Amit Koner

Snowflake Administrator Good To Have Skills Talend Admin, Cost Optimization Must Have Skills DDL/DML, Snowflake, Role-Based Access Control (RBAC) Experience Level 5 - 7 Years Employee Type Salaried External Description Snowflake Administrator Acquired skills: Identify the various aspects of compute and storage management Illustrate administrative tasks within the detailed architecture of Snowflake Review Snowflake best practices & considerations for managing load operations and performance Describe data governance in Snowflake, including column-level data security using secure views and dynamic data masking features Manage multiple accounts across the Organization Describe the DDL operations that work with fundamental database objects Discuss transaction and concurrency models and DML considerations Employ recovery methods and agile development with Time Travel & Cloning Implement advanced techniques for Snowflake performance-tuning methodologies Design and develop secure access to objects in Snowflake with Role-Based Access Control (RBAC) Recommend the Snowflake best practices for management, monitoring, and optimization Use data replication for data sharing across accounts and for failover scenarios Share data securely both within and without your organization Detailed Snowflake Architecture and Overview.

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Have completed week 28 of the spark structured streaming part 2.Have covered the below topics in great details :- 1>Streaming Transformations 2>Triggers in spark structured streaming 3>Fault tolerance in spark structured streaming 4>Aggregation in spark structured streaming that is time bound and continuous 5>watermark feature in streaming and its use different output modes 6>Joins in streaming between two stream or one stream and one static table Have covered all these topics in much depth especially the fault tolarance ,aggregation and watermark related topics . Thanks to Sumit Mittal & TrendyTech for covering these in much depth. #dataengineering #streaming #sql #spark

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This question was asked for a Senior Data Engineer role at LTI - Larsen & Toubro Infotech. 📊 Given a JSON file with the following content: {"category_id":301, "product_ids":[30101, 30102, 30103]} {"category_id":302, "product_ids":[30201, 30202]} The data is loaded into a Spark DataFrame named products_df with the following schema: category_id: bigint product_ids: array<bigint> What is the best way to get each product_id individually along with the category_id? #dataengineering

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Common spark interview questions that might help for cracking interviews as well as for better learning!! Picking up a practical question related to spark i.e. : How to dynamically rename multiple columns in PySpark dataFrame ? Methods to dynamically rename multiple columns in PySpark data frame: i. Using loops : First obtain all the columns in the list using the columns function: total_columns=data_frame.columns Further, run a loop to dynamically rename multiple columns in Pyspark data frame using prefix, suffix or doing any other changes: for i in range(len(total_columns)): data_frame=data_frame.withColumnRenamed(total_columns[i], 'class_'+ total_columns[i]) #or data_frame=data_frame.withColumnRenamed(total_columns[i], total_columns[i].replace('_','__')) ii. Using reduce() function : Dynamically rename multiple columns in PySpark data frame using prefix, suffix or doing any other changes using reduce() function: import functools data_frame = functools.reduce(lambda data_frame, idx: data_frame.withColumnRenamed(list(data_frame.schema.names)[idx], list(data_frame.schema.names)[idx] + '_suffix'), range(len(list(data_frame.schema.names))), data_frame) Later on, dynamically rename multiple columns in PySpark data frame by replacing some characters using replace and reduce() function: data_frame = functools.reduce(lambda data_frame, idx: data_frame.withColumnRenamed(list(data_frame.schema.names)[idx], list(data_frame.schema.names)[idx].replace('#character','#other-character')), range(len(list(data_frame.schema.names))), data_frame) iii. Using the alias() function: Dynamically rename multiple columns in PySpark data frame using prefix, suffix or doing any other changes using alias: updated_columns = [col(col_name).alias("prefix_" + col_name + "_suffix") for col_name in data_frame.columns] v. Using toDF() function: Define the new column names which you want to give to all the columns: columns=['new_column_name_1','new_column_name_2','new_column_name_3'] Finally, use the function toDF() and assign the names to the data frame and display it: data_frame.toDF(*columns).show() #pyspark #interviewpreparation

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Completed Apache spark opitmising part 1 by Sumit Mittal TrendyTech - Big Data By Sumit Mittal Following topics we have covered: >Internals of groupBy >Normal Join vs Broadcast join  >Types of joins  >Join strategies >Partition skew >AQE (Adaptive query execution) >Optimizing join by bucketing for larger tables 

