Rahul Shukla’s Post

Here are some recently asked Big Data Engineer interview questions 👇 🔹 What is the difference between MapReduce and Spark? 🔹 Explain the concept of data partitioning in distributed systems like Spark. 🔹 How does Kafka handle message durability and fault tolerance? 🔹 What is the role of YARN in the Hadoop ecosystem? 🔹 What is speculative execution in Hadoop, and why is it used? 🔹 How does Spark handle data lineage, and why is it important? 🔹 Explain the difference between a Data Lake and a Data Warehouse. 🔹 What are Hive partitions and bucketing, and when should you use them? 🔹 How do you optimize Spark jobs for better performance? 🔹 What is the role of Zookeeper in distributed systems? 🔹 How do you handle schema evolution in Big Data pipelines? 🔹 What are combiners in MapReduce, and how do they help optimize performance? 🔹 What is the difference between Avro, Parquet, and ORC file formats? 🚨 We have just started the new batch of my "Data Engineering With AWS" BootCAMP which is high quality, affordable, practical & industry grade project oriented✌🏻Join now & upskill with the most modern & demanding tech stack 👇 👉 Enroll Here - https://bit.ly/3Y5gCJE 🎉 Dedicated placement assistance & doubt support 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳

Here are 10 Apache Flink interview questions focused on data engineering roles 👇 ✅ What are the key components of Apache Flink’s architecture, and how do they work together? ✅ How does Flink manage state in stream processing, and why is it important for fault tolerance? ✅ Explain the difference between processing time, event time, and ingestion time in Flink. ✅ How does Flink’s windowing mechanism work, and what are the different types of windows available? ✅ Describe Flink’s checkpointing mechanism and how it ensures exactly-once processing semantics. ✅ What are keyed streams in Flink, and how do they affect the processing of events? ✅ How do you handle late data in Flink, and what are watermark strategies? ✅ Explain how Apache Flink integrates with Kafka, and how you can process data from a Kafka topic in Flink. ✅ What are some techniques for optimizing Flink jobs for performance and resource management? ✅ Describe a scenario where you would choose Flink over another stream processing tool like Apache Kafka Streams or Spark Streaming. 🚨 Join my high quality, industry & modern tech stack driven (AWS, GCP, Snowflake, Databricks, Flink, Iceberg, Hudi and so on) and practical project driven Data Engineering 4.0 With AWS 👇 👉 Enroll Here - https://lnkd.in/d4SS8JTm 🚀 Live Classes Starting on 9-Nov-2024 📲 Call/WhatsApp for any query (+91) 9893181542 Shashank Mishra 🇮🇳

Here are 10 Apache Iceberg interview questions for data engineering roles 👇 ✅ What is Apache Iceberg, and how does it differ from other data lake table formats like Parquet or ORC? ✅ How does Apache Iceberg handle schema evolution, and why is it beneficial in a data lake environment? ✅ Can you explain how partitioning works in Apache Iceberg and how it optimizes query performance? ✅ What is the role of metadata in Apache Iceberg, and how does Iceberg manage metadata differently from traditional file formats? ✅ How does Apache Iceberg support time travel queries, and what are the use cases for this feature? ✅ What are the key advantages of Apache Iceberg in terms of data versioning and snapshot isolation? ✅ How does Apache Iceberg integrate with query engines like Spark, Flink, or Hive? ✅ What are the benefits of using Apache Iceberg for large-scale data ingestion and management in a cloud-based environment? ✅ How does Iceberg handle data compaction, and why is it important for performance optimization? ✅ What are the best practices for using Apache Iceberg in a multi-tenant data lake architecture? 🚨 Join my high quality, industry & modern tech stack driven (AWS, GCP, Snowflake, Databricks, Flink, Iceberg, Hudi and so on) and practical project driven Data Engineering 4.0 With AWS 👇 👉 Enroll Here - https://lnkd.in/d4SS8JTm 🚀 Live Classes Starting on 9-Nov-2024 📲 Call/WhatsApp for any query (+91) 9893181542

Roadmap to become Azure Data Engineer in 2024. 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀 - SQL - Python 𝗔𝘇𝘂𝗿𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 - Azure Data Factory - Azure Databricks - Azure Synapse - Azure Functions - Azure Storage - Azure Data Lake - Azure HDInsight 𝗔𝘁 𝗹𝗲𝗮𝘀𝘁 𝟮 𝗘𝗻𝗱 𝘁𝗼 𝗘𝗻𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 - Involves end to end pipeline - All the major services 𝗔𝗧𝗦 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝘁 𝗥𝗲𝘀𝘂𝗺𝗲 - Score 80+ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 - Fundamental concepts - Scenario-Based Questions - Mock Interviews Now, If you want to get into Azure Data Engineering then Deepak Goyal is conducting free Masterclass Topic - How to become an highest paid Azure Data Engineer

I have successfully completed my Cloud Big Data Engineer certification! Special thanks to Sumit Mittal Sir for the Ultimate Big Data Masters Program. This course provided profound insights into the vast domain of Big Data. Here’s a glimpse of the key concepts covered: 🔹 Fundamentals of Distributed Processing 🔹 PySpark 🔹 Optimizations and Performance Tuning 🔹 A Comprehensive PySpark Project 🔹 CI/CD: (Git, GitHub) 🔹 Data Warehousing Tool: Hive 🔹 In-depth Azure Services 🔹 Databricks 🔹 End-to-End Azure Capstone Project 🔹 Spark Stream Processing / Real-time Data Handling 🔹 Structured Streaming with Kafka 🔹 System Design and Data Modeling 🔹 DSA Concepts #trendytech #sumitmittal #bigdata #dataengineering

