Darshil Parmar

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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Spot the difference, Data Science hiring edition: Approach 1: - Entire tech stack on JD - Obsess over degree & years of experience - Critique Software Engineering skills on the whiteboard - Complain about lack of good candidates Approach 2: - Outline the needs & motives of DS in the role & company - Obsess over value-add & growth potential - Collaboratively assess problem-solving & communication - Make efficient & effective hires Don't do number 1. #datascience #analytics #data #datascienceinfinity

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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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🔥Top Hadoop Questions Answered 🔍 👷️ As a data engineer, you need to stay updated with the latest trends and challenges in big data. This blog post tackles some of the most popular questions in big data engineering today! 💪 What are the primary challenges in big data engineering? 1. 📈 Data Volume: Huge data generation poses challenges in storage, processing, and analysis. 2. 🌐 Data Variety: Diverse data types and structures require specialized tools for efficient management. 3. 🏃️ Data Velocity: Quick data generation demands real-time processing capabilities. 🧑‍💻 What skills are essential for big data engineers? 1. 💻 Proficiency in languages like Python, Java, or Scala 2. 🛠️ Understanding of big data technologies like Hadoop, Spark, and NoSQL databases 3. 🛠️ Experience with data processing tools like Apache Hive, Pig, and Kafka 4. 🔑 Strong grasp of data warehousing, ETL processes, and data modeling ☁️ What's the role of cloud computing in big data engineering? Cloud computing provides scalable and cost-effective infrastructure for big data management. Major cloud providers like AWS, Azure, and GCP offer big data services to manage data workloads efficiently. 🔒 How important is data privacy and security in big data engineering? Data privacy and security are crucial in big data engineering. Adhering to regulations and implementing robust security measures protect sensitive data and maintain trust. 📡 What's the future of big data engineering? Big data engineering's future will likely see increased adoption of AI and machine learning for advanced analytics, real-time processing, and decision-making. Cloud computing and IoT growth will also drive innovation in big data engineering. 📚 By staying informed about these questions, data professionals can excel in this rapidly evolving field! Good luck with your preparation! 🤞 ➕ Do Follow Dolly P. For more Data Engineering content #dataengineering #followme #pyspark #InterviewPrep #TechCareer #DataJobs #interviewskills #KnowledgeSharing #TechSkills #interview #ETL #dataengineerjobs #LearningResources #sql #cloudcomputing #interview #databricks #azuresynapse #azuredatabricks #azure #azuredataengineer #spark #sparksql  #azuredatafactory #deltalake #deltatable #connections #interviewtips #dataengineers #interviewsuccess #azurecloud #datacleaning #pyspark #optimization #BigData #azuredataengineer

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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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𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐢𝐬 𝐭𝐡𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈 for Apache Spark, designed for large-scale data processing. It's known for: 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Handling massive datasets across distributed computing clusters. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Optimized for high-performance data processing with features like in-memory computing. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Seamlessly integrates with big data frameworks and tools. 𝐁𝐞𝐬𝐭 𝐟𝐨𝐫: Large-scale data processing tasks that require distributed computing and efficient handling of vast amounts of data. 𝐏𝐚𝐧𝐝𝐚𝐬 is a go-to library for data manipulation and analysis in Python. It's widely used for: 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Efficiently handling data in-memory with powerful data manipulation capabilities. 𝐑𝐢𝐜𝐡 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲: Comprehensive functions for data cleaning, transformation, and aggregation. 𝐄𝐚𝐬𝐞 𝐨𝐟 𝐔𝐬𝐞: Intuitive API and excellent support for smaller datasets. Best for: Small to medium-sized datasets that fit into memory and require intricate data manipulation. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐂𝐡𝐨𝐨𝐬𝐞 𝐖𝐡𝐢𝐜𝐡? For Smaller, In-Memory Data Tasks: Use Pandas for its ease of use and rich functionality. For Large-Scale Data Processing: Opt for PySpark to leverage its distributed computing power and scalability. doc credit - Shwetank Singh #aws #dataengineering ✅ Enrollments for Version 3 of scenario based structured hands-on Real-world AWS Data Engineering ( RADE™) program 𝐖𝐈𝐋𝐋 𝐂𝐋𝐎𝐒𝐄 𝐕𝐄𝐑𝐘 𝐒𝐎𝐎𝐍! 𝑵𝒆𝒘 𝑨𝑾𝑺 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑩𝒂𝒕𝒄𝒉 𝒕𝒐 𝒔𝒕𝒂𝒓𝒕 𝑱𝒖𝒍𝒚 28𝒕𝒉- 𝐉𝐨𝐢𝐧 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 60 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 𝐰𝐡𝐨 𝐡𝐚𝐯𝐞 𝐞𝐧𝐫𝐨𝐥𝐥𝐞𝐝. ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝, 𝐫𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐎𝐖 for FREE 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐦𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬- 𝐥𝐨𝐨𝐤 𝐚𝐭 𝐜𝐨𝐦𝐦𝐞𝐧𝐭 𝐛𝐞𝐥𝐨𝐰!

