Ziyauddin Shaikh

An under appreciated reason why data processing companies like Confluent, Databricks, Splunk etc get really big is how data scales. Data usage in a company scales O(EQS), if we use Big-O notation, E=number of employees using the data, Q=number of queries being run , S=size of data queried. The reason customer ACVs can get absolutely massive is that if you’ve built an incredible product you will be able to process more data if the customer does even one of the 3: - store more data - add more employees who want to work with the data - have more computations being done on the data Most of the time, companies that are growing have all 3 increasing simultaneously. Ofcourse, because data tools are sticky this growth also persists and even when employee growth slows down, often more data and uses of the data have emerged If you’re building any tools, or even side projects that involve data processing, I’d love to learn more and talk to you!

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Hadoop Vs Spark Interview Question!! Interviewer: Let's discuss the evolution of the Hadoop ecosystem and the rise of Apache Spark. One significant change that occurred was the replacement of Hadoop MapReduce with Spark in many data processing workflows. Can you explain why this transition happened? Candidate: Certainly! The transition from Hadoop MapReduce to Apache Spark can be attributed to several factors. One key reason is Spark's superior performance compared to MapReduce. Spark's in-memory processing model allows it to execute tasks much faster by minimizing data shuffling and disk I/O, leading to significant speed improvements for data processing tasks. Interviewer: That's an important point. Can you elaborate on how Spark's in-memory processing differs from Hadoop MapReduce's disk-based processing? Candidate: Of course! In Hadoop MapReduce, intermediate data between map and reduce tasks is typically written to disk, which can incur overhead due to disk I/O operations. In contrast, Spark keeps intermediate data in-memory whenever possible, reducing the need for disk reads and writes. This in-memory processing model enables Spark to achieve faster processing speeds, especially for iterative algorithms and interactive analytics workloads. Interviewer: I see. So, apart from performance, are there any other reasons why Spark replaced MapReduce in many data processing workflows? Candidate: Absolutely. Another factor is Spark's versatility. While Hadoop MapReduce is primarily designed for batch processing, Spark supports a wide range of processing workloads, including batch processing, interactive querying, streaming data processing, and machine learning. This versatility makes Spark a more attractive choice for organizations looking to address diverse analytical use cases within a single framework. Interviewer: That's a compelling advantage. How did Spark's programming model contribute to its popularity? Candidate: Spark offers a more developer-friendly programming model compared to Hadoop MapReduce. With its rich set of high-level APIs in languages like Scala, Python, and Java, Spark simplifies the development of complex data processing workflows. Additionally, Spark's interactive shell (Spark REPL) facilitates rapid prototyping and experimentation, enhancing developer productivity. Interviewer: Thank you for the insightful explanation. It's clear that Spark's performance, versatility, and ease of use have made it a preferred choice for many organizations seeking to modernize their data processing infrastructure. Candidate: My pleasure! #DataEngineering #PySpark #ETLProject #pyspark #python #azuredataengineer #databricks #awscloud #googlecloud #dataengineer #dataengineerjobs #dataanalysis #dataanalytics #glue #azuredatabricks #azuredatafactory #synapse #sql #sqlserver #powerbi #database #etl #etldeveloper #Python #Pandas #BeginnertoAdvance #Dataframe #Sorting #Joins #Grouping #RealTimeScenarios #sql #awscloud #powerbi #databricks

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DATA ENGINEERING (DE) FOR DOWNSTREAM USECASES 🙂 Basically, what I did was feed FEW RAW JSON FILES Residing in AWS S3 to SnowFlake & Perform ELT VIA DBT (Data Build Tool) in VS Code where I made use of JINJA Script to make Transformations on the Data Residing in SnowFlake in a series of steps ensuring Modularity of CODE abiding by Medallion Architecture- Schematic Data for Downstream Users (BI / AI / ML / DS) By me DE! 😎