I’m excited to share that I’ve embarked on a new journey to enhance my skill set. Now that I'm in the second week of learning. What is Big Data? Big Data is defined by the following characteristics, often referred to as the "V's": Volume: Large quantities of data Variety: Diverse data formats Velocity: Speed of data generation Veracity: Accuracy and quality of data Value: Relevance and usefulness of data What makes a good Big Data system? A good Big Data system should excel in: Storage Computation Scalability Core Components of Hadoop: 1.HDFS (Hadoop Distributed File System) 2.MapReduce 3.YARN (Yet Another Resource Negotiator) > I also explored where Spark fits in and how MapReduce has evolved in the Big Data landscape. > Database vs. Data Warehouse vs. Data Lake > Big Data Overview: Here’s a snapshot of the data processing flow: 1. Data from various sources 2. Ingestion 3. Storage 4. Computing 5. Serving Layer 6. Visualization > I also covered Serverless vs. Serverful computing and the HDFS architecture, Azure and AWS services for Big data. The first week provided a solid foundation in these concepts. I have just mentioned some takeaways! Many thanks to Sumit Mittal sir and TrendyTech Stay Tuned 😊✌️

𝘼𝙧𝙚 𝙮𝙤𝙪 𝙬𝙖𝙞𝙩𝙞𝙣𝙜 𝙛𝙤𝙧 𝙖 𝙥𝙚𝙧𝙛𝙚𝙘𝙩 𝙧𝙤𝙖𝙙𝙢𝙖𝙥 🗺️ 𝙩𝙤 𝙨𝙩𝙖𝙧𝙩 Data Engineering❓ Remember ⚠️ Taking the first step is more important than having a perfect plan ⛷️ It's more important to get practice in early than learning the new technologies 🌎 Hello 🙋🏻 Aspiring data engineers, ⛷️ learn SQL ⚙️ and Python first 🐍 ⛷️ build a Flask or FastAPI app for your personal usages 🧑🏻💻 ⛷️ learn database modeling OLAP vs OLTP as a most critical thing🦫 ⛷️ normalized data modeling vs dimensional data modeling 🦣 ⛷️ learn distributed compute whether it's Spark, Snowflake, BigQuery, AWS Glue , Databricks etc. ⛷️ learn job orchestration whether it's Airflow, Databricks , AWS Step Functions etc ⛷️ learn some cloud services for data engineering whether it's Azure ADF , AWS Glue , GCP DataFlow etc. Just complete this and you gonna start believing in your skills more confidently ✅ If you need any help 🧑🏻💻 let's connect with me 📆 link available in the comments 💬 follow for more contents ❄️ #dataengineering

🌟 Kickstart Your Journey in Data Engineering! 🛠️ Embarking on a career in Data Engineering? Here’s how to dive in: Understand the Basics: Grasp the fundamentals of databases, SQL, and Python. Learn Big Data Tools: Familiarize yourself with Hadoop, Spark, and Kafka. Cloud Platforms: Get hands-on with AWS, GCP, or Azure services. Projects: Apply your skills to real-world problems and build a portfolio. 📚 Resources: Coursera and Udemy for structured courses. GitHub for open-source projects to contribute. Meetups and forums for networking and knowledge sharing. Start your data engineering journey today and unlock the potential of big data! #DataEngineering #BigData #CloudComputing #CareerDevelopment

📊🔧 Just Started an Intensive Data Engineering Course! 🔧📊 Exciting news! I've recently embarked on an intensive data engineering course by Grow Data Skills, under the guidance of Shashank Mishra 🇮🇳 covering a wide array of topics to enhance my skills 🔍 Course Highlights: SQL Big Data Technologies Apache Hadoop Apache Hive Kafka MongoDB Cassandra PySpark Databricks Airflow Data Warehousing Snowflake AWS (Amazon Web Services) Real-world Projects 🎯 Goals: Mastering database management and optimization. Exploring big data technologies for scalable solutions. Gaining hands-on experience with Apache Hadoop and its ecosystem. Understanding real-time data processing with Kafka. Delving into NoSQL databases like MongoDB and Cassandra. Harnessing the power of PySpark and Databricks for data processing. Implementing efficient workflows with Apache Airflow. Building robust data warehouses and utilizing Snowflake. Leveraging AWS for cloud-based solutions. Applying knowledge to practical projects for real-world experience. 💡 Progress Update: I'm thrilled to share that I've completed Module 1 focusing on SQL fundamentals. It's been an insightful journey so far, and I'm eager to dive deeper into the upcoming modules. I can't wait to share my progress with you all. Stay tuned for updates on my journey! #Google #Amazon #Microsoft #Facebook #Apple #Netflix #Uber #Airbnb #LinkedIn #IBM #yashtechnologies #indore #Impetus #taskus #Infobeans #celebal #azure #databricks #DataEngineering #BigData #DataPipeline #ETL #DataArchitecture #DataIntegration #DataWarehousing #DataOps #DataInfrastructure #DataProcessing #DataQuality #DataModeling #ApacheSpark #Hadoop #CloudDataEngineering #StreamingData #DatabaseManagement #DataAnalytics #MachineLearning #DataScience #redhat #AddendAnalytics

Data Engineers use PySpark for Large-Scale Data Processing and if you’re preparing for Data Engineering roles it is a must have. 💪🏻 It's crucial to master various concepts as it's asked in the interviews💯 So, to help you prepare here's a guide to PySpark Huge thanks to Bosscoder Academy for sharing this doc. Check them here: https://bit.ly/49wIR9G Enroll in their program and get: ✅ Structured curriculum to master ETL & Warehousing, Big Data & Cloud, Advanced Data Ops, and more. ✅ Personalized guidance from experts working at Google, Samsung, and other top companies. ✅ Multiple projects focused on Big Data pipeline, data processing and other in-demand skills to build a strong portfolio.