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🎯🎯 Tiger Analytics Data Engineering Interview challenges CTC - 20 LPA : Some challenging interview questions for a Data Engineer position at Tiger Analytics, targeting candidates with 2-5 years of experience: 1. ETL Pipelines and Data Transformation • Question: How would you design a scalable ETL pipeline to process large datasets in a distributed environment? What are the key considerations for performance optimization? • Focus: Knowledge of ETL tools (e.g., Apache Airflow, dbt), distributed computing (e.g., Hadoop, Spark), and performance tuning for large datasets. 2. Data Modeling • Question: Can you explain the differences between star schema and snowflake schema? In which scenarios would you use each, and how would you optimize these models for querying performance? • Focus: Data modeling concepts, database optimization, and schema design for business intelligence systems. 3. SQL Query Optimization • Question: Given a slow-running SQL query, how would you approach optimizing it? Can you walk us through the techniques you would apply in an OLAP environment? • Focus: SQL optimization, indexing, partitioning, query execution plans, and performance tuning. 4. Handling Data Quality Issues • Question: How do you handle data quality issues such as missing or corrupted data in your ETL pipelines? Can you share an example of how you’ve handled such situations in the past? • Focus: Data validation techniques, data cleaning, and ensuring high data quality in the pipeline. 5. Working with Real-Time Data • Question: How would you design a system for real-time data processing? What tools and frameworks would you use for stream processing, and how would you handle data consistency in real-time? • Focus: Real-time processing frameworks (e.g., Apache Kafka, Apache Flink), event-driven architectures, and data consistency challenges. 6. Data Storage Solutions • Question: How would you decide between different storage solutions (e.g., relational databases, NoSQL, data lakes) for a given use case? What are the trade-offs involved? • Focus: Knowledge of various storage technologies, their advantages and limitations, and selecting the appropriate one based on business needs. 7. Cloud Technologies • Question: What is your experience with cloud data engineering tools (e.g., AWS Redshift, Google BigQuery, Azure Synapse)? How do you manage scalability, performance, and cost optimization in the cloud? • Focus: Cloud platforms, data warehousing solutions, scalability, and cost management. 8. Version Control in Data Engineering • Question: How do you use version control in your data engineering projects? Can you explain the best practices for managing versioned data pipelines and code in Git? • Focus: Git for version control, collaboration, and managing versioned codebases. #dataengineering #sql #tigeranalytics #interview #bigdata #data

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Hello connections, As a part of my learning process at Inferenz , I started learning Snowflake badges a few days back. Today I am excited to share that I have earned Snowflake badge 1. The badge 1 helped me to get my hands on the following topics: 1) Snowflake Identity and Access Control. 2) Predefined roles of snowflake roles and hierarchy. 3) Creation and Dropping Databases. 4) Role Creations and Transfer the ownerships. 5) Understanding the concept of Worksheets. 6) Tables, Data types and loading data into the table. 7) Warehouses and their size. 8) Concept of file format. 9) Concept staging. 10) Data storage structure. 11) Concept of sequences. 12) Concept of semi-structured and nested semi-structure data. Link of the badge: https://sgq.io/fViwAmK #snowflake #data #dataengineering #technology #bigdata #datamining #datascience #inferenz #greatplacetowork #innovation #snowflakebadges #snowflakelearning

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This question was asked for a Senior Data Engineer role at LTI - Larsen & Toubro Infotech. 📊 Given a JSON file with the following content: {"category_id":301, "product_ids":[30101, 30102, 30103]} {"category_id":302, "product_ids":[30201, 30202]} The data is loaded into a Spark DataFrame named products_df with the following schema: category_id: bigint product_ids: array<bigint> What is the best way to get each product_id individually along with the category_id? hashtag #dataengineering