Apache Spark Vs Hadoop MapReduce! Interviewer: Let's delve into the comparison between Apache Spark and Hadoop MapReduce. Can you elaborate on their core differences? Candidate: Certainly. Apache Spark is characterized by its in-memory processing, enabling faster computation by keeping data in memory across multiple processing steps. This contrasts with Hadoop MapReduce, which follows a disk-based processing model, reading data from and writing data to disk after each Map and Reduce phase. This fundamental difference in processing models greatly influences their performance and suitability for various types of workloads. Interviewer: That's insightful. How about their ecosystem support? Candidate: While Hadoop MapReduce benefits from its longstanding presence in the ecosystem, Spark has rapidly gained popularity and built a comprehensive ecosystem of its own. Spark seamlessly integrates with various data sources and storage systems, and its high-level APIs for SQL, streaming, machine learning, and graph processing simplify application development. Additionally, Spark can run both standalone and on existing Hadoop clusters, offering flexibility and compatibility with existing infrastructure. Interviewer: Good points. Now, let's talk about fault tolerance. How do Spark and MapReduce handle failures in distributed environments? Candidate: Both frameworks employ fault tolerance mechanisms, but they differ in their approaches. In MapReduce, fault tolerance is achieved through data replication and re-execution of failed tasks. Intermediate data is persisted to disk after each phase, allowing tasks to be rerun on other nodes in case of failure. On the other hand, Spark leverages lineage and resilient distributed datasets (RDDs) to achieve fault tolerance. RDDs track the lineage of each partition, enabling lost partitions to be recomputed from the original data source. Because Spark primarily operates in-memory, it can recover from failures more quickly compared to MapReduce. Interviewer: That's a comprehensive explanation. Lastly, in what scenarios would you recommend using Apache Spark over Hadoop MapReduce, and vice versa? Candidate: I would recommend using Apache Spark for applications that require real-time processing, iterative algorithms, or interactive analytics. Its in-memory processing capabilities and high-level APIs make it well-suited for these use cases. Conversely, Hadoop MapReduce may be more suitable for batch processing tasks that involve large-scale data processing and do not require real-time or iterative computation. It's essential to consider factors such as performance requirements, processing models, and ecosystem compatibility when choosing between the two frameworks. #spark #hadoop #bigdata

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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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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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🌟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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Enter the ultimate face-off: Apache Spark vs. Apache Hadoop. These two powerful tools serve distinct purposes.                Know more ......          👇👇👇👇👇👇👇👇👇👇👇👇👇 https://lnkd.in/gYAFCM7b #Apachespark #ApacheHadoop #DataProcessing #RealTimeAnalytics #BatchProcessing #BigDataTools #DataAnalytics #CloudComputing #DataStorageSolutions #MachineLearning #DataArchitecture

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Clearing Interviews are not tough. Most of successfully people follow this plan. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗹𝗮𝗻: 👇 - 𝗟𝗶𝘀𝘁 𝗼𝘂𝘁 𝗺𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝘁𝗵𝗮𝘁 𝗝𝗼𝗯 Example: For Data Engineer- ADF, pyspark - 𝗠𝗮𝘀𝘁𝗲𝗿 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽 𝘁𝗼𝗽𝗶𝗰𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗮𝘁 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: In ADf ( Optimization, triggers, On-prem to cloud, IR etc) - 𝗡𝗼𝘄 𝗹𝗶𝘀𝘁 𝗼𝘂𝘁 𝘀𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: For Data Engineer- Synpase, Key vault, ADLS, etc - 𝗖𝗼𝘃𝗲𝗿 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗼𝗽𝗶𝗰𝘀 𝗳𝗼𝗿 𝗮𝗯𝗼𝘃𝗲 𝗹𝗶𝘀𝘁 Example: ADLS vs Blob, when to use what - 𝗧𝗮𝗸𝗲 𝗮𝗻𝘆 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲, & 𝗽𝘂𝘁 𝘆𝗼𝘂𝗿 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 Example: use my resume template (see comment) - 𝗨𝗽𝗹𝗼𝗮𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗴𝗶𝘃𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 - 𝗔𝗳𝘁𝗲𝗿 𝗲𝘃𝗲𝗿𝘆 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄, 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁 & 𝘂𝗽𝗱𝗮𝘁𝗲 - 𝗥𝗲𝗽𝗲𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 - After 4-5 repetition you will crack at least ONE OFFER. Need any help in switching the stream to Azure data engineering, 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗺𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 (𝗗𝗠) PS: Everything seem tough at start, but becomes easy as you start doing #Data #design #analytics #india

