Aditya Sharma

🗓 Snowflake Optimization - Summary of the week 1. Prune Partitions Snowflake uses the concept of micropartitions. Apply filters to prune data. 2. Enable Automatic Clustering Automatic clustering can be useful for certain tables that are heavily queried on certain fields. It does cost credits, so you might need to a cost-benefit analysis 3. Reduce Idle time Set AUTO_SUSPEND = 60. 4. Consolidate warehouses Consolidate similar warehouses into a single multicluster warehouse. 5. Use incremental models Write incremental models in dbt. Incremental models only process the new incoming data based on a field, generally date.

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Week 10 of the "Ultimate Big Data Masters Program" provided an insightful journey into Apache Spark Optimizations and Performance Tuning - 1, provided by Sumit Mittal Sir. Here's a summary of Week 10 key learnings: ·       Pyspark Optimizations – Internals of groupBy ·       Normal Join Vs Broadcast Join ·       Different types of Joins ·       Partition Skew ·       Adaptive Query Execution (AQE) ·       Join Strategies ·       Optimizing Join of 2 large tables – Bucketing Thank you Sumit Mittal Sir and TrendyTech for all the support and guidance whenever needed. #Bigdata #Spark #Python #DAG #Join #dataengineer #AWS #Azure #ADLS #HDFS #Linux #SQL #athena #redshift

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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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🌟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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Hello Everyone, Here are the most asked 40 Spark Questions in a Data Engineering Interview in 2024 : Medium-Level Questions: - What is Apache Spark, and how does it differ from Hadoop MapReduce? - Explain the difference between transformations and actions in Spark. - What are the different deployment modes in Spark? Explain each. - What is a Spark DataFrame? How does it differ from an RDD? - Explain the concept of lazy evaluation in Spark. - How does Spark handle fault tolerance? - What are the advantages of using Spark over other big data processing tools? - Explain the concept of a partition in Spark. - How can you improve the performance of a Spark job? - What is the significance of the driver and executor in Spark? - Explain the concept of a broadcast variable in Spark. - How can you monitor the performance of a Spark application? - What is shuffle in Spark, and when does it occur? - How does Spark handle data skew? - Explain the concept of window functions in Spark SQL. - Explain the difference between narrow and wide transformations in Spark. - What is the role of the DAG scheduler in Spark? - How does Spark handle data serialization and deserialization? - What is the purpose of the SparkSession in Spark applications? - Explain the use of checkpoints in Spark streaming applications? Hard-Level Questions: Spark Internals - Explain the architecture of Apache Spark in detail. - How does Spark handle memory management and garbage collection? - Explain the different join types in Spark SQL with examples. - What is the purpose of the SparkContext in Spark applications? - How does Spark handle skewed data in joins? - Explain the concept of lineage in Spark RDDs. - What are the different serialization formats supported by Spark? Explain when to use each. - How does Spark ensure data locality? - Explain the concept of Spark DAG (Directed Acyclic Graph) and its significance. - What are the challenges of running Spark on a large-scale cluster? - Explain the use cases of Spark's MLlib (Machine Learning Library). - How does Spark Streaming work? Explain the micro-batch architecture. - How can you tune the performance of a Spark application? - Explain the process of checkpointing in Spark Streaming. - How does Spark handle data skew in aggregations? - Explain the concept of speculative execution in Spark. - How does Spark integrate with external storage systems like HDFS or S3? - What is the role of the BlockManager in Spark? - Explain the concept of dynamic resource allocation in Spark. - How does Spark handle resource allocation and scheduling in a cluster environment? #CareerGoals #CareerGrowth #JobOpportunity, #JobMarket #JobApplication #JobFair #JobSeekerAdvice #JobSearchStrategies #JobSuccess #JobSkills #CareerPath #CareerAdvice #InterviewSkills #CareerChange #JobOffer, #JobSearchJourney #EmploymentOpportunity #CareerBuilding #CareerPlanning #JobSearchStrategy #JobApplicationTips #CareerAdviceForGraduates #JobHuntingTips #JobInterviewSkills