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PySpark Data Engineer Interview experience at Big 4 - KPMG KPMG India Deloitte EY PwC (4 years of experience) Follow Lokesh Tendulkar for insightful posts✨ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 1. Can you provide an overview of your experience working with PySpark and big data processing? 2. What motivated you to specialize in PySpark, and how have you applied it in your previous roles? 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐁𝐚𝐬𝐢𝐜𝐬: 3. Explain the basic architecture of PySpark. 4. How does PySpark relate to Apache Spark, and what advantages does it offer in distributed data processing? 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 5. Describe the difference between a DataFrame and an RDD in PySpark. 6. Can you explain transformations and actions in PySpark DataFrames? 7. Provide examples of PySpark DataFrame operations you frequently use. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐉𝐨𝐛𝐬: 8. How do you optimize the performance of PySpark jobs? 9. Can you discuss techniques for handling skewed data in PySpark? 𝐃𝐚𝐭𝐚 𝐒𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧: 10. Explain how data serialization works in PySpark. 11. Discuss the significance of choosing the right compression codec for your PySpark applications. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚: 12. How do you deal with missing or null values in PySpark DataFrames? 13. Are there any specific strategies or functions you prefer for handling missing data? 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐒𝐐𝐋: 14. Describe your experience with PySpark SQL. 15. How do you execute SQL queries on PySpark DataFrames? 𝐁𝐫𝐨𝐚𝐝𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐒𝐩𝐚𝐫𝐤: 16. What is broadcasting, and how is it useful in PySpark? 17. Provide an example scenario where broadcasting can significantly improve performance. 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 18. Discuss your experience with PySpark's MLlib. 19. Can you give examples of machine learning algorithms you've implemented using PySpark? 𝐉𝐨𝐛 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠: 20. How do you monitor and troubleshoot PySpark jobs? 21. Describe the importance of logging in PySpark applications. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐎𝐭𝐡𝐞𝐫 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: 22. Have you integrated PySpark with other big data technologies or databases? If so, please provide examples. 23. How do you handle data transfer between PySpark and external systems? 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: 24. Explain the project that you worked on in your previous organizations. 25. Describe a challenging PySpark project you've worked on. What were the key challenges, and how did you overcome them? #bigdata #data #spark #interviewtips #layoff #job #urgent #laidoff #help #everyone #hiring #technology #management #india

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PySpark Data Engineer Interview experience at Big 4 - KPMG KPMG India Deloitte EY PwC (4 years of experience) Follow Lokesh Tendulkar for insightful posts✨ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 1. Can you provide an overview of your experience working with PySpark and big data processing? 2. What motivated you to specialize in PySpark, and how have you applied it in your previous roles? 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐁𝐚𝐬𝐢𝐜𝐬: 3. Explain the basic architecture of PySpark. 4. How does PySpark relate to Apache Spark, and what advantages does it offer in distributed data processing? 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 5. Describe the difference between a DataFrame and an RDD in PySpark. 6. Can you explain transformations and actions in PySpark DataFrames? 7. Provide examples of PySpark DataFrame operations you frequently use. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐉𝐨𝐛𝐬: 8. How do you optimize the performance of PySpark jobs? 9. Can you discuss techniques for handling skewed data in PySpark? 𝐃𝐚𝐭𝐚 𝐒𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧: 10. Explain how data serialization works in PySpark. 11. Discuss the significance of choosing the right compression codec for your PySpark applications. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚: 12. How do you deal with missing or null values in PySpark DataFrames? 13. Are there any specific strategies or functions you prefer for handling missing data? 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐒𝐐𝐋: 14. Describe your experience with PySpark SQL. 15. How do you execute SQL queries on PySpark DataFrames? 𝐁𝐫𝐨𝐚𝐝𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐒𝐩𝐚𝐫𝐤: 16. What is broadcasting, and how is it useful in PySpark? 17. Provide an example scenario where broadcasting can significantly improve performance. 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 18. Discuss your experience with PySpark's MLlib. 19. Can you give examples of machine learning algorithms you've implemented using PySpark? 𝐉𝐨𝐛 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠: 20. How do you monitor and troubleshoot PySpark jobs? 21. Describe the importance of logging in PySpark applications. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐎𝐭𝐡𝐞𝐫 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: 22. Have you integrated PySpark with other big data technologies or databases? If so, please provide examples. 23. How do you handle data transfer between PySpark and external systems? 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: 24. Explain the project that you worked on in your previous organizations. 25. Describe a challenging PySpark project you've worked on. What were the key challenges, and how did you overcome them? #bigdata #data #spark #interviewtips #layoff #job #urgent #laidoff #help #everyone #hiring #technology #management #india