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Clearing Interviews are not tough. Most of successfully people follow this plan. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗹𝗮𝗻: 👇 - 𝗟𝗶𝘀𝘁 𝗼𝘂𝘁 𝗺𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝘁𝗵𝗮𝘁 𝗝𝗼𝗯 Example: For Data Engineer- ADF, pyspark - 𝗠𝗮𝘀𝘁𝗲𝗿 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽 𝘁𝗼𝗽𝗶𝗰𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗮𝘁 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: In ADf ( Optimization, triggers, On-prem to cloud, IR etc) - 𝗡𝗼𝘄 𝗹𝗶𝘀𝘁 𝗼𝘂𝘁 𝘀𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: For Data Engineer- Synpase, Key vault, ADLS, etc - 𝗖𝗼𝘃𝗲𝗿 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗼𝗽𝗶𝗰𝘀 𝗳𝗼𝗿 𝗮𝗯𝗼𝘃𝗲 𝗹𝗶𝘀𝘁 Example: ADLS vs Blob, when to use what - 𝗧𝗮𝗸𝗲 𝗮𝗻𝘆 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲, & 𝗽𝘂𝘁 𝘆𝗼𝘂𝗿 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 Example: use my resume template (see comment) - 𝗨𝗽𝗹𝗼𝗮𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗴𝗶𝘃𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 - 𝗔𝗳𝘁𝗲𝗿 𝗲𝘃𝗲𝗿𝘆 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄, 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁 & 𝘂𝗽𝗱𝗮𝘁𝗲 - 𝗥𝗲𝗽𝗲𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 - After 4-5 repetition you will crack at least ONE OFFER. Need any help in switching the stream to Azure data engineering, 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗺𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 (𝗗𝗠) PS: Everything seem tough at start, but becomes easy as you start doing #Data #design #analytics #india

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Connections, I have a 3GB file in.gz format. I am writing this file into an external table in adls location which is getting written in PARQUET format. The write job is taking around 27 mins for only 3 GB of data for only one date partition ; and it is over - partitioning the original file of 17 MB each and creating 200 part parquet files in adls. Its just reading and writing. No transformations in between. Workarounds that I have tried - 1) Incresed the number of executors per node. 2) Set the maxPartitionBytes spark parameter to a value targeting 5-6 partitions in adls. But its still taking 27 mins. Can anyone suggest how can I bring down the write time ?

