HiveContext, SQLContext, everything is now within a SparkSession. it unifies all of sparks numerous contexts. before version 2.0 need to create separate context per JVM. however with SparkSession this problem has been resolved. Check out this databricks interview questions course: https://lnkd.in/dCjUuEGe

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How to copy data between two or more Hadoop clusters? 1. DistCp Version 2 (distributed copy) is a tool used for large inter/intra-cluster copying. 2. It uses MapReduce to effect its distribution, error handling and recovery, and reporting. 3. It expands a list of files and directories into input to map tasks, each of which will copy a partition of the files specified in the source list. Copy From one source hadoop cluster to target hadoop cluster: hadoop distcp hdfs://NN1:8020/data/source1/ \ hdfs://NN2:8020/data/target/ Copy From Multiple source hadoop clusters to target hadoop cluster: hadoop distcp hdfs://NN1:8020/data/source1/ \ hdfs://NN2:8020/data/source2/ \ hdfs://NN3:8020/data/target/ update is used to copy files from source that don’t exist at the target or differ from the target version distcp -update hdfs://NN1:8020/source/first \ hdfs://NN1:8020/source/second \ hdfs://NN2:8020/target   overwrite overwrites target-files that exist at the target distcp -overwrite hdfs://NN1:8020/source/first \ hdfs://NN1:8020/source/second \ hdfs://NN2:8020/target for more in depth understanding of distCp architecture : https://lnkd.in/gCmc2f2b #BigData, #Hadoop, #MapReduce, #Spark, #DataEngineering, #SoftwareEngineering, #trendytech

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Hadoop vs Spark! Interviewer: There's a common belief that Hadoop has been replaced by Spark. Can you explain why this is a misconception? Candidate: This is a misconception because Hadoop and Spark serve different purposes within the big data ecosystem. Hadoop is an ecosystem that includes HDFS (storage), YARN (resource management), and MapReduce (processing). Spark is primarily a processing engine that can run on top of Hadoop's HDFS and YARN. While Spark has largely replaced Hadoop's MapReduce due to its faster in-memory processing capabilities, it has not replaced Hadoop's storage and resource management components. Interviewer: So, Spark complements rather than replaces Hadoop? Candidate: Exactly. Spark is designed to complement Hadoop by providing a more efficient and versatile processing engine. It can utilize Hadoop's HDFS for distributed storage and YARN for resource management, making them work together seamlessly. Interviewer: Can you elaborate on the technical differences between Hadoop MapReduce and Spark that led to Spark's popularity? Candidate: Certainly. The primary technical differences include: Performance: Spark performs in-memory computations, which makes it significantly faster than Hadoop MapReduce, which relies on disk-based processing. Ease of Use: Spark offers high-level APIs in Java, Scala, Python, and R, making it easier for developers to write and maintain code compared to the more complex Java code required for MapReduce. Unified Framework: Spark provides a unified framework for batch processing, real-time streaming, machine learning, and graph processing, whereas MapReduce is limited to batch processing. These advantages make Spark more suitable for a variety of data processing tasks, leading to its increased adoption over Hadoop MapReduce. Interviewer: What considerations should an organization take into account when transitioning from Hadoop MapReduce to Spark? Candidate: When transitioning from Hadoop MapReduce to Spark, organizations should consider the following: Compatibility: Ensure the existing Hadoop cluster is compatible with Spark, typically achieved through distributions like Cloudera or Hortonworks. Training: Provide adequate training for developers and data engineers to become proficient in Spark. Migration Planning: Develop a detailed migration plan, including testing and validation of Spark jobs to ensure they meet performance and accuracy requirements. Resource Management: Adjust resource allocation and configurations in YARN to optimize for Spark’s in-memory processing model. Gradual Transition: Start with less critical workloads to gain confidence before migrating more critical processes. By considering these factors, organizations can effectively transition to Spark while maintaining the benefits of the Hadoop ecosystem. #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis

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Hadoop vs Spark! Interviewer: There's a common belief that Hadoop has been replaced by Spark. Can you explain why this is a misconception? Candidate: This is a misconception because Hadoop and Spark serve different purposes within the big data ecosystem. Hadoop is an ecosystem that includes HDFS (storage), YARN (resource management), and MapReduce (processing). Spark is primarily a processing engine that can run on top of Hadoop's HDFS and YARN. While Spark has largely replaced Hadoop's MapReduce due to its faster in-memory processing capabilities, it has not replaced Hadoop's storage and resource management components. Interviewer: So, Spark complements rather than replaces Hadoop? Candidate: Exactly. Spark is designed to complement Hadoop by providing a more efficient and versatile processing engine. It can utilize Hadoop's HDFS for distributed storage and YARN for resource management, making them work together seamlessly. Interviewer: Can you elaborate on the technical differences between Hadoop MapReduce and Spark that led to Spark's popularity? Candidate: Certainly. The primary technical differences include: Performance: Spark performs in-memory computations, which makes it significantly faster than Hadoop MapReduce, which relies on disk-based processing. Ease of Use: Spark offers high-level APIs in Java, Scala, Python, and R, making it easier for developers to write and maintain code compared to the more complex Java code required for MapReduce. Unified Framework: Spark provides a unified framework for batch processing, real-time streaming, machine learning, and graph processing, whereas MapReduce is limited to batch processing. These advantages make Spark more suitable for a variety of data processing tasks, leading to its increased adoption over Hadoop MapReduce. Interviewer: What considerations should an organization take into account when transitioning from Hadoop MapReduce to Spark? Candidate: When transitioning from Hadoop MapReduce to Spark, organizations should consider the following: Compatibility: Ensure the existing Hadoop cluster is compatible with Spark, typically achieved through distributions like Cloudera or Hortonworks. Training: Provide adequate training for developers and data engineers to become proficient in Spark. Migration Planning: Develop a detailed migration plan, including testing and validation of Spark jobs to ensure they meet performance and accuracy requirements. Resource Management: Adjust resource allocation and configurations in YARN to optimize for Spark’s in-memory processing model. Gradual Transition: Start with less critical workloads to gain confidence before migrating more critical processes. By considering these factors, organizations can effectively transition to Spark while maintaining the benefits of the Hadoop ecosystem. #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis

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Broadcast Join in PySpark 🈴 ================== A broadcast join sends the smaller table (or DataFrame) to all worker nodes, ensuring each worker node has a complete copy of the smaller table in memory.💯 A more detailed explanation of a broadcast join: 👇👇 1️⃣The large DataFrame is the one that contains a substantial amount of data. 2️⃣The small DataFrame is the one with significantly fewer rows or a smaller size. Gains because of a broadcast join: ❇️ 1️⃣Since the smaller DataFrame is available on each node, the join operation can be performed locally on each node without the need for extensive data movement. 2️⃣This reduces the amount of network traffic and can significantly improve the performance of the join, especially when the small DataFrame fits in the memory of each node. Tags: 🏛️ #data #dataengineering #databricks #dataengineerjobs #python #pyspark #bigdata #hadoopadmin #scala #dataanalytics #snowflake #snowflakedevelopers #datacloud #aws #awscloud #kafka #knowledgesharing #dailypost #datascience #machinelearning #AWS

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Week 10 of the "Ultimate Big Data Masters Program" provided an insightful journey into Apache Spark Optimizations and Performance Tuning - 1, provided by Sumit Mittal Sir. Here's a summary of Week 10 key learnings: ·       Pyspark Optimizations – Internals of groupBy ·       Normal Join Vs Broadcast Join ·       Different types of Joins ·       Partition Skew ·       Adaptive Query Execution (AQE) ·       Join Strategies ·       Optimizing Join of 2 large tables – Bucketing Thank you Sumit Mittal Sir and TrendyTech for all the support and guidance whenever needed. #Bigdata #Spark #Python #DAG #Join #dataengineer #AWS #Azure #ADLS #HDFS #Linux #SQL #athena #redshift

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**𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐤𝐢𝐥𝐥𝐬 𝐰𝐢𝐭𝐡 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠!** A huge shoutout to my friends, Vishal Kaushal , a Snowflake Squad member, and Sachin Mittal, a 4x Snowflake Data Superhero, who are starting Snowflake Training: Zero to Hero course starting May 18th! . **Why Snowflake? ** Snowflake is more than just a data warehouse. It's a powerful, cloud-native data platform that empowers data engineers to build next-generation data solutions. Here's why learning Snowflake can be a game-changer for your career. By mastering Snowflake, you'll equip yourself with the skills to design and implement robust, scalable data solutions that are in high demand across industries. This positions you as a valuable asset in the ever-evolving world of data engineering. This comprehensive training, will equip you with the knowledge and skills to become a Snowflake master. Here's what you'll gain: Solid Foundation: Grasp the core concepts and functionalities of Snowflake. Hands-on Learning: Get practical experience through real-world exercises and projects. Unlimited doubt support Real time Projects : 2 End to End Real time projects , 1 using Matillion as well. Ready to take your data engineering skills to the next level? Don't miss out on this amazing opportunity! Sign up for the Snowflake Training: Zero to Hero course starting May 18th. 👉🏻 Enroll Here - https://lnkd.in/gcfxzNx4 📅 Live classes starts from 18th May 2024 Discount Coupon : LIVE20 PS : This is not a paid post. #Snowflake #DataEngineering #CloudDataWarehouse #Training #Upskilling #DataSuperhero