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Must Know HDFS Questions to Help You Get Ready for a Data Engineering Round!!! 👇 1. What is HDFS, and why is it crucial for handling Big Data? 2. Describe the HDFS architecture. What roles do the NameNode and DataNodes play? 3. How does HDFS ensure fault tolerance? Explain the replication factor. 4. What is the purpose of the Secondary NameNode? How does it differ from the standby NameNode? 5. How does HDFS handle large files and block storage? Why are block sizes typically large? 6. Explain data replication in HDFS. How does it work, and what are the benefits? 7. What is data locality in HDFS, and why is it important? 8. How does HDFS manage the “small files problem”? What strategies help reduce its impact? 9. What is Rack Awareness in HDFS? How does it improve data reliability? 10. Describe the steps for reading and writing data in HDFS. 11. How does erasure coding in Hadoop 3.0 compare to replication? What are the trade-offs? 12. What is high availability in HDFS, and why is it essential? 13. What are common HDFS commands for file management? (E.g., -put, -get, -ls) 14. How does HDFS handle access control and permissions? 15. Explain the role of DistCp in HDFS. How is it used for distributed data copy? Add more from your side!! #softwareengineering #dataengineering #bigdata #database #performance #distributedsystem #parallelcomputing #spark #python #datastructure #interview #hadoop #hdfs Sumit Mittal Andreas Zach Shashank Apache Spark Snowflake Databricks

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I attended almost 30+ interviews before Joining PwC and i found many questions have common among them 🎯........ Those questions include -- Explain Hadoop Architecture? What is 5 v's of big data? What is default replica in Hadoop? Can you increase or decrease it? Difference between Hadoop (Gen1) and Hadoop (Gen2)? What is heartbeats in hadoop? why is that important? Write down few Linux commands? What is partition, shuffling, sort in Mapreduce? What is Record Reader? Explain Sqoop Eval Command? Explain different optimizations used in Sqoop? Explain combiner in MapReduce? What is Yarn? Why is it used? Features of sqoop? Explain significance of them? Explain Boundary Val's Query? Explain the formula? Explain Modes available in Sqoop that used in job execution? Difference between Target Vs Warehouse directory? What is split by command? when it is used? Hive Architecture? Explain Transactional Processing Vs Analytical Processing? Difference between Hive and RDBMS? What is seek time in Hive? Difference between SQL Vs HQL? Explain UDF? How many types? What is views in hive? Explain Managed Table and External Table? Spark Architecture? What is transformations and actions? Name few? Intermediate to Advanced questions. Those Include -- Explain different no.of optimizations in hive? Explain types of Joins? What is Map side Join? What is Bucket Map Join and Sort Merge Bucket join(SMB)? Explain SCD Types in Hive? Explain File-formats in hive? Explain CAP Theorem? Explain RDD? Difference between RDD Vs Dataframe vs Dataset? Broadcast in Spark? Explain Catalyst optimizer? Difference between client Mode vs Cluster Mode? Explain Cache & persist? Explain Spark Performance Optimizations? Explain Accumulators? Bonus-- Additionally SQL and Coding questions are important. should have look at frequently asked questions before the interview. Let me know in the #comment section if it helpful If you like to know to my experience with PwC then do checkout the link in comment section Do follow Ajay Kadiyala ✅ #data #interviewexperience #interview #hbase #pwc #job #cloud #experience #comment #like #dataengineering

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Week - 2 of Ultimate Big Data Master's program continued learning around distributed storage in Hadoop which is HDFS, Cloud Based Data Lakes VS HDFS and distributed processing in MapReduce. Below are the key learnings achieved - 1. HDFS Architecture 2. Distributed Storage fundamentals in HDFS 3. Fault Tolerance and Scalability and HDFS 4. HDFS and Linux Commands 5. HDFS vs Cloud Data Lakes 6. Fundamentals of distributed processing All thanks to Sumit Mittal and TrendyTech for motivations in continuous learning and crystal-clear explanations. #bigdata #bigdataengineer #hdfs #distributedcomputing

