Shashank Mishra 🇮🇳

Real-Time Indexing at Scale: How Meesho Leveraged Apache Flink 🚀 At Meesho, they support millions of entrepreneurs across Bharat. With over 50 million products in their catalog, keeping product data fresh in real-time is essential. Delays in indexing lead to users seeing outdated prices or out-of-stock items, causing frustration, cart abandonment, and potential customer loss 😯 Here's how we overhauled our indexing architecture using Apache Flink for horizontal scalability 👇🏻 They started with a crontab-based approach, scheduling indexing jobs every 10 minutes. It worked well initially, with indexing taking 5–7 minutes for our smaller product base 🕑 The Scaling Challenge 📈 As Meesho grew, so did their problems 👇🏻 1️⃣ Exponential Growth: The number of products exploded into tens of millions, pushing indexing time to 4 hours. 2️⃣ Data Lag: Users saw outdated prices and stock information, especially during peak sales. 3️⃣ Processing Inefficiency: Recomputing all product data—even if unchanged—added unnecessary load. 4️⃣ Failure Delays: Job failures caused delays of up to 8 hours. Interim Fix: Kafka Consumers ✅ While plotting a long-term solution, they implemented Kafka consumers to 👇🏻 1️⃣ Parallelize Elasticsearch Writes: This reduced ES write time from 2 hours to 15 minutes, cutting overall indexing time to ~3 hours. 2️⃣ Adopt Batch Processing: Catalogs were sent directly to Kafka for immediate indexing, reducing memory consumption. Though this helped temporarily, as their catalog grew, indexing time soon crept back up to 4 hours. Requirements for the New Solution ✍🏻 They needed a system that was 👇🏻 1️⃣ Low-latency & fault-tolerant: Processing events in seconds, not hours. 2️⃣ Decoupled: Separating indexing from the enrichment pipeline. 3️⃣ Extensible: Supporting multi-consumer queues and future data flow operations. Why They Chose Apache Flink ❓ After evaluating several options, Apache Flink stood out for 👇🏻 1️⃣ Exactly-once processing guarantees: Ensuring data consistency. 2️⃣ Very low latency: Even with fault tolerance enabled. 3️⃣ High throughput: Capable of processing 182 million records/sec across 30 nodes. Flink vs. Storm: Performance Benchmark 😎 1️⃣ Flink (no fault tolerance): 182M records/sec, 0 ms latency. 2️⃣ Flink (with 5 sec checkpoints): 178M records/sec, 0 ms latency. 3️⃣ Storm (no fault tolerance): 9.8M records/sec, 11 ms latency. 4️⃣ Storm (with fault tolerance): 0.57M records/sec, 30–120 ms latency. 5️⃣ Flink had 300× higher throughput than Storm, even with fault tolerance. ✅ Read Full blog - https://lnkd.in/gQS_WyQt 🚨 Join my high quality, industry & modern tech stack based and practical project driven Data Engineering BootCAMP✌🏻👇 👉 Enroll Here - https://bit.ly/4eA2tuX 🚀 Dedicated Doubt Support & Placement Assistance 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills 🙂 #dataengineering

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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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Spark Streaming!! Interviewer: "Today, let's explore Spark Streaming, an integral component of Apache Spark for processing real-time streaming data. Are you familiar with Spark Streaming?" Candidate: "Yes, I've worked with Spark Streaming. It provides a scalable and fault-tolerant framework for processing real-time data streams." Interviewer: "Excellent! Spark Streaming enables developers to build robust and scalable stream processing applications. Could you elaborate on the key features and benefits of Spark Streaming?" Candidate: "Certainly! Spark Streaming offers several advantages. Firstly, it provides high throughput and low latency processing of real-time data streams. Secondly, it integrates seamlessly with the core Spark API, enabling users to leverage Spark's rich ecosystem of libraries and tools. Additionally, Spark Streaming offers fault tolerance through lineage and checkpointing, ensuring that data processing is resilient to failures. Lastly, it supports various data sources and sinks, allowing users to ingest data from sources like Kafka, Flume, or HDFS, and output processed data to databases, file systems, or dashboards." Interviewer: "Well summarized! Now, let's delve into the architecture of Spark Streaming. How does Spark Streaming process and analyze real-time data streams?" Candidate: "Spark Streaming follows a micro-batch processing model, where real-time data streams are divided into small batches of data, which are then processed using Spark's batch processing engine. The architecture consists of a Receiver that ingests data from input sources, a StreamingContext that manages the execution of streaming jobs, and a Discretized Stream (DStream) abstraction that represents a continuous stream of data. Each batch of data is processed using RDDs (Resilient Distributed Datasets), enabling parallel and fault-tolerant stream processing." Interviewer: "Spot on! Spark Streaming's micro-batch processing model provides fault tolerance and scalability, making it suitable for handling large-scale streaming data. Now, let's discuss the use cases of Spark Streaming. Can you provide examples of scenarios where Spark Streaming excels?" Candidate: "Certainly! Spark Streaming is well-suited for various real-time analytics and monitoring applications. For example, in e-commerce, it can be used to analyze user activity and recommend products in real-time. In finance, it can process streaming market data to detect anomalies or fraud in transactions. Additionally, in telecommunications, it can analyze network logs to identify network issues or optimize network performance in real-time." Interviewer: "Impressive examples! Spark Streaming's versatility makes it applicable across different industries for real-time data processing and analysis. Thank you for sharing your insights on Spark Streaming!" Candidate: "You're welcome! #bigdata #spark #sparkstreaming #pyspark #python