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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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Clearing Interviews are not tough. Most of successful people follow this plan. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗹𝗮𝗻: 👇 - 𝗟𝗶𝘀𝘁 𝗼𝘂𝘁 𝗺𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝘁𝗵𝗮𝘁 𝗝𝗼𝗯 Example: For Data Engineer- ADF, pyspark - 𝗠𝗮𝘀𝘁𝗲𝗿 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽 𝘁𝗼𝗽𝗶𝗰𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗮𝘁 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: In ADf ( Optimization, triggers, On-prem to cloud, IR etc) - 𝗡𝗼𝘄 𝗹𝗶𝘀𝘁 𝗼𝘂𝘁 𝘀𝗲𝗰𝗼𝗻𝗱𝗮𝗿𝘆 𝘀𝗸𝗶𝗹𝗹 𝘀𝗲𝘁 Example: For Data Engineer- Synpase, Key vault, ADLS, etc - 𝗖𝗼𝘃𝗲𝗿 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗼𝗽𝗶𝗰𝘀 𝗳𝗼𝗿 𝗮𝗯𝗼𝘃𝗲 𝗹𝗶𝘀𝘁 Example: ADLS vs Blob, when to use what - 𝗧𝗮𝗸𝗲 𝗮𝗻𝘆 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲, & 𝗽𝘂𝘁 𝘆𝗼𝘂𝗿 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 Example: use my resume template (see comment) - 𝗨𝗽𝗹𝗼𝗮𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗴𝗶𝘃𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 - 𝗔𝗳𝘁𝗲𝗿 𝗲𝘃𝗲𝗿𝘆 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄, 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗹𝗶𝘀𝘁 & 𝘂𝗽𝗱𝗮𝘁𝗲 - 𝗥𝗲𝗽𝗲𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 - After 4-5 repetitions you will crack at least ONE OFFER. Need any help in switching the stream to Azure data engineering, 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗺𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 (𝗗𝗠) PS: Everything seems tough at start, but becomes easy as you start doing #Data #design #analytics #india

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Read the industry story titled "Claude 3.5 Sonnet sets new coding benchmarks." Times Techies, The Times Of India, where Bebi Negi, Senior Lead Data Scientist, Analytics Center of Excellence, emphasize on the necessity of coders to meet the 10% gap in the final output: https://shorturl.at/nn0SW #GenAI #Claude #sonnetsets #coders #code #HappiestMinds

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Snowflake Interview Question. Part 5:Advanced Techniques and Miscellaneous. Asked in #Epam, #Cisco, #Kipi.bi #TCS #Infosys 18. How to exclude double quotes in the file format of Snowflake? 19. What happens to compute in Snowflake? 20. What is the cost computation for cloning in Snowflake? 21. Limitations in a Reader account in Snowflake? 22. How to change the sequence number in Snowflake? 23. How to alter the auto concrete in Snowflake? 24. Syntax to change the datatype in Snowflake? 25. What are the credits for each warehouse in Snowflake? 26. What are credits in Datawarehouse? 27. Different types of credits in Snowflake? 28. Errors we get when loading files to Snowflake tables? 29. How to eliminate the entire row duplicate in the flat file of Snowflake? 30. How to retain one unique record and delete duplicates in Snowflake tables? 31. How to check the long-running step in SQL at Snowflake? 32. What are the limitations we have in using SQL language in Snowflake Java scripts? 33. How to integrate and share data between Unix and Snowflake using SnowSQL on Unix? 34. How to load a large volume of data from on-premise to Snowflake? 35. How do you load data into an external stage in S3 in Snowflake? 36. Python in Snowflake? 37. Does lambda function in Snowflake? 38. Use of any ETL tool in Snowflake? Follow Avinash S. Get Interview Resources: https://lnkd.in/dCcD-kBt Get SQL, Python, Snowflake(25% OFF) https://lnkd.in/dRehzivv

