Vaibhav Kumar

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

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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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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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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! 😎

🌟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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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 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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How the Sort Merge Join Happens in the Apache Spark , Know the SMJ internals Sort-Merge Join (SMJ): In Apache Spark, Sort-Merge Join (SMJ) is the default join strategy for combining datasets based on a shared join key.  It excels at handling large datasets that might not entirely reside in memory on a single worker node. its execution phases: 1. Shuffle Phase: Spark partitions both input DataFrames according to the join key. This ensures records with matching join keys end up on the same worker node. Each partition is shuffled and sent across the network to the appropriate worker nodes based on the join key's hash value. 2. Sort Phase: Within each worker node, the shuffled partitions are sorted independently and in parallel using a stable sorting algorithm (like Timsort) to maintain the order of join keys. 3. Merge Phase: Worker nodes iterate through their sorted partitions simultaneously, performing a merge join operation. They compare the join keys of each DataFrame, and when a match is found, they combine the corresponding rows into a new joined row. This process continues until all matching join keys from both DataFrames have been processed. Example: Imagine you have two DataFrames: customers: (customer_id, name, city) orders: (order_id, customer_id, amount) You want to join them on the customer_id to get customer details along with their orders. Execution Steps: Shuffle: Both customers and orders are partitioned and shuffled based on customer_id. Sort: On each worker node, the shuffled partitions for customers and orders are sorted independently by customer_id. Merge: Worker nodes iterate through their sorted partitions, comparing customer_id values. When a match is found, a new row is created with data from both DataFrames (e.g., (customer_id, name, city, order_id, amount)). Key Points: SMJ works well for datasets that don't fit entirely in memory or where the join key has good cardinality (distinct values). It requires shuffling data, which can be network-intensive. Spark can automatically choose a more efficient join strategy like broadcast join if one DataFrame is significantly smaller than the other. Additional Considerations: You can configure the number of shuffle partitions using spark.sql.shuffle.partitions. SMJ can be optimized by using in-memory sorting if data fits in memory (spark.sql.sortMergeJoin.forceInMemory=true). For very large datasets, Spark might spill intermediate results to disk during shuffling and sorting, potentially impacting performance. TrendyTech #Spark, #BigData, #DataEngineering, #SoftwareEngineering

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I found some usefull new features in pyspark. Which will help everyone in day to day work. 👉 1. Parameterised Query. sparkDf = spark.createDataFrame([ ("Java", 2012, 20000), ("dotNET", 2012, 5000), ("dotNET", 2013, 48000), ("Java", 2013, 30000)], schema=("course", "year", "earnings")) spark.sql("select max(earnings) from {df}", df=sparkDf).show() 👉2. withColumns(). df = sparkDf.withColumns({"Country":lit("BHARAT"),"Pin":lit("332422")}) 👉3. array_compact(). sparkDf = spark.createDataFrame([ (["java","python",None], 2012, 20000), ([None,"python",None], 2012, 5000), (["dotNet",None], 2013, 48000), (["Java","C++"], 2013, 30000)], schema=("courses", "year", "earnings")) sparkDf.withColumn("courses", array_compact(col('courses'))) \ .show() 👉4. lit(). country_df = sparkDf.withColumn("Country", lit("BHARAT")) 👉5. max_by(). sparkDf.groupby("course").agg(max_by("year", "earnings")).show() ''' In sql :- select course, max_by(year,earnings) from students group by course; ''' 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍 𝑬𝒗𝒆𝒓𝒚𝒅𝒂𝒚, 𝑰 𝒍𝒆𝒂𝒓𝒏 𝒂𝒏𝒅 𝒔𝒉𝒂𝒓𝒆 𝒔𝒕𝒖𝒇𝒇 𝒂𝒃𝒐𝒖𝒕: 🌀 Data Engineering 🌀 Python/SQL 🌀 Databricks/Pyspark 🌀 AWS 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/gbmdY42q 👉𝐅𝐨𝐥𝐥𝐨𝐰 Shiva Pandey 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭. #DataEngineeringInterview #ApacheSpark #InterviewPrep #DataEngineering #TechInterviewTips #data #dataengineering #dataengineerjobs #interviewquestions #interviewprep #newlearnings #interviewpreparation #interviewtips #dataengineers #interviewsuccess #followme #ETL #dataengineerjobs #spark #azurecloud #python #datacleaning #pyspark #optimization #interviewsuccess #interviewskills #DataEngineering #DataScience #intrested #LinkedInLearning #modi #indiaelection #election #DataProcessing #BigData #InterviewPrep #TechCareer #DataJobs #KnowledgeSharing #TechSkills #BigData #LearningResources #LinkedInLearning #DataScience #PySparkFunctions #CareerDevelopment #mapreduce #interviewguidance #interviewingskills #PySpark #DataCleaning #DataScience #LinkedInLearning #certified #certification #azurecertified #codingchallenge #hadoop #hadoopadministration #hadoopdeveloper #intrested #DeltaLake #DataLakes #BigData #DataEngineering #DataScience #DataManagement #LinkedInLearning #DataInfrastructure #AI #ChatGpt #Google #Microsoft Krish Naik, Delta Lake, Databricks, Apache Spark, W3Schools.com, Darshil Parmar, PySpark NugGits

