Ziyauddin Shaikh

🌟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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Hadoop vs Spark! Interviewer: There's a common belief that Hadoop has been replaced by Spark. Can you explain why this is a misconception? Candidate: This is a misconception because Hadoop and Spark serve different purposes within the big data ecosystem. Hadoop is an ecosystem that includes HDFS (storage), YARN (resource management), and MapReduce (processing). Spark is primarily a processing engine that can run on top of Hadoop's HDFS and YARN. While Spark has largely replaced Hadoop's MapReduce due to its faster in-memory processing capabilities, it has not replaced Hadoop's storage and resource management components. Interviewer: So, Spark complements rather than replaces Hadoop? Candidate: Exactly. Spark is designed to complement Hadoop by providing a more efficient and versatile processing engine. It can utilize Hadoop's HDFS for distributed storage and YARN for resource management, making them work together seamlessly. Interviewer: Can you elaborate on the technical differences between Hadoop MapReduce and Spark that led to Spark's popularity? Candidate: Certainly. The primary technical differences include: Performance: Spark performs in-memory computations, which makes it significantly faster than Hadoop MapReduce, which relies on disk-based processing. Ease of Use: Spark offers high-level APIs in Java, Scala, Python, and R, making it easier for developers to write and maintain code compared to the more complex Java code required for MapReduce. Unified Framework: Spark provides a unified framework for batch processing, real-time streaming, machine learning, and graph processing, whereas MapReduce is limited to batch processing. These advantages make Spark more suitable for a variety of data processing tasks, leading to its increased adoption over Hadoop MapReduce. Interviewer: What considerations should an organization take into account when transitioning from Hadoop MapReduce to Spark? Candidate: When transitioning from Hadoop MapReduce to Spark, organizations should consider the following: Compatibility: Ensure the existing Hadoop cluster is compatible with Spark, typically achieved through distributions like Cloudera or Hortonworks. Training: Provide adequate training for developers and data engineers to become proficient in Spark. Migration Planning: Develop a detailed migration plan, including testing and validation of Spark jobs to ensure they meet performance and accuracy requirements. Resource Management: Adjust resource allocation and configurations in YARN to optimize for Spark’s in-memory processing model. Gradual Transition: Start with less critical workloads to gain confidence before migrating more critical processes. By considering these factors, organizations can effectively transition to Spark while maintaining the benefits of the Hadoop ecosystem. #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis

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Hadoop vs Spark! Interviewer: There's a common belief that Hadoop has been replaced by Spark. Can you explain why this is a misconception? Candidate: This is a misconception because Hadoop and Spark serve different purposes within the big data ecosystem. Hadoop is an ecosystem that includes HDFS (storage), YARN (resource management), and MapReduce (processing). Spark is primarily a processing engine that can run on top of Hadoop's HDFS and YARN. While Spark has largely replaced Hadoop's MapReduce due to its faster in-memory processing capabilities, it has not replaced Hadoop's storage and resource management components. Interviewer: So, Spark complements rather than replaces Hadoop? Candidate: Exactly. Spark is designed to complement Hadoop by providing a more efficient and versatile processing engine. It can utilize Hadoop's HDFS for distributed storage and YARN for resource management, making them work together seamlessly. Interviewer: Can you elaborate on the technical differences between Hadoop MapReduce and Spark that led to Spark's popularity? Candidate: Certainly. The primary technical differences include: Performance: Spark performs in-memory computations, which makes it significantly faster than Hadoop MapReduce, which relies on disk-based processing. Ease of Use: Spark offers high-level APIs in Java, Scala, Python, and R, making it easier for developers to write and maintain code compared to the more complex Java code required for MapReduce. Unified Framework: Spark provides a unified framework for batch processing, real-time streaming, machine learning, and graph processing, whereas MapReduce is limited to batch processing. These advantages make Spark more suitable for a variety of data processing tasks, leading to its increased adoption over Hadoop MapReduce. Interviewer: What considerations should an organization take into account when transitioning from Hadoop MapReduce to Spark? Candidate: When transitioning from Hadoop MapReduce to Spark, organizations should consider the following: Compatibility: Ensure the existing Hadoop cluster is compatible with Spark, typically achieved through distributions like Cloudera or Hortonworks. Training: Provide adequate training for developers and data engineers to become proficient in Spark. Migration Planning: Develop a detailed migration plan, including testing and validation of Spark jobs to ensure they meet performance and accuracy requirements. Resource Management: Adjust resource allocation and configurations in YARN to optimize for Spark’s in-memory processing model. Gradual Transition: Start with less critical workloads to gain confidence before migrating more critical processes. By considering these factors, organizations can effectively transition to Spark while maintaining the benefits of the Hadoop ecosystem. #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis