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For Being Power BI Developer to get JOB first and work on Real Time Projects ✌️✌️✌️ Must 👇👇👇 DAX ✅ SQL ✅ Modelling ✅ Power BI Service ✅ Data Concepts ✅ BookMarks ✅ Understanding Logics ✅ Optional 👇👇👇 M Language ❌ Custom Visuals ❌ Excel ❌ Very Good English ❌ Power Automate ❌ Azure / Snowflake ❌ MSBI ❌ Focus on "MUST" part first...Learn it and then move to "OPTIONAL" part ✌️

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Snowflake Data Engineering SQL Interview Question. Asked in Cisco for 4 - 6 years of experience. Table: Seats +-------------+------+ | Column Name | Type | +-------------+------+ | seat_id | int | | free | int | +-------------+------+ seat_id is an auto-increment column for this table. Each row of this table indicates whether the ith seat is free or not. 1 means free while 0 means occupied. Find all the consecutive available seats in the cinema. Return the result table ordered by seat_id in ascending order. The test cases are generated so that more than two seats are consecutively available. -- Sample data creation CREATE TABLE seats ( seat_id INT, free INT ); INSERT INTO seats (seat_id, free) VALUES (1, 1), (2, 0), (3, 1), (4, 1), (5, 1), (6, 0), (7, 1), (8, 1), (9, 0), (10, 1), (11, 0), (12, 1), (13, 0), (14, 1), (15, 1), (16, 0), (17, 1), (18, 1), (19, 1), (20, 1); -- SQL query to get all consecutive free seats WITH seat_window AS ( SELECT seat_id, free, LAG(free, 1) OVER (ORDER BY seat_id) AS prev_seat, LEAD(free, 1) OVER (ORDER BY seat_id) AS next_seat FROM seats ) SELECT seat_id FROM seat_window WHERE (free = 1 AND (prev_seat = 1 OR next_seat = 1)); Output in the first comment. Follow Avinash S. Get new update Resources: https://lnkd.in/diKrpsRX 25% Off apply code: GETOFF25 Happy Ganesh Chaturthi All 🍫 #Dataengineering #Interviewprep #python #sql #interviews

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Snowflake Data Engineering SQL Interview Question. Asked in Cisco for 4 - 6 years of experience. Table: Seats +-------------+------+ | Column Name | Type | +-------------+------+ | seat_id | int | | free | int | +-------------+------+ seat_id is an auto-increment column for this table. Each row of this table indicates whether the ith seat is free or not. 1 means free while 0 means occupied. Find all the consecutive available seats in the cinema. Return the result table ordered by seat_id in ascending order. The test cases are generated so that more than two seats are consecutively available. -- Sample data creation CREATE TABLE seats (   seat_id INT,   free INT ); INSERT INTO seats (seat_id, free) VALUES (1, 1), (2, 0), (3, 1), (4, 1), (5, 1), (6, 0), (7, 1), (8, 1), (9, 0), (10, 1), (11, 0), (12, 1), (13, 0), (14, 1), (15, 1), (16, 0), (17, 1), (18, 1), (19, 1), (20, 1); -- SQL query to get all consecutive free seats WITH seat_window AS (   SELECT     seat_id,     free,     LAG(free, 1) OVER (ORDER BY seat_id) AS prev_seat,     LEAD(free, 1) OVER (ORDER BY seat_id) AS next_seat   FROM     seats ) SELECT   seat_id FROM   seat_window WHERE   (free = 1 AND (prev_seat = 1 OR next_seat = 1)); Output in the first comment. Follow Avinash S. Get new update Resources: https://lnkd.in/diKrpsRX 25% Off apply code: GETOFF25

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Data Engineering Interview question series - 1 1. Explain spark architecture 2. How you configure a spark cluster? 3. How do you decide the number of partitions? 4. Repartition vs Coalesce 5. What is RDD/dataframe/dataset? 6. Acid transactions 7. Spark performance optimization 8. Spark context vs Spark Session 9. Challenges faced 10. 2 to 3 SQL questions Follow Tharani T L for more such content 😊 #ApacheSpark #BigData #DistributedComputing #FaultTolerance #LinkedInLearning #DataPipelines #CloudComputing #DataWarehouse #ApacheSpark #PySpark #AzureDatabricks #DataIntegration #DataProcessing #DataStrategy #DataManagement #DataTransformation #DataEngineering #interviewquestions

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