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💡 Understanding Garbage Collection in Apache Spark: Managing Memory Like a Pro 💡 One key factor that can make or break your Spark job is memory management. Specifically, let's talk about Garbage Collection (GC) in Spark and how to avoid memory pitfalls like memory leaks that can lead to job failures. 🚨 What is Garbage Collection in Spark? Garbage Collection is the process by which the Java Virtual Machine (JVM) reclaims memory that’s no longer being used by your Spark applications. It kicks in when memory usage grows too high, trying to clear out space for new data. However, in Spark’s distributed environment, inefficient GC can cause long pauses and drastically slow down performance. 🚀 What Causes Excessive GC or Memory Leaks? Too many small objects: Spark works on RDDs/DataFrames that may contain millions of small objects. If these are not properly released, they stay in memory longer than needed, triggering frequent garbage collection. Large shuffles: When wide transformations (like groupByKey() or join()) occur, Spark has to shuffle data across partitions, consuming a lot of memory and causing excessive garbage collection. Long-living objects: Variables held in memory (like broadcast variables or accumulators) can also contribute to memory leaks if not cleaned up after their usage. Inefficient caching: If RDDs/DataFrames are cached in memory but not properly managed (or not cleared when no longer needed), they can hog memory space unnecessarily. 🔥 Best Practices to Manage Memory and Avoid GC Issues 🔥 Restart your cluster occasionally to clear out hidden cached objects that refuse to exit memory Write optimized code and leverage Photon Engine in Databricks Avoid memory sapping objects like pandas dataframe when working with large data in Spark Use serialization efficiently: Opt for Kryo serialization, which is faster and more memory-efficient than Java serialization. Tune your GC settings: You can customize the JVM's garbage collection settings (e.g., using the G1 garbage collector) and adjust memory allocation to make GC more efficient for your specific workload. Use persist() smartly: When caching RDDs or DataFrames, specify the right storage level (e.g., MEMORY_ONLY, MEMORY_AND_DISK). Ensure that cached data is released when not needed using unpersist(). Avoid large shuffles: Try to reduce wide transformations and repartition data efficiently to prevent excessive shuffling, which can spike memory usage. Monitor and Optimize: Leverage tools like the Spark UI and metrics like GC time to identify when memory pressure is too high and optimize accordingly.

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Week 11 of the "Ultimate Big Data Masters Program" provided an insightful journey into Apache Spark Optimizations and Performance Tuning - 2, provided by Sumit Mittal Sir. Here's a summary of Week 11 key learnings: ·       Memory Management in Apache Spark ·       Sort Aggregate Vs Hash Aggregate ·       Apache Spark Logical and Physical Plans ·       Catalyst Optimizer ·       File Formats and Compression techniques ·       Specialized File Formats AVRO | ORC | Parquet ·       Schema Evolution Thanks to Sumit Mittal sir and TrendyTech team for this learning experience. #BigData #ApacheSparkProject #LendingClub #DataScience #LearningJourney #CareerGrowth #ApacheSpark

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Spark query to provide different discount rates to different product categories. #SPARK #SCALA #BIGDATA Karthik K. def main(args: Array[String]):Unit = { val ss = SparkSession.builder.appName("25_05").master("local[*]").getOrCreate() import ss.implicits._ val data= Seq( (10,32,100), (110,100,null), (210,142,120), (310,150,140) ) val data_1 = data.map(x =>Row(x._1,x._2, x._3)) println(data_1) val rdd = ss.sparkContext.parallelize(data_1,3) val schema=StructType( Seq( StructField("PRODUCT ID", IntegerType,true), StructField("QUANTITY", IntegerType,true), StructField("PRICE", IntegerType,true) ) ) val tempDF=ss.createDataFrame(rdd,schema) tempDF.show() val finalDF= tempDF.select(col("PRODUCT ID"), col("QUANTITY"), col("PRICE"), when(col("PRODUCT ID") <=100,col("QUANTITY")*col("PRICE")). when(col("PRODUCT ID") >100 && col("PRODUCT ID")<=200,lit(0.9)*col("QUANTITY")*col("PRICE")). when(col("PRODUCT ID") >200 && col("PRODUCT ID")<=300,lit(0.8)*col("QUANTITY")*col("PRICE")). when(col("PRODUCT ID") >300,0).as("REVENUE"), when(col("PRODUCT ID") <=100,lit("0%")). when(col("PRODUCT ID") >100 && col("PRODUCT ID")<=200,lit("10%")). when(col("PRODUCT ID") >200 && col("PRODUCT ID")<=300,lit("20%")). when(col("PRODUCT ID") >300,lit("100%")).as("DISCOUNT OFFERED") ) finalDF.show() OUTPUT:

