Data Engineers

10 Data Engineering Projects to build your portfolio. 1. Olympic Data Analytics using Azure https://lnkd.in/gHNyz_Bg 2. Uber Data Analytics using GCP. https://lnkd.in/gqE-Y4HS 3. Stock Market Real-time Data Analysis using Kafka https://lnkd.in/gknh7ZEr 4. Twitter Data Pipeline using Airflow https://lnkd.in/g7YPnH7G 5. Smart City End to End project using AWS https://lnkd.in/gh2eWF66 6. Realtime Data Streaming using spark and Kafka https://lnkd.in/gjH2efgz 7. Zillow Data Analytics - Python, ETL https://lnkd.in/gvEVZHPR 8. End to end Azure Project https://lnkd.in/gCVZtNB5 9. End to end project using snowlake https://lnkd.in/g96n6NbA 10. Data pipeline using Data Fusion https://lnkd.in/gR5pkeRw Data Engineering Interview Preparation Resources: 👇 https://topmate.io/analyst/910180 Hope this helps you 😊 If you've read so far, do LIKE the post👍

Complete Data Engineering Roadmap to keep yourself in the hunt in job market. 1. I will Learn SQL --variables, data types, Aggregate functions -- Various joins, data analysis -- data wrangling, operators like(union, intersect etc.) --Advanced SQL(Regex, Having, PIVOT) --Windowing functions, CTE --finally performance optimizations. 2. I will learn Python... -- Basic functions, constructors, Lists, Tuples, Dictionaries -- Loops (IF, When, FOR), functional programming -- Libraries like(Pandas, Numpy, scikit-learn etc) 3. Learn distributed computing... --Hadoop versions/hadoop architecture --fault tolerance in hadoop --Read/understand about Mapreduce processing. --learn optimizations used in mapreduce etc. 4. Learn data ingestion tools... --Learn Sqoop/ Kafka/NIFi --Understand their functionality and job running mechanism. 5. i ll Learn data processing/NOSQL.... --Spark architecture/ RDD/Dataframes/datasets. --lazy evaluation, DAGs/ Lineage graph/optimization techniques --YARN utilization/ spark streaming etc. 6. Learn data warehousing..... --Understand how HIve store and process the data --different File formats/ compression Techniques. --partitioning/ Bucketing. --different UDF's available in Hive. --SCD concepts. --Ex Hbase. cassandra 7. Learn job Orchestration... --Learn Airflow/Oozie --learn about workflow/ CRON etc. 8. Learn Cloud Computing.... --Learn Azure/AWS/ GCP. --understand the significance of Cloud in #dataengineering --Learn Azure synapse/Redshift/Big query --Learn Ingestion tools/pipeline tools like ADF etc. 9. Learn basics of CI/ CD and Linux commands.... --Read about Kubernetes/Docker. And how crucial they are in data. --Learn about basic commands like copy data/export in Linux. Data Engineering Interview Preparation Resources: 👇 https://topmate.io/analyst/910180 Like if you need similar content 😄👍 Hope this helps you 😊

Top Interview Questions for Apache Airflow 👇👇 1. What is Apache Airflow? 2. Is Apache Airflow an ETL tool? 3. How do we define workflows in Apache Airflow? 4. What are the components of the Apache Airflow architecture? 5. What are Local Executors and their types in Airflow? 6. What is a Celery Executor? 7. How is Kubernetes Executor different from Celery Executor? 8. What are Variables (Variable Class) in Apache Airflow? 9. What is the purpose of Airflow XComs? 10. What are the states a Task can be in? Define an ideal task flow. 11. What is the role of Airflow Operators? 12. How does airflow communicate with a third party (S3, Postgres, MySQL)? 13. What are the basic steps to create a DAG? 14. What is Branching in Directed Acyclic Graphs (DAGs)? 15. What are ways to Control Airflow Workflow? 16. Explain the External task Sensor. 17. What are the ways to monitor Apache Airflow? 18. What is TaskFlow API? and how is it helpful? 19. How are Connections used in Apache Airflow? 20. Explain Dynamic DAGs. 21. What are some of the most useful Airflow CLI commands? 22. How to control the parallelism or concurrency of tasks in Apache Airflow configuration? 23. What do you understand by Jinja Templating? 24. What are Macros in Airflow? 25. What are the limitations of TaskFlow API? 26. How is the Executor involved in the Airflow Life cycle? 27. List the types of Trigger rules. 28. What are SLAs? 29. What is Data Lineage? 30.What is a Spark Submit Operator? 31. What is a Spark JDBC Operator? 32. What is the SparkSQL operator? 33. Difference between Client mode and Cluster mode while deploying to a Spark Job. 34. How would you approach if you wanted to queue up multiple dags with order dependencies? 35. What if your Apache Airflow DAG failed for the last ten days, and now you want to backfill those last ten days' data, but you don't need to run all the tasks of the dag to backfill the data? 36. What will happen if you set 'catchup=False' in the dag and 'latest_only = True' for some of the dag tasks? 37. What if you need to use a set of functions to be used in a directed acyclic graph? 38. How would you handle a task which has no dependencies on any other tasks? 39. How can you use a set or a subset of parameters in some of the dags tasks without explicitly defining them in each task? 40. Is there any way to restrict the number of variables to be used in your directed acyclic graph, and why would we need to do that? Data Engineering Interview Preparation Resources: 👇 https://topmate.io/analyst/910180 Like if you need similar content 😄👍 Hope this helps you 😊

