Shruti Verma

Launching Databricks Certified Data Engineer - Zero to Hero course on GeekCoders I yet to upload the videos in the course (ETA 15-June-2024) Here is the TOC: 1. Spark Introduction 2. Databricks Setup and Introduction 3. Read different types of files in Spark 4. Manipulate data using PySpark 5. Dbutils commands 6. Spark Optimisation methods 7. Delta lake feature 8. Utilities and Framework 9. Unity Catalog 10. Delta Live Tables 11. LLM - GenAI POC No one will give you everything in one course at very affordable price. Check out full video here and feel free to enroll https://lnkd.in/d76vWhGN Here is the link to the course https://lnkd.in/daGsXPFb Note: Share this post if you liked my free Databricks and Playlist course 😄 hashtag #databricks hashtag #azure hashtag #dataengineer

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Glad to hear 🤟 My Spark Streaming playlist on YouTube just helped someone to crack his JOB INTERVIEW 🤩 Congratulations Ravi Prakash 🏆 If you are reading this post, please tag yourself in the comments 😄 If you want to learn PySpark or Spark Streaming from basics to advanced Performance Optimization, check out the links in comments 👇 #spark #streaming #dataengineering

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These are the Pyspark functions which I use frequently as a Data Engineer 1. select(): Choose specific columns of interest. 2. filter(): Filter rows based on conditions. 3. groupBy(): Group your data based on columns. 4. agg(): Perform aggregations like sum, count, etc. 5. orderBy(): Sort your DataFrame with ease. 6. join(): Combine two DataFrames effortlessly. 7. union(): Merge DataFrames together, appending rows. 8. distinct(): Get unique rows from your DataFrame. 9. count(): Quickly count the rows in your DataFrame. 10. withColumn(): Add or replace columns in your DataFrame. 11. drop(): Easily drop unwanted columns. 12. na.fill(): Fill null or missing values rapidly. 13. na.drop(): Effortlessly drop rows with nulls. 14. groupBy().pivot(): Pivot columns like a pro! 15. explode(): Split columns into multiple rows. 16. when().otherwise(): Define conditional transformations. 17. cast(): Convert column data types seamlessly. 18. sum(): Compute the sum of values in a column. 19. avg(): Find the average of column values. 20. window(): Perform windowing operations in PySpark, Let me know in comments which functions you use very frequently ;) 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍 𝑬𝒗𝒆𝒓𝒚𝒅𝒂𝒚, 𝑰 𝒍𝒆𝒂𝒓𝒏 𝒂𝒏𝒅 𝒔𝒉𝒂𝒓𝒆 𝒔𝒕𝒖𝒇𝒇 𝒂𝒃𝒐𝒖𝒕: 🌀 Data Engineering 🌀 Python/SQL 🌀 Databricks/Pyspark 🌀 Azure 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dGDBXWRY 👉𝐅𝐨𝐥𝐥𝐨𝐰 Riya Khandelwal 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭.

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Interview Question - Pyspark!! **Interviewer:** So, can you explain what PySpark is? **Candidate:** Absolutely! PySpark is a Python API for Apache Spark, designed to enable Python programmers to interact with the Spark framework for distributed data processing tasks. **Interviewer:** What advantages does PySpark offer over traditional MapReduce? **Candidate:** PySpark offers several advantages over traditional MapReduce, including in-memory computation, lazy evaluation, support for multiple data sources, and a more user-friendly API. **Interviewer:** Could you explain the concept of RDD in PySpark? **Candidate:** Of course! RDD stands for Resilient Distributed Dataset, which is the primary abstraction in PySpark. It represents a distributed collection of objects that can be operated on in parallel, providing fault tolerance and immutability. **Interviewer:** How does lazy evaluation work in PySpark? **Candidate:** Lazy evaluation means that transformations in PySpark are not immediately executed when called. Instead, they are recorded and executed only when an action is called, which allows for optimizations such as pipelining and avoids unnecessary computation. **Interviewer:** What are the different ways to create RDDs in PySpark? **Candidate:** RDDs in PySpark can be created from existing Python collections, external data sources such as text files or databases, or by transforming existing RDDs using operations like map and filter. **Interviewer:** What is the role of broadcasting in PySpark? **Candidate:** Broadcasting in PySpark allows efficient distribution of read-only variables to all nodes in the cluster, reducing the amount of data shuffled over the network during computation. It's typically used for small lookup tables or variables used in multiple stages of computation. **Interviewer:** And finally, what is Spark SQL in PySpark? **Candidate:** Spark SQL is a module in PySpark that provides a programming interface for working with structured data. It allows users to run SQL queries, load data in various formats, and interact with data frames using a familiar SQL syntax, enabling seamless integration of SQL queries with PySpark applications. **Interviewer:** Thank you for the thorough explanations. Your understanding of PySpark seems quite solid. Do you have any experience working with it in a production environment? **Candidate:** Yes, I have worked on several projects where PySpark was used extensively for processing large volumes of data efficiently. I'm comfortable with its various components and have experience optimizing PySpark jobs for performance.

