Anurag Sharma’s Post

🚀 DE Interview📢Data Imputation⌛1-Min-Read 🧔♂️Interviewer: Can you explain how you improved data accuracy in your project, leading to significant business impact? 👶Candidate: In our project, we were facing issues with inaccurate business results due to missing values in our raw data layer. We knew which records were missing values and had a clear understanding of what those values should be. To tackle this, I developed a data imputation framework and created a new layer as a parallel pipeline. Here’s how I approached it: Data Imputation: I designed a framework that identified missing values and filled them with the appropriate data. This involved analyzing patterns and ensuring the imputed values were accurate and relevant. Parallel Pipeline: I set up a parallel pipeline to compare the imputed data with the actual data. This allowed us to validate the effectiveness of the imputation process. Comparison and Validation: By running the imputed data through the pipeline, we could see that the imputed data was much closer to the actual results compared to the raw data. Production Implementation: Due to the significant improvement in data accuracy, the team decided to move my imputation code into production. This ensured that business results were more accurate and reliable. By implementing this data imputation framework, we achieved several impacts: Increased Accuracy: The imputed data significantly reduced discrepancies, resulting in more accurate business insights. Improved Efficiency: Automating the imputation process saved time and resources, allowing the team to focus on more critical tasks. Enhanced Reliability: The production implementation of the imputation framework ensured consistent and reliable data quality. Real-time Validation: The parallel pipeline provided real-time insights, helping us quickly identify and address any data issues. Summary: 1️⃣ Problem: Inaccurate business results due to missing values in raw data. 2️⃣ Solution: Developed a data imputation framework and parallel validation pipeline. 3️⃣ Impact: Increased accuracy, improved efficiency, and enhanced data reliability. #DataEngineering #DataImputation #DataAccuracy #Automation #Python #DataValidation #TechInnovation #BusinessIntelligence #DataQuality #Efficiency

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗲𝗿𝗺𝘀 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄! Are you passionate about data engineering? Understanding these key terms will help you navigate the world of data pipelines, storage, and processing like a pro! ⚙ 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: An automated process that moves and prepares data. 💾 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: An organized collection of data for easy access. 📋 𝗦𝗰𝗵𝗲𝗺𝗮: The blueprint defining a database's structure. 💡 𝗧𝗮𝗯𝗹𝗲: A structured grid containing related data points. 🏠 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: A central hub for integrated data analysis. ⤵️ 𝗘𝗧𝗟: Extract, Transform, Load - The traditional way to extract, clean, and load data. ⤴️ 𝗘𝗟𝗧: Extract, Load, Transform - The modern approach of loading data first, then transforming it. 🏞️ 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: Massive storage for raw, unorganized data. ⏱️ 𝗕𝗮𝘁𝗰𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Processing data in large chunks at set times. ⏱️ 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Processing data in real-time as it arrives. 📊 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝘁: A specific slice of a data warehouse for a particular domain. 🔍 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Ensuring data accuracy, consistency, and reliability. 🕸️ 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Designing the logical structure and connections of data. 🌊 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Combines the flexibility of a data lake with a data warehouse's structure. 🎻 𝗗𝗮𝘁𝗮 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Coordinating and managing complex data workflows. 🔎 𝗗𝗮𝘁𝗮 𝗟𝗶𝗻𝗲𝗮𝗴𝗲: Tracing a data's origin and journey through its use. 👉 Follow Bahae Eddine HALIM 💻 for more resources! #data #python #sql #dataengineering #ai #programming #technology #coding #tech #database