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Power BI in Hindi- DAX Function: RANK - How It Differs from RANKX| How to break ties: https://lnkd.in/gkce99ti #powerbiinterview #PowerBI #dax #powerquery #AmitChandakDA #businessintelligence #PL300 #powerplatform #bi #powerbidesktop #datatodiscovery #powerbitutorial

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After my Databricks and PySpark free Youtube course. I'm launching Databricks Certified Data Engineer - Zero to Hero paid course. You might be confused what will be covered in this course? Will I get job after enrolling in this course? Answer is You can if you follow all videos step by step I will covering everything which is required to be Databricks Data Engineer ✅ Introduction of Spark ✅ Introduction of Databricks ✅ Read Multiple files like CSV, PDF, Parquet, etc using PySpark ✅ Manipulate the data using Pyspark - Sort, Add columns, Delete rows ✅ Delta Lake feature ✅ Optimisation in Spark - Cache, Salting, Broadcast,etc ✅ Unity Catalog ✅ DeltaLive Tables ✅ Batch vs Streaming ✅ Utilities and Framework ✅ GenAI Minor Project ✅ Deployments Still I'm making videos and trying to provide you all videos ASAP but it will take time. Fees is very very affordable and it includes everything from zero to hero Check out this course link here: https://lnkd.in/daGsXPFb Promo code: DBX15 to get 10% off (Early discount)

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Launching Databricks Certified Data Engineer - Zero to Hero course on GeekCoders I yet to upload the videos in the course (ETA 15-June-2024) Here is the TOC: 1. Spark Introduction 2. Databricks Setup and Introduction 3. Read different types of files in Spark 4. Manipulate data using PySpark 5. Dbutils commands 6. Spark Optimisation methods 7. Delta lake feature 8. Utilities and Framework 9. Unity Catalog 10. Delta Live Tables 11. LLM - GenAI POC No one will give you everything in one course at very affordable price. Check out full video here and feel free to enroll https://lnkd.in/d76vWhGN Here is the link to the course https://lnkd.in/daGsXPFb Note: Share this post if you liked my free Databricks and Playlist course 😄 hashtag #databricks hashtag #azure hashtag #dataengineer

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Today's Learning ✌️ While going through one PBIX file. I saw that data was more than 5 Million Rows in Fact Table where Import Mode was used as Connectivity Mode. Snowflake Schema was used. But PBIX file was only of 25 MB. How Come? 🤔🤔🤔 Reason: ⬆️ 1) Data was Fetched of only last 6 months not all ✅ 2) Unwanted Columns where removed from Table ✅ 3) Grouping was done ✅ It looks very simple but Very Important observation real time work perspective 👍 Hope this helps 🙏 #powerbi #powerbideveloper

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Leveraging Snowflake Time Travel in a Real-Time Data Pipeline 🚀: 1)🚆Our data pipeline job runs 6 times a day, extracting and loading data from multiple collections into Snowflake sequentially. 2)A config table drives the consecutive runs of these collections and tracks the status of each collection's data load. 3)📁 Post-loading, one more job is triggered which stores the log into S3. 4)One morning, I noticed that the overnight data pipeline job had failed. 5)🔍 Upon investigation, I couldn't find the log for that specific run. 6)✅ Checked the config table. The most recent job had completed without issues, all collections were marked as successfully loaded. 7)⏳ I then utilized Snowflake's Time Travel feature to retrieve the config table data just after the failed job's timestamp. 8)This revealed that one of the collections had loaded a huge amount of data, causing a timeout. This prevented the job of log transfer to s3 from running. 9)🛠️ Thanks to Time Travel, we identified the issue quickly and took corrective action to prevent future occurrences. Snowflake Time Travel proved invaluable in diagnosing and resolving this critical issue, showcasing its importance in maintaining robust data pipeline operations. 🔄✨ https://lnkd.in/gAY93mtC #data #snowflake #dataengineering #elt #datascience