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Speaking at #DES24, Pavan Nanjundaiah, Head – Tredence Studio, said today’s data engineer needs a beginner’s mindset to adapt to the growing change. “Today’s data engineers should expand left and right. Focus on your core foundational skills because they are not going to change. You don’t need to handcraft your code anymore.   Understand what is the business problem you’re trying to solve. My recommendation to data engineers is to shift left, which is the person who gave you the requirement and probably understands the business a little better. By doing this, you’re now zooming out of a core data engineer’s profile and understanding the business and the longer impact you can create.”   Read more here: https://lnkd.in/gKhtBwp6 #DataEngineers #Tredence #BeyondPossible

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Comprehensive Interview Questions on Apache Hive, Spark, Kafka, Airflow, Hudi, Flink, Iceberg 🎯 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝗶𝘃𝗲 - What are the differences between internal and external tables in Hive, and when would you use each? - How do partitioning and bucketing improve query performance in Hive? - What are optimized joins in Hive, and how can you improve the efficiency of large table joins? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 - What is the difference between transformations and actions in Spark, and can you provide an example? - How can data partitioning be optimized for performance in Spark? - What is the difference between cache() and persist() in Spark, and when would you use each? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 - How does Kafka partitioning enable scalability and load balancing? - How does Kafka’s replication mechanism provide durability and fault tolerance? - How would you manage Kafka consumer rebalancing to minimize data loss? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 - What are dynamic DAGs in Airflow, and what benefits do they offer? - What are Airflow pools, and how do they help control task concurrency? - How do you implement time-based and event-based triggers for DAGs in Airflow? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝘂𝗱𝗶 - How does Apache Hudi support incremental data processing in data lakes? - What are the different table types available in Apache Hudi, and how do they differ? - How does Hudi handle upserts (updates and inserts) at scale? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 - What is Apache Flink’s event-time processing, and how is it different from processing time? - How does Flink’s state management work for fault-tolerance in stream processing? - What are the different windowing functions available in Apache Flink? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 - How does Apache Iceberg enable schema evolution without breaking existing queries? - What is the purpose of partitioning in Iceberg, and how does it differ from traditional partitioning techniques? - How does Iceberg handle large-scale data versioning and rollback? 🚨 To learn more, Join my high quality, industry & modern tech stack based and practical project driven Data Engineering BootCAMP✌🏻👇 👉 Enroll Here - https://lnkd.in/dRgYCmWd 🚀 Dedicated Doubt Support & Placement Assistance 📲 Call/WhatsApp for any query (+91) 9893181542 Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar

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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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🌟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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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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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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Informative Article for Beginners in Data Engineering

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Ever wondered how to run any code anywhere without worrying about the underlying infrastructure? That’s exactly what Task Runners in Kestra do! Task Runners are a pluggable system that gives you the flexibility to run flows on different platforms like GCP and AWS, exactly when you need them. Whether it's scheduling a flow to run on a specific instance or optimizing your cloud consumption, Task Runners have got you covered. https://lnkd.in/e7trJANx

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🔥 PySpark Practice Questions for Real-World Scenarios 🔥 Working with big data and distributed processing requires hands-on experience with tools like PySpark! Here are some scenario-based questions to level up your PySpark skills: 1️⃣ Data Transformation Challenge 💡 Scenario: You have a dataset with customer details, including purchase amounts. Some rows have null values in the purchase column. 👉 Task: Write code to replace null values with the average purchase amount and display the top 5 customers based on purchase total. 2️⃣ Data Cleaning and Aggregation 💡 Scenario: Your dataset contains product details across multiple regions. Some records are duplicated and have inconsistent formats for region names. 👉 Task: Deduplicate the data, standardize region names, and calculate the total sales per region. 3️⃣ Joining DataFrames 💡 Scenario: You have two DataFrames – one with customer details and another with order history. 👉 Task: Write code to join these DataFrames on a common key, keeping only customers who have made purchases in the last year. Display the resulting dataset with order count per customer. 4️⃣ Window Functions 💡 Scenario: You need to calculate the running total of sales for each product, ordered by sale date. 👉 Task: Use PySpark's window functions to compute the running total and display it alongside each sale record. 5️⃣ Filtering Complex Data Structures 💡 Scenario: Your dataset contains nested JSON data with details of each transaction, including customer, items purchased, and transaction date. 👉 Task: Flatten the JSON structure, filter for transactions from the last quarter, and calculate the total sales for each item category. 6️⃣ Optimizing Spark Jobs 💡 Scenario: Your Spark job is taking too long to process a large dataset due to data shuffling. 👉 Task: Identify optimization techniques to reduce shuffle, such as partitioning strategies and caching, and explain how you would apply them in this context. 💻 Practicing these scenarios will help you become a pro at handling large datasets in PySpark! #PySpark #BigData #DataEngineering #SparkSQL #DistributedComputing #DataTransformation #ApacheSpark #DataScience #CodingInterview #TechSkills #DataProcessing #ETL #DataCleaning #DataPipeline #InterviewPreparation #RealWorldData #BigDataSkills #SparkOptimization

