Rahul Shukla’s Post

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

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 🇮🇳

Recently Asked Data Engineering Interview Questions 👇 🔹 Given a table of sales transactions, write a query to calculate the cumulative sales for each product over time. 🔹 How would you handle NULL values in SQL when performing aggregations? 🔹 Explain the concept of data partitioning and its significance in distributed systems. 🔹 How does Apache Spark handle data processing, and what are its advantages over Hadoop MapReduce? 🔹 Explain the role of HDFS in the Hadoop ecosystem. 🔹 What is the purpose of a message broker like Kafka in a data pipeline? 🔹 Write a Python script to merge two sorted lists into a single sorted list. 🔹 How would you handle large datasets in Python to ensure efficient memory usage? 🔹 What are normalization and denormalization in database design? Provide examples of when to use each. 🔹 Explain the concept of serverless computing and its advantages. 🔹 How would you deploy a data pipeline in a cloud environment? 🔹 What is the Data Catalog in AWS Glue? 🔹 Difference between Athena and Aurora. 🔹 What are the key components of a data governance framework? 🔹 Explain the importance of data lineage and how it can be tracked. 🔹 Explain the CAP theorem and its implications for distributed databases. 🚨 We have started the batch of "Data Engineering With AWS" BootCAMP which is high quality, affordable, practical & industry grade project oriented✌🏻We have included Apache Flink, Hudi & Iceberg too😇 👉 Enroll Here - https://bit.ly/3Y5gCJE 🎉 Dedicated placement assistance & doubt support 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 😎 

📊🔧 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

This has been a incredibly insightful and enriching week for me! I have exceeded 😅 my daily target of learning atleast one new thing daily. Here are some of the topics I learnt this week: 1. What is Big Data, 5V's? 2. Monolithic vs Distributed systems 3. Hadoop, overview and it's core components (HDFS, MapReduce, YARN), and challenges with it. 4. On premise vs Cloud 5. Advantages and types of Cloud 6. Apache Spark, why require? 7. Introduction and comparison btw DB, DWH & Data Lakes. 8. Data Engineering flow 9. Data Pipeline Visualization with Hadoop and Cloud(Azure, AWS) 10. Schema on Write and Schema on Read what are Name node, Data node Data Engineering Flow: Data from Multiple sources > Storage > Processing > Serving Ingestion: Sqoop/Azure Data Factory/Amazon Glue, Storage: HDFS/ ADLS Gen2/Amazon S3, Computing/Processing: MapRedue/Azure Databricks/Synapse/ AWS Databricks/Athena Serving: Hive/Azure SQL/ AWS RDS All above terminologies were Alien 👽 to me since last week, now I feel like I know all these for months, all thanks to Sumit Mittal Sir for making it so easy to understand. Excited to deep dive into it! #Week1 #learning #DataEngineering #trendytech #bigdata #SummitMittal

This week in the "Big Data Cloud Focused Master's Program" at TrendyTech Sumit Mittal, I delved into Apache Spark Project 1, covering key project elements, example problem statements, and Agile Methodology. The sessions on data cleaning have deepened my understanding of Apache Spark Optimizations. Excited to apply this knowledge in real-world scenarios! 🚀 🥷 #BigData #Cloud Tags: 🏛️ #data #dataengineering #databricks #dataengineerjobs #python #pyspark #bigdata #hadoopadmin #scala #dataanalytics #snowflake #snowflakedevelopers #datacloud #aws #awscloud #kafka #knowledgesharing #dailypost #datascience #machinelearning #dailypost