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Comprehensive Interview Questions on Apache Hive, Spark, Kafka, Airflow, Hudi, Flink, Iceberg 🎯 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝗶𝘃𝗲 - What are the differences between internal and external tables in Hive, and when would you use each? - How do partitioning and bucketing improve query performance in Hive? - What are optimized joins in Hive, and how can you improve the efficiency of large table joins? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 - What is the difference between transformations and actions in Spark, and can you provide an example? - How can data partitioning be optimized for performance in Spark? - What is the difference between cache() and persist() in Spark, and when would you use each? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 - How does Kafka partitioning enable scalability and load balancing? - How does Kafka’s replication mechanism provide durability and fault tolerance? - How would you manage Kafka consumer rebalancing to minimize data loss? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 - What are dynamic DAGs in Airflow, and what benefits do they offer? - What are Airflow pools, and how do they help control task concurrency? - How do you implement time-based and event-based triggers for DAGs in Airflow? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗛𝘂𝗱𝗶 - How does Apache Hudi support incremental data processing in data lakes? - What are the different table types available in Apache Hudi, and how do they differ? - How does Hudi handle upserts (updates and inserts) at scale? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 - What is Apache Flink’s event-time processing, and how is it different from processing time? - How does Flink’s state management work for fault-tolerance in stream processing? - What are the different windowing functions available in Apache Flink? 📍 𝗔𝗽𝗮𝗰𝗵𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 - How does Apache Iceberg enable schema evolution without breaking existing queries? - What is the purpose of partitioning in Iceberg, and how does it differ from traditional partitioning techniques? - How does Iceberg handle large-scale data versioning and rollback? 🚨 To learn more, Join my high quality, industry & modern tech stack based and practical project driven Data Engineering BootCAMP✌🏻👇 👉 Enroll Here - https://lnkd.in/dRgYCmWd 🚀 Dedicated Doubt Support & Placement Assistance 📲 Call/WhatsApp for any query (+91) 9893181542 Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar

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Here are 35 most common Pyspark interview questions for 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 position: 𝗥𝗗𝗗𝘀 - 1. What is an RDD in Apache Spark? Explain its characteristics. 2. How are RDDs fault-tolerant in Apache Spark? 3. What are the different ways to create RDDs in Spark? 4. Explain the difference between transformations and actions in RDDs. 5. How does Spark handle data partitioning in RDDs? 6. Can you explain the lineage graph in RDDs and its significance? 7. What is lazy evaluation in Apache Spark RDDs? 8. How can you persist RDDs in memory for faster access? 9. Explain the concept of narrow and wide transformations in RDDs. 10. What are the limitations of RDDs compared to DataFrames and Datasets? 𝗗𝗮𝘁𝗮𝗳𝗿𝗮𝗺𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 - 1. What are DataFrames and Datasets in Apache Spark? 2. What are the differences between DataFrame and RDD? 3. Explain the concept of a schema in a DataFrame. 4. How are DataFrames and Datasets fault-tolerant in Spark? 5. What are the advantages of using DataFrames over RDDs? 6. Explain the Catalyst optimizer in Apache Spark. 7. How can you create DataFrames in Apache Spark? 8. What is the significance of Encoders in Datasets? 9. How does Spark SQL optimize the execution plan for DataFrames? 10. Can you explain the benefits of using Datasets over DataFrames? 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 - 1. What is Spark SQL, and how does it relate to Apache Spark? 2. How does Spark SQL leverage DataFrame and Dataset APIs? 3. Explain the role of the Catalyst optimizer in Spark SQL. 4. How can you run SQL queries on DataFrames in Spark SQL? 5. What are the benefits of using Spark SQL over traditional SQL queries? 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - 1. What are some common performance bottlenecks in Apache Spark applications? 2. How can you optimize the shuffle operations in Spark? 3. Explain the significance of data skew and techniques to handle it in Spark. 4. What are some techniques to optimize Spark job execution time? 5. How can you tune memory configurations for better performance in Spark? 6. What is dynamic allocation, and how does it optimize resource usage in Spark? 7. How can you optimize joins in Spark? 8. What are the benefits of partitioning data in Spark? 9. How does Spark leverage data locality for optimization? 🔥 New Big Data Masters Program (𝐎𝐧-𝐏𝐫𝐞𝐦𝐢𝐬𝐞 + 𝐀𝐖𝐒) by Seekho Bigdata Institute – Starting November 15! 🔍 Visit our website for full details on the syllabus and course structure. www.seekhobigdata.com or 📲 Join the Group for More Info! https://lnkd.in/g2ij_jUs 💥 Master Big Data and Equip Yourself for Success! Don't miss out on this life-changing opportunity! Karthik K. Seekho Bigdata Institute

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