Data Engineers

Here are 15 basic Linux commands you must know before starting your first full-time job or internship. Save this post for later. 1. How to create a new directory? A: mkdir 2. How to create new files? A: touch 3. How to print the current directory that you are in? A: pwd 4. How to list the contents of a directory? A: ls 5. How to move to a different directory? A: cd 6. How to preview the content of a file? A: cat 7. How to see the history of commands that you've used previously? A: history 8. How to search a pattern of text within a directory (dfs the whole subtree) using a regular expression? A: grep 9. How to stop a running process using it's process id? A: kill 10. How to change the permission of a file and directory? A: chmod 11. How to replace occurrences in a file? A: sed 12. How to output something on terminal (usually from inside of a scripts) A: echo 13. How to display the beginning for a text file? A: head 14. How to display the end of a text file? A: tail 15. How to copy files and directories? A: cp Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career. 📊Introduction to Data Engineering ✅Overview of Data Engineering & its importance ✅Key responsibilities & skills of a Data Engineer ✅Difference between Data Engineer, Data Scientist & Data Analyst ✅Data Engineering tools & technologies 📊Programming for Data Engineering ✅Python ✅SQL ✅Java/Scala ✅Shell scripting 📊Database System & Data Modeling ✅Relational Databases: design, normalization & indexing ✅NoSQL Databases: key-value stores, document stores, column-family stores & graph database ✅Data Modeling: conceptual, logical & physical data model ✅Database Management Systems & their administration 📊Data Warehousing and ETL Processes ✅Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema ✅ETL: designing, developing & managing ETL processe ✅Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue ✅Data lakes & modern data warehousing solution 📊Big Data Technologies ✅Hadoop ecosystem: HDFS, MapReduce, YARN ✅Apache Spark: core concepts, RDDs, DataFrames & SparkSQL ✅Kafka and real-time data processing ✅Data storage solutions: HBase, Cassandra, Amazon S3 📊Cloud Platforms & Services ✅Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure ✅Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake ✅Data storage & management on the cloud ✅Serverless computing & its applications in data engineering 📊Data Pipeline Orchestration ✅Workflow orchestration: Apache Airflow, Luigi, Prefect ✅Building & scheduling data pipelines ✅Monitoring & troubleshooting data pipelines ✅Ensuring data quality & consistency 📊Data Integration & API Development ✅Data integration techniques & best practices ✅API development: RESTful APIs, GraphQL ✅Tools for API development: Flask, FastAPI, Django ✅Consuming APIs & data from external sources 📊Data Governance & Security ✅Data governance frameworks & policies ✅Data security best practices ✅Compliance with data protection regulations ✅Implementing data auditing & lineage 📊Performance Optimization & Troubleshooting ✅Query optimization techniques ✅Database tuning & indexing ✅Managing & scaling data infrastructure ✅Troubleshooting common data engineering issues 📊Project Management & Collaboration ✅Agile methodologies & best practices ✅Version control systems: Git & GitHub ✅Collaboration tools: Jira, Confluence, Slack ✅Documentation & reporting Resources for Data Engineering 1️⃣Python: https://t.me/pythonanalyst 2️⃣SQL: https://t.me/sqlanalyst 3️⃣Excel: https://t.me/excel_analyst 4️⃣Free DE Courses: https://t.me/free4unow_backup/569 Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 All the best 👍👍

DevOps Tech Stack

Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. SQL Interview Resources: https://topmate.io/analyst/864764 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Python Interview Resources: https://topmate.io/analyst/907371 Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

Frequently asked SQL interview questions for Data Analyst/Data Engineer role- 1 - What is SQL and what are its main features? 2 - Order of writing SQL query? 3- Order of execution of SQL query? 4- What are some of the most common SQL commands? 5- What’s a primary key & foreign key? 6 - All types of joins and questions on their outputs? 7 - Explain all window functions and difference between them? 8 - What is stored procedure? 9 - Difference between stored procedure & Functions in SQL? 10 - What is trigger in SQL? 11 - Difference between where and having?

Complete topics & subtopics of #SQL for Data Engineer role:- 𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅: SQL keywords Data types Operators SQL statements (SELECT, INSERT, UPDATE, DELETE) 𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟): CREATE TABLE ALTER TABLE DROP TABLE Truncate table 𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟): SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs) INSERT statement UPDATE statement DELETE statement 𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: SUM, AVG, COUNT, MIN, MAX GROUP BY clause HAVING clause 𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: Primary Key Foreign Key Unique NOT NULL CHECK 𝟲. 𝗝𝗼𝗶𝗻𝘀: INNER JOIN LEFT JOIN RIGHT JOIN FULL OUTER JOIN Self Join Cross Join 𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀: Types of subqueries (scalar, column, row, table) Nested subqueries Correlated subqueries 𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER) Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD) Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD) Conditional functions (CASE, COALESCE, NULLIF) 𝟵. 𝗩𝗶𝗲𝘄𝘀: Creating views Modifying views Dropping views 𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀: Creating indexes Using indexes for query optimization 𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀: ACID properties Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT) Transaction isolation levels 𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Data integrity constraints (referential integrity, entity integrity) GRANT and REVOKE statements (granting and revoking permissions) Database security best practices 𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Creating stored procedures Executing stored procedures Creating functions Using functions in queries 𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Query optimization techniques (using indexes, optimizing joins, reducing subqueries) Performance tuning best practices 𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: Recursive queries Pivot and unpivot operations Window functions (Row_number, rank, dense_rank, lead & lag) CTEs (Common Table Expressions) Dynamic SQL Here you can find quick SQL Revision Notes👇 https://topmate.io/analyst/864817 Like for more Hope it helps :)

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