Chandradip Banerjee

20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !! 𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬 1. Data Processing and Transformation 2. Performance Tuning and Optimization 3. Data Pipeline Development 4. Debugging and Error Handling 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance? 2. How would you join two large datasets (say 100GB each) in PySpark efficiently? 3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark? 4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark? 5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios? 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 6. How do you debug and optimize PySpark jobs that are taking too long to complete? 7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance. 8. Describe a situation where you had to handle data skew in PySpark. What steps did you take? 9. How do you handle and optimize PySpark jobs in a YARN cluster environment? 10. Explain the difference between `repartition()` and `coalesce()` in PySpark. When would you use each? 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data. 12. How do you ensure data consistency and fault tolerance in a PySpark job? 13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark? 14. How would you orchestrate and manage a complex PySpark job with multiple stages? 15. Explain how you would handle schema evolution in PySpark while reading and writing data. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 16. Have you encountered out-of-memory errors in PySpark? How did you resolve them? 17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it? 18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this? 19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them? 20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results? 🤝 Follow 👨‍💻Abhisek Sahu for a regular curated feed of Data Engineering insights and valuable content! Save and repost✅ If you've made it this far, don't forget to hit the like 👍 button!! #pyspark #databricks #apachespark #dataengineering #dataengineer #bigdata

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20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !! 𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬 1. Data Processing and Transformation 2. Performance Tuning and Optimization 3. Data Pipeline Development 4. Debugging and Error Handling 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance? 2. How would you join two large datasets (say 100GB each) in PySpark efficiently? 3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark? 4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark? 5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios? 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 6. How do you debug and optimize PySpark jobs that are taking too long to complete? 7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance. 8. Describe a situation where you had to handle data skew in PySpark. What steps did you take? 9. How do you handle and optimize PySpark jobs in a YARN cluster environment? 10. Explain the difference between `repartition()` and `coalesce()` in PySpark. When would you use each? 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data. 12. How do you ensure data consistency and fault tolerance in a PySpark job? 13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark? 14. How would you orchestrate and manage a complex PySpark job with multiple stages? 15. Explain how you would handle schema evolution in PySpark while reading and writing data. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 16. Have you encountered out-of-memory errors in PySpark? How did you resolve them? 17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it? 18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this? 19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them? 20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results? Like and reshare ✅ ,If you find it useful !! 🔈 Join Data Engineering Community : https://lnkd.in/gy4R55Tj #dataengineering #dataengineer #questions #bigdata #spark #interviews

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20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !! 𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬 1. Data Processing and Transformation 2. Performance Tuning and Optimization 3. Data Pipeline Development 4. Debugging and Error Handling 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance? 2. How would you join two large datasets (say 100GB each) in PySpark efficiently? 3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark? 4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark? 5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios? 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 6. How do you debug and optimize PySpark jobs that are taking too long to complete? 7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance. 8. Describe a situation where you had to handle data skew in PySpark. What steps did you take? 9. How do you handle and optimize PySpark jobs in a YARN cluster environment? 10. Explain the difference between repartition() and coalesce() in PySpark. When would you use each? 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data. 12. How do you ensure data consistency and fault tolerance in a PySpark job? 13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark? 14. How would you orchestrate and manage a complex PySpark job with multiple stages? 15. Explain how you would handle schema evolution in PySpark while reading and writing data. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 16. Have you encountered out-of-memory errors in PySpark? How did you resolve them? 17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it? 18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this? 19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them? 20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results? #SQL #BigData #DataEngineering #Hadoop #HDFS #Sqoop #Hive #Spark #Snowflake #AWS #Azure #GCP #Interview