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𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐢𝐬 𝐭𝐡𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐀𝐏𝐈 for Apache Spark, designed for large-scale data processing. It's known for: 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Handling massive datasets across distributed computing clusters. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Optimized for high-performance data processing with features like in-memory computing. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Seamlessly integrates with big data frameworks and tools. 𝐁𝐞𝐬𝐭 𝐟𝐨𝐫: Large-scale data processing tasks that require distributed computing and efficient handling of vast amounts of data. 𝐏𝐚𝐧𝐝𝐚𝐬 is a go-to library for data manipulation and analysis in Python. It's widely used for: 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Efficiently handling data in-memory with powerful data manipulation capabilities. 𝐑𝐢𝐜𝐡 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲: Comprehensive functions for data cleaning, transformation, and aggregation. 𝐄𝐚𝐬𝐞 𝐨𝐟 𝐔𝐬𝐞: Intuitive API and excellent support for smaller datasets. Best for: Small to medium-sized datasets that fit into memory and require intricate data manipulation. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐂𝐡𝐨𝐨𝐬𝐞 𝐖𝐡𝐢𝐜𝐡? For Smaller, In-Memory Data Tasks: Use Pandas for its ease of use and rich functionality. For Large-Scale Data Processing: Opt for PySpark to leverage its distributed computing power and scalability. P.S ✅Version 3 of 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐑𝐞𝐚𝐥-world AWS Data Engineering ( RADE™) program 𝐰𝐢𝐭𝐡 80+ 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 completed on Oct 5th. Onboarding for the Next one to begin in November. ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 and want to get in the next batch, fill the form below to be get invited for the webinar for RADE™ program V4 Click https://lnkd.in/ervbN-zt 𝐔𝐒𝐏: 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 program ! doc credit - Shwetank Singh #aws #dataengineering