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Wrapping up Milestone 1 with Week 15, a deep dive into “ Apache Hive “ provided by Sumit Mittal Sir and it's been an incredible journey so far! Here are some of the important topics covered: ·       Introduction to Apache Hive ·       Apache Hive Tables – Managed & External ·       Hive Optimizations – Partitioning & Bucketing ·       Hive Join Optimizations ·       Hive Transactional tables | ACID Properties ·       Insert only Transactional tables with ACID Properties ·       Spark-Hive Integration ·       Hive MSCK Repair       Thanks to Sumit Mittal sir and TrendyTech team for this learning experience. #Hive #Spark #BIGDATA #LearningJourney #TechInnovation #DataEngineering

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🔥Top Hadoop Questions Answered 🔍 👷️ As a data engineer, you need to stay updated with the latest trends and challenges in big data. This blog post tackles some of the most popular questions in big data engineering today! 💪 What are the primary challenges in big data engineering? 1. 📈 Data Volume: Huge data generation poses challenges in storage, processing, and analysis. 2. 🌐 Data Variety: Diverse data types and structures require specialized tools for efficient management. 3. 🏃️ Data Velocity: Quick data generation demands real-time processing capabilities. 🧑‍💻 What skills are essential for big data engineers? 1. 💻 Proficiency in languages like Python, Java, or Scala 2. 🛠️ Understanding of big data technologies like Hadoop, Spark, and NoSQL databases 3. 🛠️ Experience with data processing tools like Apache Hive, Pig, and Kafka 4. 🔑 Strong grasp of data warehousing, ETL processes, and data modeling ☁️ What's the role of cloud computing in big data engineering? Cloud computing provides scalable and cost-effective infrastructure for big data management. Major cloud providers like AWS, Azure, and GCP offer big data services to manage data workloads efficiently. 🔒 How important is data privacy and security in big data engineering? Data privacy and security are crucial in big data engineering. Adhering to regulations and implementing robust security measures protect sensitive data and maintain trust. 📡 What's the future of big data engineering? Big data engineering's future will likely see increased adoption of AI and machine learning for advanced analytics, real-time processing, and decision-making. Cloud computing and IoT growth will also drive innovation in big data engineering. 📚 By staying informed about these questions, data professionals can excel in this rapidly evolving field! Good luck with your preparation! 🤞 ➕ Do Follow Dolly P. For more Data Engineering content #dataengineering #followme #pyspark #InterviewPrep #TechCareer #DataJobs #interviewskills #KnowledgeSharing #TechSkills #interview #ETL #dataengineerjobs #LearningResources #sql #cloudcomputing #interview #databricks #azuresynapse #azuredatabricks #azure #azuredataengineer #spark #sparksql  #azuredatafactory #deltalake #deltatable #connections #interviewtips #dataengineers #interviewsuccess #azurecloud #datacleaning #pyspark #optimization #BigData #azuredataengineer

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Glad to hear 🤟 My Spark Streaming playlist on YouTube just helped someone to crack his JOB INTERVIEW 🤩 Congratulations Ravi Prakash 🏆 If you are reading this post, please tag yourself in the comments 😄 If you want to learn PySpark or Spark Streaming from basics to advanced Performance Optimization, check out the links in comments 👇 #spark #streaming #dataengineering

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"Learning # 25 : Ways to Access Columns in PySpark 🚀📊 In my journey of mastering Big Data Engineering, I've been exploring Apache Spark and its DataFrame operations. Today, I want to share some useful ways to access columns in PySpark: 1️⃣ Column String 📝: orders_df.select("order_id","order_date").show() 2️⃣ Column Object 🧩: col("cust_id") or column('order_id') 3️⃣ Column Index 🔢: orders_df['order_date'] or orders_df.order_date 4️⃣ Column Expression 🎭: expr("order_status") or selectExpr("order_id + 1 as new_order_id") These methods come in handy when joining two DataFrames with the same column names. 🔄 For instance, using the col() function, we can filter rows in two ways: DataFrame way 🗂️: orders_df.select("order_id",orders_df.order_date,orders_df['order_date'],column('order_id'),col('order_id'),expr("order_status")).where(col("order_status").like ("PENDING%")).show() SQL way 📊: orders_df.select("order_id",orders_df.order_date, orders_df['order_date'],column('order_id'),col('order_id'),expr("order_status")).where("order_status like 'PENDING%' ").show() Big thanks to Sumit Mittal and TrendyTech for the insightful lessons! #bigdata #apachespark #dataengineering #Technology #data

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