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🌟𝐖𝐞'𝐫𝐞 𝐇𝐢𝐫𝐢𝐧𝐠: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈 + 𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐬𝐞𝐚𝐫𝐜𝐡 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬! TekPillar®🌟 🔧𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 : Python API+ Elastic Search 📍𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧 : Bangalore  📅𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 : 5+ Years      #KeyResponsibilities: 👉Develop custom search and analytics applications using FastAPI and Elasticsearch. 👉Index data from various sources, including big data systems like Databricks and Snowflake. 👉Query Elasticsearch data and create robust REST APIs. Implement AI/ML use cases and optimize Elasticsearch performance. 👉Create reusable libraries for index templates, custom analyzers, and scripts. 👉Collaborate with data engineers on pipelines and ETL jobs. 👉Guide developers on search, filtering, and aggregation requirements. 👉Research and prototype new Elasticsearch features. 🔗 𝐀𝐩𝐩𝐥𝐲 𝐇𝐞𝐫𝐞:  📞8799447189 | ✉️ [email protected] #Python #Elasticsearch #FastAPI #BigData #Azure #TechJobs #Bangalore #Hiring #tekpillarjobs

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Important Pyspark Interview Question!! Interviewer: Can you explain what Adaptive Query Execution (AQE) is in the context of PySpark? Candidate: Yes, Adaptive Query Execution (AQE) is a feature introduced in Apache Spark to optimize query execution dynamically based on the runtime statistics of data. It essentially allows Spark to adapt its query execution plans during runtime to improve performance. Interviewer: That's correct. How does AQE work under the hood? Candidate: AQE employs runtime statistics, such as data skewness, data size, and data distribution, to make decisions about query execution. It dynamically adjusts various optimization strategies, such as join strategies, shuffle partitions, and broadcast joins, based on these statistics. For example, if it detects data skewness, it might choose a different join strategy to balance the workload across the cluster. Interviewer: Can you give me an example of how AQE can improve query performance in PySpark? Candidate: Certainly. Let's say we have a join operation between two DataFrames, and during runtime, AQE detects that one of the DataFrames is much smaller than the other. Instead of performing a regular shuffle join, AQE might decide to broadcast the smaller DataFrame to all the nodes in the cluster, reducing the shuffle data and improving performance. Interviewer: That's a good example. What are some of the key benefits of using AQE in PySpark? Candidate: One of the main benefits is improved query performance. By dynamically adjusting execution plans based on runtime statistics, AQE can optimize query performance for various workloads and data distributions. This can lead to faster query execution times and better resource utilization. Additionally, AQE can handle data skewness and other runtime issues more effectively, leading to more stable and predictable query performance. Interviewer: Excellent explanation. #pyspark #bigdata #dataengineer

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Week 10 of the "Ultimate Big Data Masters Program" provided an insightful journey into Apache Spark Optimizations and Performance Tuning - 1, provided by Sumit Mittal Sir. Here's a summary of Week 10 key learnings: ·       Pyspark Optimizations – Internals of groupBy ·       Normal Join Vs Broadcast Join ·       Different types of Joins ·       Partition Skew ·       Adaptive Query Execution (AQE) ·       Join Strategies ·       Optimizing Join of 2 large tables – Bucketing Thank you Sumit Mittal Sir and TrendyTech for all the support and guidance whenever needed. #Bigdata #Spark #Python #DAG #Join #dataengineer #AWS #Azure #ADLS #HDFS #Linux #SQL #athena #redshift