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Hadoop Vs Spark Let's explore a few scenarios!! Scenario 1: Interviewer: We've discussed how Spark replaced MapReduce in many data processing workflows. Can you explain a scenario where Spark's in-memory processing model provides a significant performance advantage over Hadoop MapReduce? Candidate: Certainly. Consider a scenario where we need to perform iterative machine learning algorithms, such as training a large-scale model using gradient descent. In Hadoop MapReduce, each iteration involves reading the data from disk, processing it, and writing intermediate results back to disk, incurring significant overhead due to disk I/O operations. In contrast, Spark keeps the data in memory between iterations, eliminating the need for costly disk reads and writes. This allows Spark to perform iterative algorithms much faster than MapReduce, making it well-suited for machine learning tasks. Interviewer: That's a clear illustration of Spark's performance advantage in iterative workloads. Thank you for explaining. Scenario 2: Interviewer: Let's discuss the versatility of Spark compared to Hadoop MapReduce. Can you provide an example of a scenario where Spark's support for multiple processing modes enables users to address diverse analytical use cases? Candidate: Absolutely. Consider a scenario where an organization needs to build a real-time analytics dashboard to monitor key performance metrics in their business operations. In this scenario, Spark's support for streaming data processing with Spark Streaming allows the organization to ingest, process, and analyze streaming data in real-time. By integrating streaming analytics with batch processing and machine learning using the same Spark framework, the organization can build an end-to-end data pipeline to address both real-time and historical analytics use cases seamlessly. Interviewer: That's a compelling example of how Spark's versatility enables organizations to address a wide range of analytical use cases. Thank you for sharing. Scenario 3: Interviewer: Let's discuss Spark's programming model and its impact on developer productivity. Can you provide an example of how Spark's high-level APIs simplify the development of complex data processing workflows? Candidate: Certainly. Consider a scenario where we need to perform complex data transformations and aggregations on a large dataset. With Spark's DataFrame API, developers can express these transformations using high-level operations like filter, groupBy, and agg, which abstract away the underlying details of distributed processing. This allows developers to write concise and expressive code, reducing the complexity of data processing workflows and accelerating development. Additionally, Spark's support for multiple programming languages such as Scala, Python, and Java enables developers to work with their preferred language! Interviewer: That's an excellent demonstration. #bigdata #dataengineering

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I Have attended 50+ Data Interviews to date ( Both as an interviewer & interviewee ) And, these are the most common Data Structures and Algorithms problems that data folks must definitely know : 1. **Two Sum** 🔢 2. **Reverse Linked List** 🔄 3. **Merge Two Sorted Lists** ➕ 4. **Best Time to Buy and Sell Stock** 📈 5. **Valid Parentheses** 🔧 6. **Maximum Subarray sum** 🧮 7. ** Palindrome check ** 8. **Longest Substring Without Repeating Characters** 📏 9. **Find Minimum in Rotated Sorted Array** 🔍 10. **Climbing Stairs** 🪜 11. **Binary Search** 🔎 12. ** Anagram check ** 13. ** Generate Permutations ** 14. ** Generate Subsets ** 15. ** Majority Element** 👥 16. ** Basic Stack Problems ** 17. **Add Two Numbers** ➗ 18. ** Basic Queue Problems ** 19. **Valid Anagram** 🔠 20. **Merge Intervals** 📅 For FREE Data Engineering learning content and 1:1 connect, checkout my website ( Below my name ) !! #data #dataEngineering #hiring

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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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God morning connectio🌻 Here are five advanced MySQL functions and their explanations: 1. What is the COALESCE() function in MySQL? :- The COALESCE() function returns the first non-null value in a list of arguments. It's useful for handling null values in queries. For example, `SELECT COALESCE(column1, column2, 'Default Value') FROM table;` will return the first non-null value from column1 and column2, or 'Default Value' if both are null. 2. How does the CASE statement work in MySQL? :- The CASE statement allows you to perform conditional logic in your queries. It can return different values based on specific conditions. For instance: ```sql SELECT name, CASE WHEN salary < 30000 THEN 'Low' WHEN salary BETWEEN 30000 AND 60000 THEN 'Medium' ELSE 'High' END AS salary_category FROM employees; ``` This query categorizes salaries into 'Low', 'Medium', or 'High'. 3. What is the purpose of the GROUP_CONCAT() function? :- The GROUP_CONCAT() function concatenates values from multiple rows into a single string. It's particularly useful for creating comma-separated lists. For example: ```sql SELECT department, GROUP_CONCAT(employee_name) AS employees FROM employees GROUP BY department; ``` This will give you a list of employee names for each department. 4. Explain the use of the RANK() function in MySQL. :- The RANK() function assigns a unique rank to each row within a partition of a result set, based on the specified column values. Rows with the same values receive the same rank, and the next rank is skipped. For example: ```sql SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rank FROM employees; ``` This will rank employees based on their salaries in descending order. 5. What is the purpose of the JSON functions in MySQL? :- MySQL provides various JSON functions to handle JSON data types. Functions like JSON_EXTRACT(), JSON_ARRAY(), and JSON_OBJECT() allow you to manipulate and query JSON data stored in tables. For example: ```sql SELECT JSON_EXTRACT(data, '$.name') AS name FROM users; ``` This will extract the 'name' field from a JSON column named 'data'. #data #analytics #mysql #server #hr #motivated