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20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !! 𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬 1. Data Processing and Transformation 2. Performance Tuning and Optimization 3. Data Pipeline Development 4. Debugging and Error Handling 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance? 2. How would you join two large datasets (say 100GB each) in PySpark efficiently? 3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark? 4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark? 5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios? 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 6. How do you debug and optimize PySpark jobs that are taking too long to complete? 7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance. 8. Describe a situation where you had to handle data skew in PySpark. What steps did you take? 9. How do you handle and optimize PySpark jobs in a YARN cluster environment? 10. Explain the difference between repartition() and coalesce() in PySpark. When would you use each? 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data. 12. How do you ensure data consistency and fault tolerance in a PySpark job? 13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark? 14. How would you orchestrate and manage a complex PySpark job with multiple stages? 15. Explain how you would handle schema evolution in PySpark while reading and writing data. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 16. Have you encountered out-of-memory errors in PySpark? How did you resolve them? 17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it? 18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this? 19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them? 20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results? #BigData #Hadoop #HDFS #DataStorage #DataEngineering #Hive #Sqoop #Spark #Snowflake #AWS #Azure #GCP #Interview

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PySpark Data Engineer Scenario-based Interview experience at Big 4 - KPMG India KPMG (4 years of experience) 𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏: Suppose you have a large dataset containing information about customer transactions, including customer ID, transaction amount, and transaction date. Your task is to calculate the total transaction amount for each customer for the year 2023. How would you approach this problem using PySpark? 𝑨𝒏𝒔𝒘𝒆𝒓: from pyspark.sql import SparkSession from pyspark.sql.functions import year, sum spark = SparkSession.builder \ .appName("Customer Transactions") \ .getOrCreate() df = spark.read.csv("customer_transactions.csv", header=True, inferSchema=True) df_2023 = df.filter(year(df["transaction_date"]) == 2023) total_transactions = df_2023.groupBy("customer_id") \ .agg(sum("transaction_amount").alias("total_transaction_amount")) \ .orderBy("total_transaction_amount", ascending=False) total_transactions.show() Better reach: Abhishek Jha Abhisek Sahu Shubham Wadekar Vaishnavi MURALIDHAR Sumit Mittal Follow Girish Gowda for more Interview Questions

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There are many students who ask me PySpark and Databricks course. I reply with simple roadmaps 1. Learn Databricks and PySpark from my YouTube channel or some other YouTube Channel 2. After completing 40% of PySpark, start working on projects Here is the 🆓 YouTube playlist : https://lnkd.in/g7m54R9Y Here is the course project: https://lnkd.in/dFy3vYkb #databricks #delta #pyspark Delta Lake Databricks

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Understanding How Logarithms Can Help You Evaluate Your Machine Learning Model Better! 📊🔍 When evaluating our regression models, the most commonly used metric is RMSE, which stands for Root Mean Square Error. RMSE is the square root of the average of the squared differences between actual and predicted values. RMSE works well until there are outliers in the target variable or if the target variable is highly skewed. To address these scenarios, we use RMSLE, which stands for Root Mean Squared Logarithmic Error. RMSLE applies a logarithmic transformation to both the actual and predicted values before calculating the square root of the average squared error. Here’s the formula: RMSLE = √(1/N∑(log(y+1) – log(y^+1)) ^2) When to Use RMSLE? 🤔 When the target variable is highly skewed and/or has outliers because logarithmic transformation normalizes the skewness of the data. When the error is in percentage because RMSLE outputs the error in terms of percentage, making it easier to interpret. Limitations ⚠️: RMSLE will fail if there are zero values in the target variable because log(0) is undefined. To counter this, we add 1 in the formula before performing the logarithmic transformation. While most of the time, RMSE will perform up to the mark, it is always beneficial to keep tools like RMSLE handy. 🛠️ Let me know if you have any thoughts on this! 💬 #machinelearning #datascience #modelevaluation Follow me on Sariq Sahazada for more such posts. 🙂

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Hi Community, I am back on my channel (to be regular this time ;)). Just shared my thoughts on how to build an analytics career. Freshers, Mid-Level Experienced and Experienced Folks can have a look I believe. Please put your comments in case you need any specific input in the field of Data Analytics/Data Science. Cheers!! #DataAnalytics #DataScience #Freshers #Mid-Level