𝐒𝐐𝐋 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐭 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝟏. 𝐖𝐡𝐲 𝐢𝐬 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? Error handling ensures consistent operations, prevents failures, and improves debugging for a better user experience. 𝟐. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐂𝐚𝐮𝐬𝐞𝐬 𝐨𝐟 𝐒𝐐𝐋 𝐄𝐫𝐫𝐨𝐫𝐬 Errors often stem from syntax mistakes, constraint violations, deadlocks, runtime issues, or connection problems. 𝟑. 𝐔𝐬𝐢𝐧𝐠 𝐓𝐑𝐘...𝐂𝐀𝐓𝐂𝐇 𝐁𝐥𝐨𝐜𝐤𝐬 TRY blocks execute SQL statements, while CATCH blocks handle any errors gracefully for seamless recovery. 𝟒. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭 𝐕𝐢𝐨𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Manage unique key or foreign key constraint errors effectively to maintain data integrity. 𝟓. 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐄𝐫𝐫𝐨𝐫𝐬 Capture error details in custom log tables for auditing and future debugging. 𝟔. 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 Combine transactions with error handling, avoid exposing technical errors, and test error scenarios during development. [Explore More In The Post] Follow Future Tech Skills Tech Skills for more such information and don't forget to save this post for later Join our group to discover more about Data Analytics, Data Science, Development & QA: https://lnkd.in/gUBDWHqe #sql #ErrorHandling #ExceptionHandling #sqlqueries #dataanalytics #datascience #dataanalyst

𝘔𝘺 𝘑𝘰𝘶𝘳𝘯𝘦𝘺 𝘪𝘯 𝘋𝘢𝘵𝘢 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨: 𝘓𝘦𝘢𝘳𝘯𝘦𝘥 𝘊𝘰𝘳𝘦 𝘚𝘬𝘪𝘭𝘭𝘴 When I started in data, I thought being a great data engineer was all about writing clean SQL and mastering its functions. While these are important, my experience over the years has shown me that truly effective data engineering requires much more. Here are the key skills I’ve learned along the way 👇 1. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐋𝐢𝐤𝐞 𝐚 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐯𝐞 🧩 One of the most critical skills I’ve developed is the ability to debug and analyze data pipelines effectively. This involves breaking a pipeline into smaller parts, understanding how the data flows through it, and identifying exactly where issues occur. Being able to pinpoint and resolve broken parts of a pipeline is essential—not just for fixing issues, but also for optimizing and improving performance. This skill extends beyond pipelines. Whether it’s debugging a system or tackling any complex problem, breaking it into manageable pieces, analyzing each part, and finding solutions is a universal and invaluable approach. 2. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐔𝐬𝐚𝐠𝐞 𝐚𝐧𝐝 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫 𝐍𝐞𝐞𝐝𝐬 A great data engineer doesn’t just build pipelines—they understand the broader context of the data. When a new data source appears, it’s important to ask: • How will this data be used? • What are the needs of stakeholders? • How can I build a scalable data warehouse without creating unnecessary complexity? By focusing on these questions, you can create systems that are purpose-driven and scalable without overengineering. Building 1,000+ models sounds impressive, but designing a lean, efficient, and scalable architecture is the real challenge. 3. 𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐂𝐥𝐞𝐚𝐫, 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐂𝐨𝐝𝐞 Code is the backbone of everything we do. It should be clean, maintainable, and designed to require minimal intervention in the future. As a data engineer, you need to anticipate that: • The needs and purposes of your data models may evolve. • Data volume may grow exponentially. These are just a few key skills I’ve learned as a data engineer, and the journey of mastering this field is ongoing. 𝐖𝐡𝐚𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞 𝐬𝐤𝐢𝐥𝐥𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐚 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐭𝐫𝐮𝐥𝐲 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥? I’d love to hear your thoughts and experiences in the comments below! #Data #DataEngineer #DataEngineering #DE #SQL #Code