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Week 11 of the "Ultimate Big Data Masters Program" provided an insightful journey into Apache Spark Optimizations and Performance Tuning - 2, provided by Sumit Mittal Sir. Here's a summary of Week 11 key learnings: ·       Memory Management in Apache Spark ·       Sort Aggregate Vs Hash Aggregate ·       Apache Spark Logical and Physical Plans ·       Catalyst Optimizer ·       File Formats and Compression techniques ·       Specialized File Formats AVRO | ORC | Parquet ·       Schema Evolution Thanks to Sumit Mittal sir and TrendyTech team for this learning experience. #BigData #ApacheSparkProject #LendingClub #DataScience #LearningJourney #CareerGrowth #ApacheSpark

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Day 12 - 30-Day Data Engineering Interview Preparation Series PySpark (#Day12): 20 PySpark interview questions that can be relevant for a data engineering role 👇 📍 What is PySpark, and how does it differ from Apache Spark? 📍 Explain the architecture of Spark. 📍 How do you initialize a SparkSession in PySpark? 📍 What are RDDs in PySpark, and how do they work? 📍 What are the transformations and actions available in RDDs? 📍 How do you create RDDs in PySpark? 📍 What are DataFrames in PySpark, and how do they differ from RDDs? 📍 How do you perform operations on DataFrames? 📍 Explain the concept of a Dataset in PySpark. 📍 How do you work with SparkSQL in PySpark? 📍 Explain how to perform SQL operations on DataFrames. 📍 What are the advantages of using SparkSQL? 📍 How do you read and write data in various formats (CSV, JSON, Parquet) using PySpark? 📍 Explain the process of handling schema evolution in PySpark. 📍 What is Catalyst Optimizer in PySpark? 📍 How do you optimize a PySpark application? 📍 Explain the concept of lazy evaluation in PySpark. 📍 What is the significance of caching and persistence in PySpark? 📍 How do you handle skewed data in PySpark? 📍 What are broadcast variables and accumulators in PySpark? 🚨 We have launched our most affordable and industry-oriented "Complete Data Engineering 3.0 With Azure" bootcamp and ADMISSIONS ARE OPEN! 🔥 Only 7 days left and 80 seats remaining! 🚀 Enrollment link is in the first comment 👇 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar 🙂 #DataEngineering #SQL #Hive #Hadoop #Kafka #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure

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Here are the 3 PySpark questions!! Scenario 1: Data Joins Interviewer: "How would you join two DataFrames based on a common column, handling duplicate values and different data types?" Candidate: # Load the DataFrames df1 = spark.read.csv("path/to/data1.csv", header=True, inferSchema=True) df2 = spark.read.csv("path/to/data2.csv", header=True, inferSchema=True) # Join the DataFrames df_joined = df1.join(df2, df1["id"] == df2["id"], "inner") # Handle duplicate values df_joined_distinct = df_joined.dropDuplicates() # Handle different data types from pyspark.sql.functions import col df_joined_cast = df_joined.withColumn("column_name", col("column_name").cast("string")) # Save the joined DataFrame df_joined_cast.write.csv("path/to/joined/data.csv", header=True) Scenario 2: Data Filtering Interviewer: "How would you filter a DataFrame based on multiple conditions, including null values and date ranges?" Candidate: # Load the DataFrame df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True) # Filter null values df_filtered_null = df.filter(df["column_name"].isNotNull()) # Filter date range from pyspark.sql.functions import to_date, col df_filtered_date = df.filter((to_date(col("date_column")) >= "2022-01-01") & (to_date(col("date_column")) <= "2022-12-31")) # Combine multiple filters df_filtered = df.filter((df["column_name"].isNotNull()) & ((to_date(col("date_column")) >= "2022-01-01") & (to_date(col("date_column")) <= "2022-12-31"))) # Save the filtered DataFrame df_filtered.write.csv("path/to/filtered/data.csv", header=True) Scenario 3: Data Transformation Interviewer: "How would you transform a DataFrame by converting a column to uppercase, handling non-string values and encoding issues?" Candidate: # Load the DataFrame df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True) # Convert column to uppercase from pyspark.sql.functions import upper, col df_transformed = df.withColumn("column_name", upper(col("column_name"))) # Handle non-string values df_transformed_cast = df_transformed.withColumn("column_name", col("column_name").cast("string")) # Handle encoding issues df_transformed_encode = df_transformed_cast.withColumn("column_name", encode(col("column_name"), "utf-8")) # Save the transformed DataFrame df_transformed_encode.write.csv("path/to/transformed/data.csv", header=True) #pyspark #sql