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**𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐤𝐢𝐥𝐥𝐬 𝐰𝐢𝐭𝐡 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠!** A huge shoutout to my friends, Vishal Kaushal , a Snowflake Squad member, and Sachin Mittal, a 4x Snowflake Data Superhero, who are starting Snowflake Training: Zero to Hero course starting May 18th! . **Why Snowflake? ** Snowflake is more than just a data warehouse. It's a powerful, cloud-native data platform that empowers data engineers to build next-generation data solutions. Here's why learning Snowflake can be a game-changer for your career. By mastering Snowflake, you'll equip yourself with the skills to design and implement robust, scalable data solutions that are in high demand across industries. This positions you as a valuable asset in the ever-evolving world of data engineering. This comprehensive training, will equip you with the knowledge and skills to become a Snowflake master. Here's what you'll gain: Solid Foundation: Grasp the core concepts and functionalities of Snowflake. Hands-on Learning: Get practical experience through real-world exercises and projects. Unlimited doubt support Real time Projects : 2 End to End Real time projects , 1 using Matillion as well. Ready to take your data engineering skills to the next level? Don't miss out on this amazing opportunity! Sign up for the Snowflake Training: Zero to Hero course starting May 18th. 👉🏻 Enroll Here - https://lnkd.in/gcfxzNx4 📅 Live classes starts from 18th May 2024 Discount Coupon : LIVE20 PS : This is not a paid post. #Snowflake #DataEngineering #CloudDataWarehouse #Training #Upskilling #DataSuperhero

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**Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

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**Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

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Hi Folks, Here is today's Byte 💡: Powering Real-Time Data Analytics with Azure Databricks 🚧 : 🔹Facilitates collaboration among data scientists and engineers, enabling them to work together on big data analytics projects. 🔹Leverages Virtual Machine Scale Sets to provide scalable and high-performance compute resources for processing large datasets. 🔹Seamless integration with SQL Data Warehouse enables organizations to perform analytics on structured data using familiar tools and languages. 🔹 Integration with Event Grid enables event-driven analytics workflows, allowing organizations to derive real-time insights and automate actions based on data analysis results. Area of Implementation 👨‍💻 : ➡ A retail company utilizes Azure Databricks for real-time sales analytics during peak shopping seasons. ➡ Databricks accesses transactional data stored in SQL Data Warehouse to analyze sales trends, customer behavior, and inventory levels in real-time. ➡ Based on the analysis results, events are triggered via Event Grid to dynamically adjust pricing, replenish inventory, and personalize marketing campaigns in real-time. ➡ By leveraging Azure Databricks for real-time data analytics, the retail company enhances customer experience, optimizes inventory management, and maximizes revenue during peak shopping periods. Hope you find this Byte useful, do share your suggestions for any specific topic/concept 👍 Stay tuned for the next Byte... 🤓 #AzureBytes #azure #azurecloud #microsoft #microsoftazure #microsoftbuild #microsoftinspire #microsoftlearn #databricks #bigdata #analytics Azure Developer Community OpsTree Solutions

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Today's Learning ✌️ While going through one PBIX file. I saw that data was more than 5 Million Rows in Fact Table where Import Mode was used as Connectivity Mode. Snowflake Schema was used. But PBIX file was only of 25 MB. How Come? 🤔🤔🤔 Reason: ⬆️ 1) Data was Fetched of only last 6 months not all ✅ 2) Unwanted Columns where removed from Table ✅ 3) Grouping was done ✅ It looks very simple but Very Important observation real time work perspective 👍 Hope this helps 🙏 #powerbi #powerbideveloper

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It's one of the most trending Spark interview questions these days.  It is one of the most frequently asked questions amongst my 200K+ students. What is the question? Q: How will you tune/optimize a join between two large tables? What is your answer? The interviewers are not looking for the following five answers. 1. Broadcast (Not applicable because both the tables are huge) 2. Bucketing (Not applicable because one of the tables is a fact table that keeps on updating every day) 3. Use delta (Both the tables are already delta) 4. Analyze the table and the join key(This is already done) 5. Hash join (Not applicable as it is throwing an OOM exception) What else can you look into a terabyte join? Let's make it a little more complicated. 1. We have an SLA to finish this join in less than 5 minutes at the minimum cost. 2. Can you come up with some suggestions on how to achieve this query in less than 5 minutes? 3. How can you estimate the cost of running this terabyte join query in less than 5 minutes? These are not beginner questions, obviously. But who wants to hire a beginner data engineer? Master the skill of Apache Spark performance tuning with me. Visit my website for the course or call the WhatsApp number (Check out the video for contact details) Keep Learning and Keep Growing.