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There are many students who ask me PySpark and Databricks course. I reply with simple roadmaps 1. Learn Databricks and PySpark from my YouTube channel or some other YouTube Channel 2. After completing 40% of PySpark, start working on projects Here is the 🆓 YouTube playlist : https://lnkd.in/g7m54R9Y Here is the course project: https://lnkd.in/dFy3vYkb #databricks #delta #pyspark Delta Lake Databricks

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#DATAENGINEERING_WITH_SNOWFLAKE HI all, #connections Here is the doc on first part of SNOWPIPE #snowflake #snowpipe #dataloading‍‍ #‍‍‍‍‍‍‍dataengineering #datascience #snowflake_25 c‍‍‍‍‍overed the following: 1. about snowpipe 2. data loading using snowpipe Thanks, #all

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The key difference between #Spark DataFrames and Spark Tables ...... "𝑵𝒆𝒘 𝑨𝑾𝑺 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑩𝒂𝒕𝒄𝒉 𝒕𝒐 𝒔𝒕𝒂𝒓𝒕 𝑱𝒖𝒍𝒚 28𝒕𝒉- 𝐉𝐨𝐢𝐧 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 49 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 𝐰𝐡𝐨 𝐡𝐚𝐯𝐞 𝐞𝐧𝐫𝐨𝐥𝐥𝐞𝐝." 𝐀 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 is a distributed in-memory dataset with a defined schema. It acts like a temporary table, existing only within your Spark application's session. You can create DataFrames from various sources like CSV files, databases, or existing RDDs (Resilient Distributed Datasets). DataFrames offer programmatic manipulation using Scala, Java, Python, or R functionalities. 𝐒𝐩𝐚𝐫𝐤 𝐓𝐚𝐛𝐥𝐞, however, represents a logical view of data stored externally. This data can reside in data warehouses (like Hive), databases, or various file formats (Parquet, ORC). Spark Tables provide an SQL interface for querying this external data. They are persistent, meaning they exist independent of your Spark application session. 𝐈𝐧 𝐬𝐮𝐦𝐦𝐚𝐫𝐲 Storage DataFrame - In-memory, temporary; Spark Table - External storage, persistent 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 DataFrame - Programmatic API; Spark Table - SQL queries 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 DataFrame - Data transformations within Spark application; Spark Table - Accessing and querying large datasets stored externally Additional Points You can create Spark Tables from DataFrames by saving them to external storage formats. Both DataFrames and Spark Tables can leverage Spark's distributed processing capabilities to handle large datasets efficiently. spark.table(tableName) is just a shortcut for spark.read.table(tableName). They both achieve the same functionality of reading data from a Spark Table. doc credit - Abhijit Sahoo #aws #dataengineering ✅ Enrollments for Version 3 of 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐑𝐞𝐚𝐥-world AWS Data Engineering ( RADE™) program is ON ! 𝑵𝒆𝒘 𝑨𝑾𝑺 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑩𝒂𝒕𝒄𝒉 𝒕𝒐 𝒔𝒕𝒂𝒓𝒕 𝑱𝒖𝒍𝒚 28𝒕𝒉- 𝐉𝐨𝐢𝐧 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 49 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 𝐰𝐡𝐨 𝐡𝐚𝐯𝐞 𝐞𝐧𝐫𝐨𝐥𝐥𝐞𝐝. ✅𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐜𝐭𝐢𝐨𝐧-𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝, fill the form below to be get invited for the webinar for RADE™ program V3 𝐔𝐒𝐏: 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 program !