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Hello, connections! 🚀🚀Today i am excited to announce a new series of SQL Interview questions from one of the top Companies in data domain. This series is in with collaboration with Nishant Kumar. Hope you all will benefit from this and hone your SQL skills. 🎯🎯 Have some other solution? Share it in the comments. ✔️✔️ --Question A table named “famous” has two columns called user id and follower id. It represents each user ID has a particular follower ID. These follower IDs are also users of hashtag #Facebook / hashtag #Meta. Then, find the famous percentage of each user. Famous Percentage = number of followers a user has / total number of users on the platform. 𝐒𝐜𝐡𝐞𝐦𝐚 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: CREATE TABLE famous (user_id INT, follower_id INT); INSERT INTO famous VALUES (1, 2), (1, 3), (2, 4), (5, 1), (5, 3), (11, 7), (12, 8), (13, 5), (13, 10), (14, 12), (14, 3), (15, 14), (15, 13);

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A very common mistake I see Analytics aspirants  Make in their Resume' Project Section -  They do not give the Problem Statement & Insights! When you are submitting your resume for applying to an Analytics role (or also roles like Data Scientist) - you need to treat your Project section as a mini-project presentation to a stakeholder/client At the end of day - Your stakeholder or client isn't only interested in the technical aspects of your project such as which libraries you used or which tools you used. They care about the key question you were solving and what insights you could bring for them from the data. Hence, if you're summarizing a project in your resume,  You can use the following structure (Also applicable when presenting in real) - 𝟭. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:  → What was the objective of the analysis or key questions you were looking to answer 𝟮. 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆:  → What was the process followed - steps, tools and/or any particular framework 𝟯. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: → What did the data tell you - Insights that help answer the question or support the objective 𝟰. 𝗜𝗺𝗽𝗮𝗰𝘁 (𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 - 𝗶𝗳 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀) → How did it help your end-user or improve any existing process - Always helpful to quantify the end-result to gauge effectiveness. Next time, if you're summarising a project in your resume, Think of it as - "How would you have presented this to someone in a real scenario in just few lines" P.S Follow me for more such powerful, relevant and actionable insights based on my conversations with industry professionals, to expand your analytics arsenal in 2024 #dataanalytics #resume #projectsection #projectpresentation #jobstrategies

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Hello Everyone......... Most asked interview question for Azure Data Engineer Role: What is the Difference Between Interactive Cluster and Job Cluster? Interactive Cluster: Purpose: Interactive clusters are designed for real-time data exploration, ad-hoc analysis, and development tasks. Usage: Development and Exploration: Ideal for data scientists and engineers who need to run interactive queries, develop and test Spark applications, or perform data exploration. Continuous Uptime: These clusters are usually kept running, allowing users to submit multiple jobs and queries over time without the need to reinitialize the cluster. *Resource Consumption*: May be more resource-intensive if kept running continuously, even when not in active use. Job Cluster: Purpose: Job clusters are designed for running a specific Spark job or a batch of jobs and are typically used in production environments for scheduled or on-demand workloads. Usage: Single Job Execution*: Created for the purpose of executing a single job or a set of jobs, and then terminated upon completion. Automated Workflows: Often used in automated data processing pipelines, ETL workflows, and other batch processing tasks. Resource Efficiency: Resources are allocated only for the duration of the job, making it more cost-effective for non-interactive, batch workloads.

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Hello, Connections ✨Here's an SQL interview question that was recently solved asked in one of Brillio PySpark Problem | Practice PySaprk Queries DATA Engineer INTERVIEW real time SQL interview problem... 𝗟𝗲𝘃𝗲𝗹: Medium 𝐃𝐚𝐲 4️⃣ 8️⃣ 𝐒𝐜𝐞𝐧𝐞𝐫𝐢𝐨: 𝙋𝙧𝙤𝙗𝙡𝙚𝙢 𝙨𝙩𝙖𝙩𝙚𝙢𝙚𝙣𝙩 Here we have a DataFrame containing employee details, including employee IDs (empid), departments (dept), salaries (salary), and dates (date). Your goal is to identify and retrieve the details of employees who appear exactly once in the DataFrame, i.e., employees whose empid is unique in the dataset. Expected Output: The result will be a DataFrame containing details of employees whose empid appears exactly once in the original dataset. For the given example data, this will provide the details of empid 200 (since empid 100 and 300 have multiple occurrences). 👇 Here is the my solution with Pyspark, SPARK_SQL Learning Challenge. 𝐇𝐚𝐩𝐩𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠!!! Regards! Together, we can grow and learn. Please share this again with your network. 𝐀𝐛𝐨𝐮𝐭 𝐦𝐞 Please do follow my LinkedIn profile link: R GANESH for more update… Please check out my GitHub project link: https://lnkd.in/gxjKWsXj Please check out my blogs Medium account link: https://lnkd.in/gDhRarfE Please check out my latest Post Instagram account for link: https://lnkd.in/gizfkVcy Please check out my latest updates Telegram Messenger for link: https://t.me/CareerBI Please check out any career assistance book slot topmate.io link: https://lnkd.in/gbauN-65