Data engineering interviews will be 18x easier if you learn these tools in seqence. Data Engineering Mastery: A Step-by-Step Guide Cracking data engineering interviews just got easier! Follow this structured approach to ace your next interview: Prerequisites: * SQL * Python (Pandas & NumPy) * Data Warehousing * Data Modeling * CI/CD Choose Your Path: * On-Prem Tools: * PySpark * Hadoop * Hive * HBase * Airflow * Kafka * Azure Tools: * Azure Data Factory * Databricks * Azure Synapse Analytics * Azure Data Lake Storage * Azure Blob Storage * Azure Functions * Azure Stream Analytics * GCP Tools: * BigQuery * Dataflow * Dataproc * Data Fusion * Cloud Composer * Pub/Sub * Google Cloud Storage * AWS Tools: * Redshift * Glue * EMR * S3 * Lambda * Step Functions * Aurora * DynamoDB Hashtags: #dataengineering #interviewprep #dataengineeringinterview #datascience #bigdata #cloudcomputing #azure #gcp #aws #hadoop #pyspark #datawarehousing #datamodelling #cicd #datafactory #databricks #synapseanalytics #datalakestorage #blobstorage #functions #streamanalytics #bigquery #dataflow #dataproc #datafusion #cloudcomposer #pubsub #googlecloudstorage #redshift #glue #emr #s3 #lambda #stepfunctions #aurora #dynamodb

𝟕 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐒𝐤𝐢𝐥𝐥𝐬 𝐈 𝐦𝐚𝐬𝐭𝐞𝐫.. With Data increasing everyday and companies sitting on huge volume of data, most of them clueless about how to utilise this resource which is as worthy as a gold mine. I feel proud to say that I started working in Big Data domain 4 years back when there was less visibility about growing demand for Data Engineers. I would like to highlight the experience of my Cloud Big Data Upskilling journey with " Big Data Masters Program". 7 Key Skills which I master are; • Distributed Processing with PySpark • Spark Architecture • Optimizations and Performance Tuning which includes Adaptive Query Execution (AQE), Partitioning, Bucketing, File Formats, Compression Techniques • CICD: Git, GitHub • AWS Cloud Services: EMR, S3, Redshift, Athena, Glue • Spark Stream Processing and Structured Streaming with Kafka • System Design and Data Modeling I enrolled for Sumit Mittal Sir Big Data Masters program 2 years back and since then there isn’t a single day when I don’t use his Big Data learnings to solve complex problem. A big thanks to Sumit Mittal Sir who made this look so easy and TrendyTech team for all the support in my Upskilling journey. #DataEngineering #PySpark #AWSCloud #BigData

🎉 Completed Week 1 of the Elite Data Engineering Course! 🎓 Excited to share that I’ve successfully completed the first week of my 20-week Elite Data Engineer course by Sumit Mittal through #TrendyTech This week was packed with invaluable insights into Big Data Fundamentals and the role of a Data Engineer. Here’s what I learned: What is Big Data? Understanding the 5 V's and the Monolithic vs Distributed systems. Key elements for designing big systems: Storage, Processing, and Scalability. Overview of Hadoop: Core components (HDFS, MapReduce, YARN), its ecosystem (Sqoop, Pig, Hive, Oozie, HBase), and challenges with MapReduce. Apache Spark: How it addresses Hadoop’s bottlenecks and works as a compute engine. Database vs. Data Warehouse vs. Data Lake: Differences in schema-on-write vs. schema-on-read approaches. Exploring cloud pipelines on Azure Cloud and AWS: Understanding AWS Redshift, Serverless vs. Serverful systems. The Data Engineer's Role: Converting raw data into formats that are ready for analysis and consumption. Looking forward to diving deeper into the world of data engineering in the coming weeks! #DataEngineering #BigData #TrendyTech #AWS #Azure #LearningJourney

I’ve completed Week 7 of "The Ultimate Big Data Masters Program (Cloud Focused - Azure & AWS)" by Sumit Mittal sir at the TrendyTech Platform! I’ve been diving deep into Week 7 of this 32-week Big Data Masters Program journey, and I wanted to share some key things I’ve learned: 1. Accessing Spark UI 2. Understanding Cache & Persist 3. Cache Practical's 4. Parsed | Analyzed | Optimized Logical Plan 5. Cache - IN Memory Table Cache | Node Local & Process Local 6. Caching Spark Table 7. Spark Catalog, Managed & External Tables 8. Cache Performance 9. Understanding Persist I’m excited to continue learning and applying these concepts to real-world projects! #BigData #ApacheSpark #DataEngineering #DataScience #SparkOptimization #Caching #Persistence #SparkPerformance #DataProcessing #Hadoop #YARN #SparkSQL #MachineLearning #DataAnalytics #TechTips #BigDataEngineering #ResourceManagement #SparkUI #DataWorkflow #DataCaching