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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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𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗶𝗺𝗲 𝗳𝗿𝗼𝗺 𝟭 𝗵𝗿 𝟯𝟬 𝗺𝗶𝗻 𝘁𝗼 𝟭𝟱 𝗺𝗶𝗻 In my recent #DataEngineering Project, I have reduced the spark job time from 1 hr 30 minutes to 15 minutes at time of data ingestion. Below are the details- source - Oracle Database destination - s3 bucket table - orders (containing 80000000 rows and 70 columns approx) load type - full load file format - parquet For Moving the table data from oracle to s3, we used the jdbc format in spark read and supplied various configurations as options to load the data in the dataframe. 𝗡𝗼𝗿𝗺𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 (𝘁𝗮𝗸𝗶𝗻𝗴 𝟭 𝗵𝗿 𝟯𝟬 𝗺𝗶𝗻) orders_df = spark.read \ .format("jdbc") \ .option("url", <host url>) \ .option("dbtable", <pass your sql query here> )\ .option("user", db_user_name) \ .option("password", your_password ) \ .option("customJdbcDriverS3Path", driver_s3_path ) \ .option("cstomJdbcDriverClassName", "oracle.jdbc.driver.OracleDriver") \ .load() 𝗛𝗮𝗰𝗸𝗲𝗱 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 (𝘁𝗮𝗸𝗶𝗻𝗴 𝟭𝟱 𝗺𝗶𝗻) orders_df = spark.read \ .format("jdbc") \ .option("url", <host url>) \ .option("dbtable", <pass your sql query here> ) \ .option("user", db_user_name) \ .option("password", your_password) \ .option("numPartitions", num_of_partitions ) \ .option("lowerBound", lower_bound) \ .option("upperBound", upper_bound) \ .option("partitionColumn", partition_column) \ .option("fetchsize", "10000") \ .option("customJdbcDriverS3Path", driver_s3_path) \ .option("customJdbcDriverClassName", "oracle.jdbc.driver.OracleDriver") \ .load() I have used 5 different options while reading - 𝗻𝘂𝗺𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝘀, 𝗹𝗼𝘄𝗲𝗿𝗕𝗼𝘂𝗻𝗱, 𝘂𝗽𝗽𝗲𝗿𝗕𝗼𝘂𝗻𝗱, 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗖𝗼𝗹𝘂𝗺𝗻, 𝗳𝗲𝘁𝗰𝗵𝗦𝗶𝘇𝗲. Note: While reading the jdbc data , spark uses only 1 worker node to open the connection to database which is a default Behavior. Important: I have passed the partition_column and number of partitions to tell spark to read data in partitions and utilize all the worker nodes. Hence by this method, the distributing computing come into picture and parallelism is achieved. R Systems #DataEngineer #Manager #TalentAcquisition Sumit Mittal TrendyTech - Big Data By Sumit Mittal

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Day 23 - 30-Day Data Engineering Interview Preparation Series Snowflake (#Day23): 18 Snowflake practical interview questions that can be relevant for a data engineering role 👇 📍 How would you design a Snowflake data warehouse for a retail company with multiple product lines and sales channels? Provide an example schema and explain your design choices. 📍 Write a SQL query in Snowflake to find the top 5 products by sales volume for each month in the last year. 📍 Describe the steps to set up data replication between two Snowflake accounts in different regions. 📍 Explain how you would implement a data ingestion pipeline in Snowflake using Snowpipe for real-time data ingestion from AWS S3. 📍 You are given a large dataset with both structured and semi-structured data (JSON). How would you load this data into Snowflake and query it efficiently? 📍 How would you optimize a Snowflake query that is taking too long to execute? Describe the techniques and tools you would use. 📍 Create a stored procedure in Snowflake that automatically archives old data from a transactional table to a historical table. 📍 Explain how to use Snowflake’s Time Travel feature to recover data that was accidentally deleted from a table. Provide an example. 📍 Demonstrate how you would implement role-based access control (RBAC) in Snowflake to ensure that different user groups have appropriate access to data. 📍 Describe a strategy to handle slowly changing dimensions (SCD) Type 2 in Snowflake. Provide SQL examples. 📍 How would you set up a continuous data pipeline using Snowflake and an ETL tool (e.g., Apache NiFi, Talend) to process and load data from multiple sources? 📍 Write a Snowflake SQL script to partition a large table based on a specific column and explain the benefits of doing so. 📍 Explain how you would implement and manage a data mart in Snowflake for the marketing department, including data extraction, transformation, and loading (ETL) processes. 📍 How can you use Snowflake’s data sharing feature to share data with external partners securely? Describe the steps involved. 📍 Discuss how to use Snowflake’s clustering keys to improve query performance. Provide an example of creating and using a clustering key. 📍 How would you automate the process of scaling up and scaling down virtual warehouses in Snowflake based on workload? 📍 Describe the process to migrate an on-premises data warehouse to Snowflake. What are the key considerations and steps? 📍 Write a SQL query to detect and handle duplicate records in a Snowflake table. 🚨 We have opened a few more spots for admission in the "Complete Data Engineering 3.0 With Azure - Basic to Advanced" course. Join now! 👉 Enroll Here: https://lnkd.in/dZSAi93i 🔗Live Classes Starting on 1-June-2024 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary 😎 

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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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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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