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#DATAENGINEERING_WITH_SNOWFLAKE H‍‍‍‍‍‍I all, #connections Here is the doc on data loading for semi structured data #snowflake #dataloading #semistructureddata #dataengineering #datascience I felt its little complicated, Hope I had formatted in detail with clear cut explanation. #snowflake_24 covered the following: 1. about semi structured data 2. process and understanding 3. load raw data and analyze nested objects 4. use of flatten function and transformation into table format in snowflake Thanks, #all

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🌟 Excited to share my latest project on the 2024 Indian Parliamentary Elections Using Tableau Desktop 🌟 I've delved into the data to provide comprehensive insights into this pivotal democratic event. Here are some key findings: 1️⃣ Total Constituency: Analyzed every constituency to provide a holistic view of the electoral landscape. 2️⃣ Total Votes & Electors 2024: From voter turnout to the total number of eligible electors, understanding the scale of participation is crucial. 3️⃣ Polling Percentage 2024: Dive into the data to see how many citizens exercised their right to vote and explore trends in participation. 4️⃣ Votes Percentage by Political Party Alliance: Uncover the distribution of votes among different political party alliances, shedding light on voter preferences and trends. 5️⃣ Top 20 Constituencies Won by the Leading Party: Discover the constituencies where the leading party secured victory, offering insights into their stronghold areas. 6️⃣ Winning Margin by Leading Candidate: Explore the margin by which leading candidates secured victory, providing insights into the competitiveness of the electoral race. 7️⃣ Total Votes by Parties: Breakdown of votes garnered by each political party, offering a detailed understanding of their electoral performance. This analysis offers valuable insights into the dynamics of the 2024 Indian Parliamentary Elections, enabling stakeholders to understand the electoral landscape comprehensively. Let's continue to leverage data-driven approaches for informed decision-making and strengthening democratic processes. Feel free to reach out for more insights or collaboration opportunities! https://lnkd.in/gPM7CiGu 📊✨ #ElectionAnalysis #DataVisualization #TableauInsights #IndianPolitics #tableau #BJP #NDA

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Amazon Pyspark interview questions for Data Engineer 2024. 1. How do you deploy PySpark applications in a production environment? 2. What are some best practices for monitoring and logging PySpark jobs? 3. How do you manage resources and scheduling in a PySpark application? 4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results). 5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark. 6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark? 8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue. 9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join? 10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data. 11. You are tasked with processing real-time sensor data to detect anomalies. Explain the steps you would take to implement this using PySpark. 12. Describe how you would design and implement an ETL pipeline in PySpark to extract data from an RDBMS, transform it, and load it into a data warehouse. 13. Given a requirement to process and transform data from multiple sources (e.g., CSV, JSON, and Parquet files), how would you handle this in a PySpark job? 14. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this. 15. Describe how you would use PySpark to join data from a Hive table and a Kafka stream. 16. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this.