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After my Databricks and PySpark free Youtube course. I'm launching Databricks Certified Data Engineer - Zero to Hero paid course. You might be confused what will be covered in this course? Will I get job after enrolling in this course? Answer is You can if you follow all videos step by step I will covering everything which is required to be Databricks Data Engineer ✅ Introduction of Spark ✅ Introduction of Databricks ✅ Read Multiple files like CSV, PDF, Parquet, etc using PySpark ✅ Manipulate the data using Pyspark - Sort, Add columns, Delete rows ✅ Delta Lake feature ✅ Optimisation in Spark - Cache, Salting, Broadcast,etc ✅ Unity Catalog ✅ DeltaLive Tables ✅ Batch vs Streaming ✅ Utilities and Framework ✅ GenAI Minor Project ✅ Deployments Still I'm making videos and trying to provide you all videos ASAP but it will take time. Fees is very very affordable and it includes everything from zero to hero Check out this course link here: https://lnkd.in/daGsXPFb Promo code: DBX15 to get 10% off (Early discount)

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Learning #PySpark can potentially help 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐲𝐨𝐮𝐫 𝐬𝐚𝐥𝐚𝐫𝐲 in the following ways: 𝘏𝘪𝘨𝘩 𝘥𝘦𝘮𝘢𝘯𝘥 𝘢𝘯𝘥 𝘴𝘢𝘭𝘢𝘳𝘺 𝘱𝘳𝘦𝘮𝘪𝘶𝘮𝘴: According to the O'Reilly Data Science Salary Survey, people with Apache Spark skills added $11,000 extra to the median salary. Apache Spark developers earn among the highest average salaries compared to other Hadoop development tools. 𝘚𝘢𝘭𝘢𝘳𝘺 𝘳𝘢𝘯𝘨𝘦𝘴: PySpark Developer salaries in India range from ₹3.5 Lakhs to ₹17.0 Lakhs per year, with an average of ₹8.6 Lakhs. In the US, Spark Developer salaries in San Francisco average around $128,000 per year, which is 35% higher than the national average. Growing job market: PySpark jobs have more than doubled since 2016 in some markets. As of 2015, there were over 34,000 job listings for "Spark Developer" on Dice com. 𝘍𝘶𝘵𝘶𝘳𝘦 𝘨𝘳𝘰𝘸𝘵𝘩 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭: As big data continues to grow, skills in distributed processing frameworks like Spark are likely to remain valuable. P.S ✅Version 3 of 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐑𝐞𝐚𝐥-world AWS Data Engineering ( RADE™) program 𝐰𝐢𝐭𝐡 80+ 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 started on July 28th; 7 𝐒𝐎𝐋𝐈𝐃 𝐇𝐀𝐍𝐃𝐒 𝐎𝐍 𝐒𝐄𝐒𝐒𝐈𝐎𝐍s 𝐀𝐑𝐄 𝐃𝐎𝐍𝐄! ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 and want to get in the next batch, fill the form below to be get invited for the webinar for RADE™ program V4 Click https://lnkd.in/ervbN-zt 𝐔𝐒𝐏: 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 program ! doc credit - unknown #aws #dataengineering

