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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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Reasons for SQL DBA Job Failures : 1.    We will check whether the Agent service is down Management - > SQL Server Agent 2.    Check whether the job Schedule is enabled Management -> SQL Server Agent -> Jobs (or) use msdb go select * from sysjobs 3.    Check whether the MSDB is corrupt select databasepropertyex ('msdb','status') 4.    Check whether Event log Services are running or not Services.msc 5.  Permission issues 6.  Check whether the backup path is accessible or not (If backup job) 7. Check whether there might be same process or query running on the task parallel ---- sp_who2 8. It can be due to Owner change USE msdb GO EXEC msdb.dbo.sp_update_job @job_name='Your Job Name', @owner_login_name='DOMAIN\ThenewOwner' GO 9.   Server Down 10.  Disk Space ------Xp_fixeddrives 11.  We can check Event Viewer, Error Logs for failure causes.

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PySpark Data Engineer Scenario-based Interview experience at Big 4 - KPMG India KPMG (4 years of experience) 𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏: Suppose you have a large dataset containing information about customer transactions, including customer ID, transaction amount, and transaction date. Your task is to calculate the total transaction amount for each customer for the year 2023. How would you approach this problem using PySpark? 𝑨𝒏𝒔𝒘𝒆𝒓: from pyspark.sql import SparkSession from pyspark.sql.functions import year, sum spark = SparkSession.builder \ .appName("Customer Transactions") \ .getOrCreate() df = spark.read.csv("customer_transactions.csv", header=True, inferSchema=True) df_2023 = df.filter(year(df["transaction_date"]) == 2023) total_transactions = df_2023.groupBy("customer_id") \ .agg(sum("transaction_amount").alias("total_transaction_amount")) \ .orderBy("total_transaction_amount", ascending=False) total_transactions.show() Better reach: Abhishek Jha Abhisek Sahu Shubham Wadekar Vaishnavi MURALIDHAR Sumit Mittal Follow Girish Gowda for more Interview Questions

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Quiz Time # 530 | 11-Nov-2024 | MS SQL DBA   Which among the below command used for shrinking tempdb primary datafile 1.                  DBCC SHRINKFILE (tempdev,’size in mb’) 2.                  DBCC SHRINKFILE (templog,’size in mb’) 3.                  DBCC SHRINKFILE(tempdb,’size in mb’) 4.                  DBCC SHRINKFILE (temp,’size in mb’) comment your answer Follow me and #dbaasservice for more update DBA as a Service: DBA as a Service (DBaaS) is a modern approach to database management where your organization can leverage 1. Dedicated expertise without the need for an in-house DBA. 2. Expand DBA support without increasing headcounts 3. Eliminate DBA recruiting and training cost. #sqldba #quiz #australia #sqlserver #sql #database #azure #microsoft #tips #careergoals   

Quiz Time # 519 | 31-Oct-2024 | MS SQL DBA     The state error 5 when executing DBCC Check db denotes 1. An assert or access violation was detected 2. An unknown error occurred that terminated dbcc command 3. Corruption in metadata that terminated dbcc command 4. A failure occurred during emergency mode database repair comment your answer Follow me and #dbaasservice for more update DBA as a Service: DBA as a Service (DBaaS) is a modern approach to database management where your organization can leverage 1. Dedicated expertise without the need for an in-house DBA. 2. Expand DBA support without increasing headcounts 3. Eliminate DBA recruiting and training cost. #sqldba #quiz #australia #sqlserver #sql #database #azure #microsoft #tips #careergoals 

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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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#3 Reasons for SQL DBA Job Failures  1.    We will check whether the Agent service is down Management - > SQL Server Agent 2.    Check whether the job Schedule is enabled Management -> SQL Server Agent -> Jobs                              (or) use msdb go select * from sysjobs 3.    Check whether the MSDB is corrupt select databasepropertyex ('msdb','status') 4.    Check whether Event log Services are running or not Services.msc 5.  Permission issues 6.  Check whether the backup path is accessible or not (If backup job) 7.  Check whether there might be same process or query running on the task parallel ---- sp_who2  8. It can be due to Owner change USE msdb GO EXEC msdb.dbo.sp_update_job @job_name='Your Job Name',  @owner_login_name='DOMAIN\ThenewOwner' GO 9.    Server Down 10.  Disk Space ------Xp_fixeddrives 11.  We can check Event Viewer, Error Logs for failure causes. please let me know if any missing checks. ✍