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In case you've missed my previous posts on EDA Here's the full blog on Exploratory Data Analysis (EDA): Read Here: https://lnkd.in/dYRybKzz #datascience

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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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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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Daatrbicks Optimization refers to the process of improving the performance and efficiency of data processing and analysis tasks in the Databricks environment. ☑Optimize & Z-order for Delta Tables ☑Auto Optimize in Delta Table ☑Partitioning ☑Broadcast hash join ☑Leverage cost-based optimizer Credits - Shwetank Singh 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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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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Wrapping up Milestone 1 with Week 15, a deep dive into “ Apache Hive “ provided by Sumit Mittal Sir and it's been an incredible journey so far! Here are some of the important topics covered: ·       Introduction to Apache Hive ·       Apache Hive Tables – Managed & External ·       Hive Optimizations – Partitioning & Bucketing ·       Hive Join Optimizations ·       Hive Transactional tables | ACID Properties ·       Insert only Transactional tables with ACID Properties ·       Spark-Hive Integration ·       Hive MSCK Repair       Thanks to Sumit Mittal sir and TrendyTech team for this learning experience. #Hive #Spark #BIGDATA #LearningJourney #TechInnovation #DataEngineering

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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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CLASSES in SCALA are pretty much like CLASSES in C++ & Other OBJECT ORIENTED PROGRAMMING LANGUAGE. #SCALA #BIGDATA #CPP #C++ Karthik K. object CLASS { class Point{ private var _x = 0 private var _y = 0 private val bound = 100 def x: Int ={ return _x} 👈 //GETTER for private var _x def y: Int ={ return _y} 👈 //GETTER for private var _y def coord( x:Int, y:Int)={ 👈 //SETTER for private vars _x & _y this._x = x 👆👇 /"This" keyword store address of the object calling the function this._y = y println(this._x,this._y) } } def main(args: Array[String]):Unit = { val a = new Point 👈 //Object creation of Point in Heap Memory a.coord(20,30) print(a) } } OUTPUT:

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Is it possible to get into Data Engineering or Data Analytics without having a coding background? ➡️ I chose Hindi over CS as an optional subject in class 11. The main reason was that I had very little interaction with computers till that time and everyone told me that even if you just miss out on a colon (:) in your program, it will not run. So I chose the option which was easy for me. ➡️ In college, I preferred Civil Engineering over CS or IT as Civil always had more opportunities in Indian Army. In Civil Engineering, our curriculum had one paper of C++ in one of the semesters where I hardly learned anything as I was not interested. Moving forward to 5 years after graduation, I worked on Data Analytics with Power BI and SQL as the primary tech stack. Then I moved to Data Engineering and worked with Azure services as my primary tech stack. Beside this, I also worked for backend development of applications with flask as the primary tech stack. I don't have a coding background, I believe I am still doing good in this field. If I can do it, anyone can 🧑‍💻 #dataanalytics #dataengineering

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Spark DAG: 📖 📢 Interviewer: Let's discuss the Directed Acyclic Graph (DAG) in Apache Spark. Could you explain what a DAG is and how it's utilized within Spark's execution model? ⏩ Candidate: Absolutely. In Apache Spark, a DAG is a logical representation of the sequence of transformations and actions that need to be performed on the input data to produce the desired output. It's a directed graph because each transformation or action is represented as a node, and the dependencies between them are represented as directed edges. Additionally, it's acyclic because there are no cycles in the graph, ensuring that the computation can be executed in a deterministic and efficient manner. 📢 Interviewer: That's a clear explanation. Now, how does Spark utilize this DAG internally during the execution of a job? ⏩ Candidate: When a Spark application is submitted, Spark parses the code and constructs a logical DAG based on the sequence of transformations and actions defined by the user. This logical DAG represents the high-level view of the computation to be performed. Then, Spark's Catalyst optimizer analyzes this DAG to apply various optimizations such as predicate pushdown, filter and projection pruning, and expression simplification to improve performance. Overall, Spark utilizes the DAG internally to efficiently orchestrate the execution of data processing tasks, optimize performance, ensure fault tolerance, and provide visibility into job progress and performance. 📢 Interviewer: Excellent overview. Could you provide more detail on how Spark's Catalyst optimizer operates on the DAG to optimize the execution plan? ⏩ Candidate: Certainly. The Catalyst optimizer performs a series of rule-based and cost-based optimizations on the logical DAG. It applies transformation rules to rewrite and simplify the DAG, eliminating redundant or unnecessary operations. For example, it might merge consecutive filter operations into a single filter, reorder operations for better performance, or push filters closer to the data source to minimize data movement. 📢 Interviewer: Fascinating insights . How does the DAG's structure contribute to performance optimization in Spark? ⏩ Candidate: The DAG's structure plays a crucial role in optimization by providing a high-level representation of the computation that allows Spark to reason about dependencies between transformations and actions. This enables Spark to apply optimizations at both the logical and physical levels. At the logical level, Spark can analyze the DAG to identify opportunities for optimization, such as common subexpression elimination or predicate pushdown. #bigdata #bigdataengineer #dataengineer #project #problemsolving #sql #hadoop #sql #python #dataengineering #jobsearch #spark #interview #resume #resumetips #hive #apachehive #optimization #interviewpreparation #interviewready #scenariobased

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