Here's what the average data engineering interview looks like in 2024: - 1 hour algorithms in Python Here you will be asked irrelevant questions about dynamic programming, linked lists, and inverting trees - 1 hour SQL Here you will be asked niche questions about recursive CTEs that you've used once in your ten year career - 1 hour data architecture Here you will be asked about CAP theorem, lambda vs kappa, and a bunch of other things that ChatGPT probably could answer in a heartbeat - 1 hour behavioral Here you will be asked about how to play nicely with your coworkers. This is the most relevant interview in my opinion - 1 hour project deep dive Here you will be asked to make up a story about something you did or did not do in the past that was a technical marvel - 4 hour take home assignment Here you will be asked to build their entire data engineering stack from scratch over a weekend because why hire data engineers when you can submit them to tests?

🔍 Mastering Spark: 20 Interview Questions Demystified! 1️⃣ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce. 2️⃣ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique. 3️⃣ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark. 4️⃣ RDD Operations: Explore the various RDD operations that power Spark. 5️⃣ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark. 6️⃣ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark. 7️⃣ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark. 8️⃣ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk. 9️⃣ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications. 🔟 spark-submit Parameters: Explore the parameters to specify in the spark-submit command. 1️⃣1️⃣ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark. 1️⃣2️⃣ Deploy Modes: Learn about the deploy modes in Spark and their significance. 1️⃣3️⃣ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem. 1️⃣4️⃣ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance. 1️⃣5️⃣ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job. 1️⃣6️⃣ Spark Job Execution Internals: Get a peek into how Spark internally executes a program. 1️⃣7️⃣ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver. 1️⃣8️⃣ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark. 1️⃣9️⃣ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans. 2️⃣0️⃣ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios. Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180

Kavitha's Journey to become a Data Engineer 👇👇 1. Startup to Dream Job Journey: - Started at a startup in India, transitioned to Infosys, then grabbed UK opportunity. - Shifted from legacy Mainframe to AWS Cloud, pursued Master's from illinoisstateu, and secured dream job at Statefarm. 2. Learn Fundamentals: - Assess skills, understand role. - Gain proficiency in Python, SQL. - Learn data technologies. 3. Database and Modeling Skills: - Understand databases, gain proficiency. - Learn data modeling principles. 4. Master ETL, Warehousing, and Visualization: - Understand ETL, data warehousing. - Gain experience in building warehouses. - Familiarize with visualization tools. - Got Certified as AWS Solutions Architect. 5. Utilize LinkedIn for Job Search: - Network and connect with professionals. - Showcase skills and achievements. - Utilize job search feature, leading to dream job at Statefarm. Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180

Google is looking for Data Engineer Intern 👇👇 https://www.linkedin.com/posts/sql-analysts_google-intern-googleanalytics-activity-7144931636453847041-OgA_?utm_source=share&utm_medium=member_android

The best channel to learn about cryptocurrency and how it works 👇👇 https://t.me/Bitcoin_Crypto_Web

How Git Commands Work Git can seem confusing at first, but a few key concepts make it clearer: There are 4 locations for your code: - Working Directory - Staging Area - Local Repository - Remote Repository (like GitHub) Basic commands move code between these locations - git add stages changes - git commit saves them locally - git push shares them remotely - git pull fetches updates from others Branching allows isolated development. Concepts like git clone, merge, rebase enable collaboration. Graphical tools like GitHub Desktop also help by providing visual interfaces and shortcuts. While advanced workflows are possible, understanding this basic flow unlocks Git's power.