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Day 23 - 30-Day Data Engineering Interview Preparation Series Snowflake (#Day23): 18 Snowflake practical interview questions that can be relevant for a data engineering role 👇 📍 How would you design a Snowflake data warehouse for a retail company with multiple product lines and sales channels? Provide an example schema and explain your design choices. 📍 Write a SQL query in Snowflake to find the top 5 products by sales volume for each month in the last year. 📍 Describe the steps to set up data replication between two Snowflake accounts in different regions. 📍 Explain how you would implement a data ingestion pipeline in Snowflake using Snowpipe for real-time data ingestion from AWS S3. 📍 You are given a large dataset with both structured and semi-structured data (JSON). How would you load this data into Snowflake and query it efficiently? 📍 How would you optimize a Snowflake query that is taking too long to execute? Describe the techniques and tools you would use. 📍 Create a stored procedure in Snowflake that automatically archives old data from a transactional table to a historical table. 📍 Explain how to use Snowflake’s Time Travel feature to recover data that was accidentally deleted from a table. Provide an example. 📍 Demonstrate how you would implement role-based access control (RBAC) in Snowflake to ensure that different user groups have appropriate access to data. 📍 Describe a strategy to handle slowly changing dimensions (SCD) Type 2 in Snowflake. Provide SQL examples. 📍 How would you set up a continuous data pipeline using Snowflake and an ETL tool (e.g., Apache NiFi, Talend) to process and load data from multiple sources? 📍 Write a Snowflake SQL script to partition a large table based on a specific column and explain the benefits of doing so. 📍 Explain how you would implement and manage a data mart in Snowflake for the marketing department, including data extraction, transformation, and loading (ETL) processes. 📍 How can you use Snowflake’s data sharing feature to share data with external partners securely? Describe the steps involved. 📍 Discuss how to use Snowflake’s clustering keys to improve query performance. Provide an example of creating and using a clustering key. 📍 How would you automate the process of scaling up and scaling down virtual warehouses in Snowflake based on workload? 📍 Describe the process to migrate an on-premises data warehouse to Snowflake. What are the key considerations and steps? 📍 Write a SQL query to detect and handle duplicate records in a Snowflake table. 🚨 We have opened a few more spots for admission in the "Complete Data Engineering 3.0 With Azure - Basic to Advanced" course. Join now! 👉 Enroll Here: https://lnkd.in/dZSAi93i 🔗Live Classes Starting on 1-June-2024 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary 😎 

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State of Data Engineering in 2024 - Jobs | Salary | Skills | Projects 📝 As data-driven decision-making continues to drive business success, the role of data engineers has become pivotal. Here's a snapshot of the key trends and findings from the latest report by Analytics India Magazine: 📈 Market Growth: The data engineering market in India is projected to grow at a CAGR of 33.8%, from USD 29.1 billion in 2023 to USD 124.7 billion by 2028. 🧑🏻‍💻 Job Trends: Demand for Senior Talent: Data engineers with 6+ years of experience are increasingly in demand, rising from 27% in 2023 to 38% in 2024. 🤑 Salary Growth: Significant increase in data engineers earning ₹6-10 lakhs, from 24% in 2023 to 32.4% in 2024. 🚶🏻‍♂️ Attrition Rates: Overall attrition rates have improved, dropping from 18% in 2023 to 14% in 2024. 🟢 Sector Highlights: ☎️ Telecom Boom: The Telecom sector saw a 12.5% increase in data engineer employment due to advancements in 5G and data analytics needs. 🏦 BFSI Sector: Despite a slight decline, BFSI remains a major employer of data engineers, reflecting ongoing digital transformation efforts. 🧑‍🎓 Skills in Demand: Proficiency in Python, SQL, ETL, and PySpark is crucial, with cloud platform skills (AWS, Azure, GCP) gaining prominence among mid-level engineers. 💸 Compensation Trends: While there has been an increase in mid-range salaries, experienced professionals are seeing adjustments due to economic conditions. 🌆 City-Specific Insights: Significant salary reductions in tech hubs like Bengaluru and Mumbai highlight changing compensation dynamics influenced by remote work trends. For detailed insights, visit AIM latest report. 📊🔍 #dataengineering #career #jobs