Being a data engineer is more than just writing code or setting up pipelines, it's about solving problems every single day. There are certain challenges we face regularly, and if you're in this field, you’ll definitely relate. First off, dirty data is real headache. Data hardly ever comes clean. You’re dealing with missing values, duplicates, and inconsistent formats. Before you even think of working with the data, you’ll spend hours cleaning it, ensuring it's ready for processing. It's like doing laundry before you can wear your favorite outfit. Then, there’s the issue of pipeline failures. You can build the best ETL (Extract, Transform, Load) pipeline, but that doesn’t mean it’ll run smoothly forever. One small error in the source data, or a sudden change in a system you rely on, and boom, the whole thing crashes. As a data engineer, you have to be on alert, monitoring the system and fixing issues as they pop up. Another big challenge is scalability. As your company grows, so does the data. What worked when you had a small amount of data might not work when you’re dealing with terabytes of information. You need to always think ahead and build systems that can scale efficiently without collapsing under pressure. Lastly, communication with other teams can be tricky. You're not just working with data in isolation. You need to understand the needs of the business, the expectations from the data science team, and how the infrastructure team wants things to be structured. Sometimes, getting everyone on the same page can be a challenge in itself. These challenges are part of what makes data engineering both tough and rewarding. It’s not just about technical skills, it’s about problem solving, adapting, and constantly learning to keep up with the ever evolving data landscape. DataInno Analytics #dataengineering #dataengineer #etlpiplies #data

🚀 𝐃𝐄 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰📢 𝐃𝐀𝐆 𝐢𝐧 𝐒𝐩𝐚𝐫𝐤 🌟 🧔🏽♂️ Interviewer: Explain What is DAG in Spark and can control it. 👨🦰 Interviewee: The DAG (Directed Acyclic Graph) in Spark represents the logical execution plan of a Spark application. It's a directed graph that depicts the sequence of operations or transformations to be executed on the data. While we can't directly control the DAG's execution, we can influence it through optimizations and configuration settings. 🧔🏽♂️ Interviewer: Can you elaborate on how the DAG is generated and utilized in Spark applications? 👨🦰 Interviewee: Certainly! When we write Spark code, each transformation creates a new RDD (Resilient Distributed Dataset) or DataFrame, and each action triggers the execution of the DAG. Spark's Catalyst optimizer analyzes the DAG to optimize the execution plan, including optimizations like predicate pushdown and join reordering. 🧔🏽♂️ Interviewer: How can we optimize or control the DAG's execution for better performance? 👨🦰 Interviewee: While we can't directly control the DAG's execution sequence, we can influence it through various means. This includes using transformations like repartition and coalesce to control the data partitioning, caching or persisting intermediate results to avoid re-computation, and tuning Spark configuration parameters to optimize resource allocation and parallelism. 🧔🏽♂️ Interviewer: It sounds like the DAG plays a crucial role in Spark's execution. How does understanding the DAG benefit Spark developers? 👨🦰 Interviewee: Understanding the DAG helps developers write more efficient Spark code and optimize performance. By visualizing the execution plan and identifying potential bottlenecks or opportunities for optimization, developers can fine-tune their applications for better resource utilization, faster processing, and improved scalability. 🧔🏽♂️ Interviewer: Have you encountered any challenges related to the DAG in your Spark projects? 👨🦰 Interviewee: Yes, managing complex DAGs can sometimes lead to performance issues, especially with large-scale data processing or intricate transformations. Understanding how transformations impact the DAG and identifying opportunities for optimization are key to overcoming these challenges effectively. 🧔🏽♂️ Interviewer: How would you summarize the significance of the DAG in Spark applications? 👨🦰 Interviewee: The DAG is the backbone of Spark's execution model, orchestrating the flow of data transformations and actions. It's instrumental in optimizing performance, maximizing resource utilization, and ensuring the efficient execution of Spark applications. #OneStepAnalytics #DataAnalytics #BigData #SQL #DataProcessing #DataWarehouse #DataEngineering