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Learning #PySpark can potentially help 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐲𝐨𝐮𝐫 𝐬𝐚𝐥𝐚𝐫𝐲 in the following ways: 𝘏𝘪𝘨𝘩 𝘥𝘦𝘮𝘢𝘯𝘥 𝘢𝘯𝘥 𝘴𝘢𝘭𝘢𝘳𝘺 𝘱𝘳𝘦𝘮𝘪𝘶𝘮𝘴: According to the O'Reilly Data Science Salary Survey, people with Apache Spark skills added $11,000 extra to the median salary. Apache Spark developers earn among the highest average salaries compared to other Hadoop development tools. 𝘚𝘢𝘭𝘢𝘳𝘺 𝘳𝘢𝘯𝘨𝘦𝘴: PySpark Developer salaries in India range from ₹3.5 Lakhs to ₹17.0 Lakhs per year, with an average of ₹8.6 Lakhs. In the US, Spark Developer salaries in San Francisco average around $128,000 per year, which is 35% higher than the national average. Growing job market: PySpark jobs have more than doubled since 2016 in some markets. As of 2015, there were over 34,000 job listings for "Spark Developer" on Dice . com. 𝘍𝘶𝘵𝘶𝘳𝘦 𝘨𝘳𝘰𝘸𝘵𝘩 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭: As big data continues to grow, skills in distributed processing frameworks like Spark are likely to remain valuable. P.S ✅Version 3 of 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐑𝐞𝐚𝐥-world AWS Data Engineering ( RADE™) program 𝐰𝐢𝐭𝐡 80+ 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 completed on Oct 5th. Onboarding for the Next one has begun. ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 and want to get in the next batch, fill the form below to be get invited for the webinar for RADE™ program V4 Click https://lnkd.in/ervbN-zt 𝐔𝐒𝐏: 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 program ! doc credit - unknown #aws #dataengineering

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**Mastering Data Engineering: Essential Keywords for Interview Success** 🚀 In the competitive world of data engineering, being well-versed in key concepts is crucial. Here are some fundamental keywords to focus on for interview preparation: 1. **ETL (Extract, Transform, Load)**: Understanding the end-to-end process of data migration. 2. **Data Warehousing**: Knowledge of systems like Redshift, Snowflake, and BigQuery. 3.**SQL & NoSQL**:Proficiency in querying and managing databases. SQL for structured data and NoSQL for handling semi-structured and unstructured data. 4. **Big Data Technologies**: Familiarity with Hadoop, Spark, and Kafka. 5. **Data Modeling**: Skills in designing and implementing data models. 6. **Cloud Platforms**: Experience with AWS, Azure, or Google Cloud. 7. **Data Pipelines**: Building and maintaining scalable data pipelines. 8. **APIs**: Integrating and utilizing various data sources. 9. **Open Formats**: Knowledge of Delta Lake and Apache Iceberg for efficient data management. 10. **Data Lineage**: Tracking data from origin to destination to ensure quality and compliance. 11. **Batch Processing**: Handling large volumes of data at once, useful for non-real-time operations. 12. **Stream Processing**: Real-time data processing for immediate insights and actions. 13. **Stream Processing Tools**: Expertise in Kafka and Flink for real-time data streams. 14. **Data Governance**: Ensuring data integrity, security, and compliance with policies and regulations. 15. **Orchestration**: Managing the execution of data workflows with tools like Apache Airflow. 16. **Data Analytics Platforms**: Utilizing tools like Databricks for unified data analytics powered by Apache Spark and Microsoft Fabric for comprehensive data management and analytics. 17. **Data Quality**: Ensuring the accuracy, consistency, and reliability of data across systems. 18. **Data Security**: Implementing measures to protect data from unauthorized access and breaches. 19. **Data Integration**: Combining data from different sources to provide a unified view. 20. **Machine Learning Integration**: Incorporating ML models into data pipelines for advanced analytics. 21.**Time-Series Databases**: Specialized for storing and analyzing time-stamped data. Commonly used for monitoring, IoT, and financial data. Examples include InfluxDB and TimescaleDB. 22.**Vector Databases**: Designed for handling high-dimensional vectors, which are used in applications like machine learning, AI, and similarity search. Examples include Pinecone and Milvus. 📈 Let's make this a knowledge-sharing hub! Comment below with your insights, experiences, or any questions you have about these topics. Let's help each other succeed in our data engineering careers! #DataEngineering #InterviewPrep #BigData #ETL #DataWarehousing #SQL #CloudComputing #DataLineage #StreamProcessing #DataGovernance #Orchestration #DataAnalytics #Databricks #MicrosoftFabric#

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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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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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