Data Engineers

𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 — 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍 📌 Preparing for Python Interviews in 2025?🗣 If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3ZbAtrW Crack your next Python interview✅️

Few topics that you need to cover for Kafka interview: 1. Topic - Partition - Message ordering - Replication - Offset - Compression 2. Producer - Serialization - Batching - Compaction - Intervals - Sync & Async - Idempotence - Some important properties 3. Broker - Kafka cluster - Replication - Retention - Cleanup - Graceful shut down 4. Consumer - Deserialization - Consumer group - Consumption types - Sync & Async - Failure handling - Some important properties 5. Zookeeper 6. Schema registry 7. Admin client API, MakeMirror 8. Kafka Streams 9. Kafka Connect

𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Feeling like your resume could use a boost? 🚀 Let’s make that happen with Microsoft Azure certifications that are not only perfect for beginners but also completely free!🔥💯 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4iVRmiQ Essential skills for today’s tech-driven world✅️

Data Science Techniques

𝗟𝗲𝗮𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 — 𝗙𝗼𝗿 𝗙𝗿𝗲𝗲!😍 Want to break into machine learning but not sure where to start?💻 Google’s Machine Learning Crash Course is the perfect launchpad—absolutely free, beginner-friendly, and created by the engineers behind the tools.👨‍💻📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jEiJOe All The Best 🎊

Data Lake vs Data Warehouse

DevOps Tech Stack

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Azure Data Engineer interview questions: Project Questions: 1) Tell me about your project. Explain it end to end. 2) On what measure you have mentioned about the storage cost decreased and cost optimization has been done? ADF and ADLS 1) There are 10 million CSV files in ADLS Gen2. How will you read them and process using Azure Data Factory? Explain in detail what and all would be used. 2) What is Integration Runtime? 3) What is variable and parameter in ADF? 4) Explain different activities and brief about what each activity does. 5) The pipeline is scheduled and it got failed. I need to automate it by sending the mail when it gets failed. How would you implement it? 6) How do you handle the exceptions in ADF? 7) If the ADF pipeline is running very slow, how would you approach and fix it? 8) What is the difference between Blob storage and ADLS Gen2? Why ADLS Gen2 is required? Azure Databricks 9) How do you connect ADLS Gen 2 with databricks? In where we mention the role assignments? 10) If you are using Service Principal to connect with ADLS from Azure Databricks explain the steps and how would you code it? 11) Why using service principal? How would you create it? 12) What is Databricks runtime? Why we need it? 13) What are Workflows? 14) Explain about the Medallion Architecture in brief. 15) Explain about Delta file format briefly. 16) Consider you are working in Facebook. User is writing data for each record. Since each record is been written, a new json transaction log will get created for each write. But we can use Datalake only right. Why do we require delta file format? It can decrease the performance every now and then right? 17) Consider a job is running very slow in Azure Databricks. How would you approach the issue and make it faster? 18) What are the optimization techniques you have worked on? Explain them in brief. 19) How would you optimize the job with respect to memory management in azure databricks?

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Data Analyst vs Data Engineer: Must-Know Differences Data Analyst: - Role: Focuses on analyzing, interpreting, and visualizing data to extract insights that inform business decisions. - Best For: Those who enjoy working directly with data to find patterns, trends, and actionable insights. - Key Responsibilities: - Collecting, cleaning, and organizing data. - Using tools like Excel, Power BI, Tableau, and SQL to analyze data. - Creating reports and dashboards to communicate insights to stakeholders. - Collaborating with business teams to provide data-driven recommendations. - Skills Required: - Strong analytical skills and proficiency with data visualization tools. - Expertise in SQL, Excel, and reporting tools. - Familiarity with statistical analysis and business intelligence. - Outcome: Data analysts focus on making sense of data to guide decision-making processes in business, marketing, finance, etc. Data Engineer: - Role: Focuses on designing, building, and maintaining the infrastructure that allows data to be stored, processed, and analyzed efficiently. - Best For: Those who enjoy working with the technical aspects of data management and creating the architecture that supports large-scale data analysis. - Key Responsibilities: - Building and managing databases, data warehouses, and data pipelines. - Developing and maintaining ETL (Extract, Transform, Load) processes to move data between systems. - Ensuring data quality, accessibility, and security. - Working with big data technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, Google Cloud). - Skills Required: - Proficiency in programming languages like Python, Java, or Scala. - Expertise in database management and big data tools. - Strong understanding of data architecture and cloud technologies. - Outcome: Data engineers focus on creating the infrastructure and pipelines that allow data to flow efficiently into systems where it can be analyzed by data analysts or data scientists. Data analysts work with the data to extract insights and help make data-driven decisions, while data engineers build the systems and infrastructure that allow data to be stored, processed, and analyzed. Data analysts focus more on business outcomes, while data engineers are more involved with the technical foundation that supports data analysis. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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FREE RESOURCES TO LEARN DATA ENGINEERING 👇👇 Big Data and Hadoop Essentials free course https://bit.ly/3rLxbul Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE [4.6 stars out of 5] https://bit.ly/3fGRjLu Understanding Data Engineering from Datacamp https://clnk.in/soLY Data Engineering Free Books https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf Big Data of Data Engineering Free book https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf The Data Engineer’s Guide to Apache Spark https://t.me/datasciencefun/783?single Data Engineering with Python https://t.me/pythondevelopersindia/343 Data Engineering Projects - 1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge 2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J 3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq 4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3 5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR 6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD 7. YouTube Data Analysis     (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF 8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY 9. Sentiment analysis Twitter:     Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU ENJOY LEARNING 👍👍

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