Data Engineering Most Frequently asked Real time Interview Questions. 1. What cluster Manager you have used in your project ? 2. What is your cluster Size ? 3. How does your data comes to your storage location ? 4. What are the other sources you have used in your project ? 5. what is the sink for your project / 6. What is the frequency of the data in your source ? 7. What is the volume of your data ? 8. Please explain your project in detail ? 9. Lets say out of 100 task, 99 tasks completed however the last task is taking long hours to finish/complete, how to handle this issue ?1 10. What all challenges you have faced and how did you overcome from it ? 11. what optimization technique you have used in your project and what is the reason for it ? 12. Have you done spark optimization tuning ? If yes, how you have done that ? 13. Can you please walk me through the spark-submit command ? 14. Lets say you are getting your data volume is 100 GB , In your spark you are doing 5 Actions and 3 transformations on the data, explain what goes behind the scene with respect to Stages ,tasks? 15. how do you take your code to the higher environment ? 16. How do you schedule your job in production ? 17. How do you reprocess the data if it failed ? 18. Tell me one scenario you have gone wrong with your decision making and what you have learnt from that mistake ? 19. Lets say you have noticed duplicate records loaded in the table for the particular partition, how you resolved such issues ? 20. What is the frequency of your jobs ? 21. How do you notify your business/Stakeholders in case of any job failure? #DataEngineer #BigData, #PySpark, #Interview, #Spark

🌟 Day 10: My Data Analyst Journey 🌟 Today, I covered several key concepts in data analysis Like Data Cleaning and Transformation Techniques 🚫 Removing Duplicates: Ensuring data accuracy by eliminating duplicate entries. I used Python's drop_duplicates() function to clean my dataset effectively. 🔄 Handling Null Values: Techniques to manage and impute missing data for better analysis. I applied methods like fillna() and dropna() to handle null values in my data. 📊 GroupBy Methods: Aggregating data to uncover insightful patterns and trends. By using the groupby() method, I was able to summarize data and derive meaningful insights. ➕ Concatenation: Merging datasets seamlessly for comprehensive analysis. I utilized concat() to combine multiple DataFrames and enrich my dataset. 🔄 Pivoting and Melting: Transforming data shapes to suit different analytical needs. The pivot() and melt() functions helped me reshape my data for different perspectives. 🔄 Column Transformation: Applying operations to transform column data effectively. I demonstrated transforming columns using apply() and map() functions. 🔗 Compare and Merge: Comparing and merging datasets to enrich data insights. I showcased using merge() to combine datasets based on common columns. 🔗 Join: Combining data from multiple sources to create a unified dataset. I demonstrated different join types like inner, outer, left, and right joins using join(). 🚫 Handling Missing Values: Strategies to deal with incomplete data, ensuring robust analysis. I used various imputation techniques to handle missing values efficiently. These skills are foundational for any data analyst, helping to prepare data for meaningful analysis and visualization. Excited to apply these techniques to real-world data and see the impact they can make! 🚀 #DataAnalysis #DataCleaning #DataTransformation #LearningJourney #Day10 #Python #Pandas #DataPreparation #CareerGrowth

Data Engineering Most Frequently asked Real time Interview Questions. 1. What cluster Manager you have used in your project ? 2. What is your cluster Size ? 3. How does your data comes to your storage location ? 4. What are the other sources you have used in your project ? 5. what is the sink for your project / 6. What is the frequency of the data in your source ? 7. What is the volume of your data ? 8. Please explain your project in detail ? 9. Lets say out of 100 task, 99 tasks completed however the last task is taking long hours to finish/complete, how to handle this issue ?1 10. What all challenges you have faced and how did you overcome from it ? 11. what optimization technique you have used in your project and what is the reason for it ? 12. Have you done spark optimization tuning ? If yes, how you have done that ? 13. Can you please walk me through the spark-submit command ? 14. Lets say you are getting your data volume is 100 GB , In your spark you are doing 5 Actions and 3 transformations on the data, explain what goes behind the scene with respect to Stages ,tasks? 15. how do you take your code to the higher environment ? 16. How do you schedule your job in production ? 17. How do you reprocess the data if it failed ? 18. Tell me one scenario you have gone wrong with your decision making and what you have learnt from that mistake ? 19. Lets say you have noticed duplicate records loaded in the table for the particular partition, how you resolved such issues ? 20. What is the frequency of your jobs ? 21. How do you notify your business/Stakeholders in case of any job failure? #DataEngineer #BigData, #PySpark, #Interview, #Spark