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I am sharing the Executive Dashboard created with the Superstore Dataset. I aimed to make it as clean and informative as possible. I would appreciate it if you could leave some feedback. #Dashboard , #Tableau , #Visualization Reference : Youtube channels: #sqlbelle , #Andy Kriebel, #TableauTim and many more. https://lnkd.in/ePWMzCpE

🚀 How I Got PySpark Certified in 4 Months Recently, I earned my PySpark certification, and many people reached out asking about the resources I used and how much time it took. So, here’s my preparation journey from scratch: ⏳ Time Commitment 👉 Weekdays: ~1 hour daily 🕓 👉 Weekends: 3–4 hours for focused practice ⏰ 📚 Resources to Build Fundamentals ✨ Manish Kumar's PySpark YouTube Playlist This 26-video series is a goldmine for beginners. It took me about 2 weeks to complete while making notes. 🔗 Link: https://lnkd.in/gefVa876 ✨ Prashant Kumar Pandey's Udemy Course This course provides excellent explanations of Spark architecture and advanced PySpark concepts. It took me ~2 weeks to complete ⏳. 🔗 Link: https://lnkd.in/gF2K3xrw 💻 Practice & Problem-Solving 🔍 Databricks Practice Exam I solved problems, noted down new syntax, and practiced consistently. This took me around 1 week 📅. 🔗 Link: https://lnkd.in/gTBeUv8w I also referred to Manish Kumar's Practical PySpark Playlist for additional guidance 📚. 🔗 Link: https://lnkd.in/gy-iMwgK 🔍 ExamTopics Questions I reviewed and practiced ~150+ questions to strengthen my understanding. This step took me about 2 weeks ⏳. 🔗 Link: https://lnkd.in/g6bz9M4X 🔍 Udemy Practice Exams Course by Florian Roscheck I attempted three practice exams that covered a variety of topics 🔍 and helped me assess my readiness. This took me ~2 weeks. 💡 Tip: Scoring around 70 to 80 % on these is a good indicator you’re ready for the real exam. 🔗 Link: https://lnkd.in/g3NvkfV7 💡 Key Takeaways 👉 Keep detailed notes on syntax and revise them regularly 🔄. 👉 Use weekends for practice exams and weekdays for theory/revision 🔄. 👉 These resources not only prepare you for certification but also equip you for technical interviews 💼. I hope this helps in your PySpark learning journey ! Feel free to reach out if you have any questions 😊 #PySpark #DataEngineering #BigData #ApacheSpark #Databricks #DataScience #LearningJourney #CloudComputing #CertificationSuccess #TechCareer #CodingJourney #CareerGrowth #Programming #DataPipeline #SparkSQL

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🚀 Day 2 of the 30 Days, 30 SQL Questions Challenge! 🚀 Welcome to Day 2! Today, we delve into the dynamic world of IPL . Let's tackle an interesting SQL question: Write a SQL query to find players who have played for more than one team across different IPL seasons and compare their average runs and wickets per match for each team. Input Table       players: player_id, player_name  teams: team_id , team_name  player_match_stats: stat_id , player_id , match_id , team_id , runs , wickets Expected Output player_name , team_name , average_runs , average_wickets , matches_played I'm eager to see how you tackle it. Don't hesitate to share your solutions or alternative approaches in the comments. we all learn from each other's insights. So dive in, SQL enthusiasts! I can't wait to see your creative solutions! Let's get those queries rolling and see who can come up with the most efficient or innovative approach. Comment below with your SQL wizardry! Follow Digvijay Rajput for more. #SQL #DataAnalytics #30DaysSQLChallenge #Day1SQLChallenge

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SAS Tip: Getting Advanced with PROC MEANS to Show Quartiles and Median Looking to go beyond basic statistics in SAS? With PROC MEANS, you can add more detailed summary statistics like Q1 (25th percentile), Median (50th percentile), and Q3 (75th percentile) to your output. Here’s how: proc means data=new_dataset N mean std min Q1 median Q3 max;   var variable1 variable2; run; Output: This command generates a summary table with all the specified statistics for each variable listed in the VAR statement. The added quartiles and median give you a better picture of the data’s center and spread, especially helpful for understanding skewed distributions. Using PROC MEANS with quartiles and median is particularly valuable for: 🔹 Detecting Skewness: Compare median and mean—if they’re quite different, your data might be skewed. 🔹 Understanding Dispersion: Quartiles help you see how data is spread around the median. Try this next time you’re exploring a dataset. #SAS #DataScience #Statistics #Analytics #SASAdvanced #Quartiles #Median

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