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Architecture of Apache Spark!! 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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50+ #Data Jobs Today | #WhatsApp +91-6232667387 Pune/Bangalore Azure Data Engineer 3-8 Yrs Azure Databricks, python, SQL and Pyspark. Email: [email protected] #DataAnalytics #DataAnalysis #DataScience #BusinessIntelligence #DataVisualization #SQL #Python #Statistics #DataEngineering #BigData #ETL #DataPipeline #BusinessAnalysis #DataDriven #interview #career #hiring #jobs #jobsearch #jobseekers

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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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In SQL Server, "wait types" refer to the various reasons why a task or process might be waiting for resources or conditions to proceed with its execution. Monitoring and understanding wait types is crucial for identifying performance bottlenecks and optimizing SQL Server performance. Some common wait types in SQL Server are in attached Image

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Hi dears Microsoft Fabric users 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 ( 𝐃𝐚𝐭𝐚 𝐜𝐢𝐭𝐢𝐳𝐞𝐧𝐬 & 𝐈𝐓) 𝐈𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐟𝐚𝐬𝐭 𝐚𝐧𝐝 𝐞𝐚𝐬𝐲 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐚𝐧𝐝 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝐝𝐨𝐧'𝐭 𝐡𝐞𝐬𝐢𝐭𝐚𝐭𝐞 𝐭𝐨 𝐞𝐦𝐛𝐫𝐚𝐜𝐞 𝐃𝐅𝐆𝐄𝐍2 𝐆𝐫𝐞𝐚𝐭 𝐩𝐫𝐨𝐦𝐢𝐬𝐞𝐬 𝐚𝐫𝐨𝐮𝐧𝐝 𝐃𝐅𝐆𝐄𝐍2 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐥𝐲 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐜𝐢𝐭𝐢𝐳𝐞𝐧𝐬 𝐮𝐬𝐞𝐫𝐬 (𝐭𝐡𝐞𝐲 𝐥𝐢𝐤𝐞 "𝐥𝐨𝐰 𝐜𝐨𝐝𝐞" 𝐭𝐨𝐨𝐥𝐬) Usage for Ingestion of course (E.L) but also why not to replace DBT cloud for Kimball gold SQL "T" transformations 😉 BUT 🚑 Strangely , It remains "strange" limitation to be perfect 1) CICD No support on Git (hub) and WS deployment pipelines Sounds Microsoft has a problem to translate DFGEN2 binary projects to TEXT format (json,TMDL) 🚀 Hoping for quick fix 2)There are no CICD parameters on DFGEN2 (exp : connection names) like on semantic models (on M scripts - updated via rules on Workspaces deployment pipeline) 🚀 Hoping for quick fix 3) Ingestion mode ? Fantastic we can handle: -Full re write -Row append -Rows Append on incremental mode (find a watermark date) 🚀 Now waiting for something to replace DBT cloud on "T" jobs - Merge (we need a Primary key and several Type 1 columns) - Rows snapshots (we need a Primary key and several Type 2 columns) =>offer start / end date time & a surrogate key for each versions Automatically deduplicate rows after append incident recovery (based PK) 4) Schema change management (the "contract" for ingestion best QUALITY) Mechanism : You have a source schema by tables (columns name & type) a destination schema by tables and the contract : A columns mapping Currently you are facing a KO if the contract is broken down because ==> On source & destination a mapped column is dropped a mapped column type is changing Super , but Can we perhaps improve that a little bit(something like on Airbyte & Fivetran) by automatically : ADD on mapping & destination ( SQL table <== game changer or file) new source columns ? ADD a default value for cases of source missing columns (even mapped) ? 𝐀𝐜𝐜𝐨𝐫𝐝𝐢𝐧𝐠 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐊𝐎 Important : ADD but Warn DFGEN2 solution owner 🚀 Hoping for quick fix ----------- Cherries on the "cake" First cherry : Please Microsoft can you reduce CUs compute cost for DFGEN2 operations ?😁 Second Cherry : offer something to deal with Rest APIs ingestion ( tricky due to access token , continuation token , loop from parent API (to children API) by passing parameters Perhaps a form to generate a M "tricky" function automatically ? #data #dataanalytics #datagovernance #powerbi #microsoftfabric #bi #azure #microsoft #DataTransformation #DataCulture #BusinessExperts #DataAwareness #DataMesh #DataCatalog #dataenginners #datafactory #dataengineering