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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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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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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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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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Processing 1 TB of data in Databricks!! Interviewer: Let’s assume you’re processing 1 TB of data in Databricks. How long do you think it would take on a single machine without using any cluster? Candidate: On a single machine, processing 1 TB of data would take significantly longer because there's no parallelism involved. The speed largely depends on the hardware specifications—specifically CPU, disk speed and RAM capacity. - For CSV files (which are uncompressed and not optimized for parallel processing), we can estimate that it would take anywhere between 6-8 hours for a single machine due to the high disk I/O, lack of compression, and lack of parallelization. - For Parquet files (which are columnar and compressed), the process would be more efficient. A single machine with sufficient resources could potentially complete the task in 3-5 hours, assuming it's not heavily transformed. Parquet’s compression and optimized storage significantly reduce the I/O and speed up reading and writing operations compared to CSV.  Interviewer: Now, if we scale this up to using a 4-node cluster in Databricks, what kind of time reduction should we expect?  Candidate: Scaling up to a 4-node cluster distributes the 1 TB dataset into 4 parts, so each node would be processing around 250 GB of data. - For Parquet data: Since Parquet is optimized for parallelism, we can expect a significant speedup. In the case of 1 TB of Parquet data, assuming good partitioning and high parallelism, the processing time could drop to around 1.5-2 hours. Each node is handling a smaller subset of the data, and with good disk I/O and CPU parallelism, the overall time can be reduced substantially. - For CSV data: Although CSV is not as optimized as Parquet, the 4-node cluster still offers better performance due to parallel processing. Processing 1 TB of CSV data might take around 3-4 hours on a 4-node cluster, compared to 6-8 hours on a single machine. However, this is still slower than Parquet due to the uncompressed nature of CSV.  Interviewer: What about scaling further to an 8-node cluster?  Candidate: An 8-node cluster offers even better performance because now each node only needs to handle 125 GB of data. The added parallelism can dramatically reduce processing times. - For Parquet data: The processing time can drop to 30 minutes to 1 hour. With 8 nodes, the workload is even more distributed, which speeds up reading, processing, and writing operations. Given Parquet’s compression and columnar nature, the data can be processed very efficiently in parallel. - For CSV data: Since CSV files are larger and not optimized for parallel processing, the improvement is not as dramatic as with Parquet, but you still see a reduction in time. Processing 1 TB of CSV data on an 8-node cluster could take around 1.5-2 hours. The increased parallelism from the 8 nodes allows more efficient data distribution, reducing the time it takes to read and write data.

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Uber's Data Platform is an inspiration for many Data Engineers. It hosts around 👀 𝟭.𝟱 𝗲𝘅𝗮𝗯𝘆𝘁𝗲𝘀 of Apache Hadoop Distributed File System (HDFS). Uber has built 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 to handle such a huge amount of data, on which thousands of #Spark jobs are running and hundreds of concurrent users are running queries. ✨ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵? ☁ Data Mesh is a cloud resources management service that organizes data resources in an organization-centric hierarchical manner.  𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵: 1. 𝗗𝗼𝗺𝗮𝗶𝗻: A hierarchical unit of organizing all the resources in an organization or team. In data mesh terms, domains can be both at the organization level or at a sub-organization/team level. 2. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Resource containers, which are a unit of access control, resource grouping, and cost accounting. 3. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: A logical container for data (DB/tables/files) belonging to the same category e.g., raw data, tables for internal consumption or modeled tables for external broad access. 4. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: The storage buckets which are a unit of access control, cost accounting, geographic location, storage class (hot, warm, cold), data lifecycle policies (TTL, legal holds, etc.), and data replication (to either standby store or archival store). 5. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁: A curated, discoverable, and usable representation of data, which includes the Hive tables, BI dashboards, workflow pipelines, etc. Read more about this here: https://lnkd.in/gkt7k5jJ #Uber #DataEngineering #DataMesh #architecture #cloud Arun M. Abhi Khune Sahana Bhat

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