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30 Most Asked PySpark Interview Questions 👇 If you are preparing data engineering interviews, then you must go through the below PySpark interview questions: What is PySpark, and how does it differ from Spark? Explain the architecture of PySpark. What is RDD in PySpark? How is it different from DataFrame? What are the advantages of using DataFrame over RDD in PySpark? How do you create a data frame in PySpark? Explain lazy evaluation in PySpark. What are transformations and actions in PySpark? How do you handle missing or null values in PySpark DataFrames? Explain the concept of partitioning in PySpark. How can you improve the performance of PySpark jobs? What is the significance of caching in PySpark? How do you join DataFrames in PySpark? Explain the different types of joins available in PySpark. How do you handle duplicate records in PySpark DataFrames? What is the broadcast variable in PySpark? When is it used? Explain the concept of window functions in PySpark. How do you handle schema evolution in PySpark? What is the purpose of the persist() method in PySpark? How do you write data to external storage systems like HDFS or S3 using PySpark? What are the different ways to read data into a DataFrame in PySpark? Explain the significance of the groupBy() and agg() functions in PySpark. How do you perform aggregation operations on PySpark DataFrames? What is the role of the collect() function in PySpark? How can you handle skewed data in PySpark? Explain the concept of repartitioning in PySpark. How do you handle nested data structures in PySpark? What are the different deployment modes available in PySpark? How do you handle data skewness in PySpark? Explain the usage of UDFs (User Defined Functions) in PySpark. What are some best practices to optimize PySpark jobs for performance? I will keep updating the list going forward, so stay tuned. Happy Learning!! Do follow Aman Verma for more such content. #dataengineeringjobs #dataengineer #azuredataengineer #pyspark #interviewquestions

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#azure #Databricks #bigdata #dataanalyst #dataengineering Hi Connections, Today, we will understand: What is Checkpointing in Databricks?

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Top 5 best practices for optimal performance in Spark jobs. 1. Apply filters as early as possible when reading data. 2. Use partitioning and clustering to design your data storage layout for the best performance. The first one has no meaning if you don't have the second one. 🫠 3. Tune the shuffle partitions for your joins, aggregations or any other wide transformation. You should do it even if you have AQE. 4. Do not use Python UDF Instead, do it in Scala and call the Scala UDF from your PySpark code. 5. ANALYZE, OPTIMIZE, and VACUUM on a weekly schedule. These top 5 best practices should be on your tips and must be implemented for every project. Anything beyond this is a performance tuning and optimization activity. Applying PT&O requires analysing, diagnosing, benchmarking, tuning, measuring, and confirming skills. You can master all those skills with terabytes of data volume and 50+ scenario-based training. Check out my website for the Spark Performance Tuning and Optimization course. https://lnkd.in/gmJUhucv Keep Learning and Keep Growing.

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While scrolling through instagram, found an interesting pyspark question from Ankur Ranjan 's reel, and thought to give it a try, the question is detailed below : data =[(1, 40.7128, -74.0060, None),        (2, 34.0522,-118.2437, "Los Angeles"),        (3, 51.5074, -0.1278, None)] columns=["user_id", "latitude", "longitude", "city"] ''' 1. you need to find the city name for users with missing city information by calling an external geo-location API. 2. updare the dataframe with the fetched city names API detals GET https://lnkd.in/ehyncusw Response : { "city" : "CityName} ''' Now while solving the problem , API produced a connection error , so I referenced json data from dp-203 labs in github, because all we need to learn is how to retrieve the values from a json data based on the API response https://lnkd.in/eMAKfhQt there are several files with the same path but with different json file names So, I reframed this question for practice, where I am giving sales order number as input and retrieving the email address from the url response data=[("SO43705", 1, "2019-07-01", "Curtis Lu", "[email protected]" ),       ("SO43700", 1, "2019-07-01", "Ruben Prasad", None),       ("SO43701", 1, "2019-07-01", "Christy Zhu", None)] columns= ["SalesOrderNumber", "SalesOrderLineItem", "OrderDate", "CustomerName", "EmailAddress"]

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Hello Everyone..!!! I have been trying to write blogs for a long time but couldn't make it. Finally, I pushed myself to make it happen. I have chosen one of the best features of Apache Spark, which is partitioning. Please take a look at my view on partitioning. Open for any new suggestions....!!!!! 😊 #Azuredataengineering #AzureCloud #DataEngineering #ApacheSparkPartitioning #LetsLearnAndGrowTogether #LearnEveryday #GrowLikeNoOneCouldImagine #ThankyouForReading

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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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