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🔄 **Handling Dynamic SQL Table Checks in SQL Server** 💡 Ever needed to dynamically query tables based on date patterns, but encountered missing tables that break your workflow? Here’s a handy SQL Server script that: 1️⃣ Iterates through distinct dates in a source table. 2️⃣ Dynamically constructs table names like `c_yyyymmdd`. 3️⃣ Checks if the table exists before attempting an insert operation. 4️⃣ Skips missing tables gracefully and moves on to the next date. A great way to ensure your data extraction and loading processes are robust and error-free! 🚀 #SQLServer #DBA #DynamicSQL #ErrorHandling #DataManagement #TechTips #Database

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Quiz Time # 526 | 07-Nov-2024 | MS SQL DBA   Whenever an sql server started , which among the below operation happens in tempdb              1.          Recreation              2.          Flushing              3.          Temporary tables truncation              4.          None of above comment your answer Follow me and #dbaasservice for more update DBA as a Service: DBA as a Service (DBaaS) is a modern approach to database management where your organization can leverage 1. Dedicated expertise without the need for an in-house DBA. 2. Expand DBA support without increasing headcounts 3. Eliminate DBA recruiting and training cost. #sqldba #quiz #australia #sqlserver #sql #database #azure #microsoft #tips #careergoals 

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Quiz Time # 535 | 16-Nov-2024 | MS SQL DBA   Which among the below is function of /res parameter in perfmon 1.                  Starts the Reliability Monitor 2.                  Starts the Resource view 3.                  Starts the Performance Monitor 4.                  Starts the System diagnostics data collector set comment your answer Follow me and #dbaasservice for more update DBA as a Service: DBA as a Service (DBaaS) is a modern approach to database management where your organization can leverage 1. Dedicated expertise without the need for an in-house DBA. 2. Expand DBA support without increasing headcounts 3. Eliminate DBA recruiting and training cost. #sqldba #quiz #australia #sqlserver #sql #database #azure #microsoft #tips #careergoals 

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✈ Day 6/30: List out various XML configuration files in Hadoop. A: The following are the various XML configuration files in Hadoop: HADOOP-ENV.sh CORE-SITE.XML HDFS-SITE.XML MAPRED-SITE.XML Masters Slave Why does Hadoop use Context objects? A: Context object permits the Mapper/Reducer to communicate with the remainder of the Hadoop system. It contains task configuration data and interfaces that allow it to send output. It can be used by programs to report progress. List table-generating functions available in Hive. A: The table-generating functions that are available in Hive are : explode(ARRAY) explode(MAP) inline(ARRAY<STRUCT[,STRUCT]>) explode(array a) json_tuple(jsonStr, k1, k2, …) parse_url_tuple(url, p1, p2, …) posexplode(ARRAY) stack(INT n, v_1, v_2, …, v_k) What is XComs In Airflow? A: XCom (short for cross-communication) are messages that allow data to be sent between tasks. The key, value, timestamp, and task/DAG id are all defined. What are Pyspark window functions? A: PySpark Window function performs statistical operations such as rank, row number, etc. on a group, frame, or collection of rows and returns results for each row individually. Name few window functions. A: row_rumber() rank() dense_rank() percent_rank() avg() sum() min() max() What does lag() do in pyspark ? A: A lag() function is used to access previous rows’ data as per the defined offset value in the function. pyspark.sql.functions.lag(col, offset=1, default=None) #30daysdataengineeringchallenge #dataengineering #PySpark #dataanalysis #bigdata #hive #airflow #hadoop #cloud

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New Post: The How To Write SQL Server Queries Correctly Cheat Sheet: Conditional Join and Where Clauses - https://lnkd.in/dE9-BNPc

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SQL SERVER INTERVIEW QSTION SQL Challenge: Retrieve Employees Who Joined in the Last 30 Days SELECT * FROM Employee WHERE JoiningDate >= DATEADD(DAY, -30, GETDATE()); Explanation 1. DATEADD(DAY, -30, GETDATE()): Subtracts 30 days from the current date (GETDATE()), giving the date 30 days ago 2.WHERE JoiningDate >=: Filters employees who joined on or after this date. Use Case: 1.Perfect for generating onboarding reports. 2. Helps identify recent joiners for training or welcome programs. How do you use date filters in SQL? Share your favorite tricks below! your thoughts and suggestions are always welcome. #SQLServer #DataEngineering #SQLTips #InterviewPreparation

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