Data Engineers

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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👍

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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 😊

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Data Analyst vs Data Engineer vs Data Scientist ✅ Skills required to become a Data Analyst 👇 - Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards. - SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data. - Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations. - Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards. - Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns. Skills required to become a Data Engineer: 👇 - Programming Languages: Strong skills in Python or Java for building data pipelines and processing data. - SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB. - Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets. - Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets. - ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration. Skills required to become a Data Scientist: 👇 - Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling. - Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras. - SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases. - Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis. - Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models. - Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models. Bonus Skills Across All Roles: - Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively. - Advanced Statistics: Strong statistical foundation to interpret and validate data findings. - Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context. - Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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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 👍👍

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These are the Top 5 Most Common SQL Questions for Data Engineering: 1. Total records after joining two tables on all types of joins 2. Rolling Sum and Nth salary based questions 3. Lag/Lead based questions e.g., consecutive months of increasing sales or YoY growth 4. Query to find employees who earn more than their managers 5. Removing duplicates from a table Key Takeaways: - Master window functions and joins - Practice medium to hard SQL questions regularly Getting good at SQL will pay off in the long run! 💪 Join our WhatsApp channel of Data Engineers: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

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Life of a Data Engineer..... Business user : Can we add a filter on this dashboard. This will help us track a critical metric. me : sure this should be a quick one. Next day : I quickly opened the dashboard to find the column in the existing dashboard's data sources.  -- column not found Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..). Then comes the pipeline changes , data model changes , dashboard changes , validation/testing. Finally deploying to production and a simple email to the user that the filter has been added. A small change in the front end but a lot of work in the backend to bring that column to life. Never underestimate data engineers and data pipelines 💪

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Data Science Packages

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Resolving OutOfMemory (OOM) Errors in PySpark: Best Practices 1️⃣ Adjust Spark Configuration (Memory Management) Increase Executor Memory: spark.conf.set("spark.executor.memory", "8g") Increase Driver Memory: spark.conf.set("spark.driver.memory", "4g") Set Executor Cores: spark.conf.set("spark.executor.cores", "2") Use Disk Persistence: df.persist(StorageLevel.DISK_ONLY) 2️⃣ Enable Dynamic Allocation Allow Spark to adjust executors: spark.conf.set("spark.dynamicAllocation.enabled", "true") spark.conf.set("spark.dynamicAllocation.minExecutors", "1") 3️⃣ Enable Adaptive Query Execution (AQE) Enable AQE to optimize query plans: spark.conf.set("spark.sql.adaptive.enabled", "true") 4️⃣ Enforce Schema for Unstructured Data Prevent schema inference overhead: df = spark.read.schema(schema).json("path/to/data") 5️⃣ Tune the Number of Partitions Repartition DataFrame: df = df.repartition(200, "column_name") 6️⃣ Handle Data Skew Dynamically Use salting for skewed joins: df1.withColumn("join_key_salted", F.concat(F.col("join_key"), F.lit("_"), F.rand())) 7️⃣ Limit Cache Usage for Large DataFrames Cache selectively, or persist to disk: df.persist(StorageLevel.MEMORY_AND_DISK) 8️⃣ Optimize Joins for Large DataFrames Use broadcast joins for smaller tables: df_join = large_df.join(broadcast(small_df), "join_key", "left") 9️⃣ Monitor Spark Jobs Use Spark UI to track memory usage and job execution. 🔟 Consider Partitioning Strategy Write partitioned data: df.write.partitionBy("partition_column").parquet("path_to_data") I have curated top-notch Data Engineering Interview Preparation Resources 👇👇 https://topmate.io/analyst/910180 All the best 👍👍