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Real-Time Indexing at Scale: How Meesho Leveraged Apache Flink 🚀 At Meesho, they support millions of entrepreneurs across Bharat. With over 50 million products in their catalog, keeping product data fresh in real-time is essential. Delays in indexing lead to users seeing outdated prices or out-of-stock items, causing frustration, cart abandonment, and potential customer loss 😯 Here's how we overhauled our indexing architecture using Apache Flink for horizontal scalability 👇🏻 They started with a crontab-based approach, scheduling indexing jobs every 10 minutes. It worked well initially, with indexing taking 5–7 minutes for our smaller product base 🕑 The Scaling Challenge 📈 As Meesho grew, so did their problems 👇🏻 1️⃣ Exponential Growth: The number of products exploded into tens of millions, pushing indexing time to 4 hours. 2️⃣ Data Lag: Users saw outdated prices and stock information, especially during peak sales. 3️⃣ Processing Inefficiency: Recomputing all product data—even if unchanged—added unnecessary load. 4️⃣ Failure Delays: Job failures caused delays of up to 8 hours. Interim Fix: Kafka Consumers ✅ While plotting a long-term solution, they implemented Kafka consumers to 👇🏻 1️⃣ Parallelize Elasticsearch Writes: This reduced ES write time from 2 hours to 15 minutes, cutting overall indexing time to ~3 hours. 2️⃣ Adopt Batch Processing: Catalogs were sent directly to Kafka for immediate indexing, reducing memory consumption. Though this helped temporarily, as their catalog grew, indexing time soon crept back up to 4 hours. Requirements for the New Solution ✍🏻 They needed a system that was 👇🏻 1️⃣ Low-latency & fault-tolerant: Processing events in seconds, not hours. 2️⃣ Decoupled: Separating indexing from the enrichment pipeline. 3️⃣ Extensible: Supporting multi-consumer queues and future data flow operations. Why They Chose Apache Flink ❓ After evaluating several options, Apache Flink stood out for 👇🏻 1️⃣ Exactly-once processing guarantees: Ensuring data consistency. 2️⃣ Very low latency: Even with fault tolerance enabled. 3️⃣ High throughput: Capable of processing 182 million records/sec across 30 nodes. Flink vs. Storm: Performance Benchmark 😎 1️⃣ Flink (no fault tolerance): 182M records/sec, 0 ms latency. 2️⃣ Flink (with 5 sec checkpoints): 178M records/sec, 0 ms latency. 3️⃣ Storm (no fault tolerance): 9.8M records/sec, 11 ms latency. 4️⃣ Storm (with fault tolerance): 0.57M records/sec, 30–120 ms latency. 5️⃣ Flink had 300× higher throughput than Storm, even with fault tolerance. ✅ Read Full blog - https://lnkd.in/gQS_WyQt 🚨 Join my high quality, industry & modern tech stack based and practical project driven Data Engineering BootCAMP✌🏻👇 👉 Enroll Here - https://bit.ly/4eA2tuX 🚀 Dedicated Doubt Support & Placement Assistance 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills 🙂 #dataengineering

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StructType and StructField Classes in PySpark .By inferring the Schema Spark will create one Extra Job .And Spark will Scan entire rows From Top To Bottom and It will Take some time and decrease The Performance We can Create Schema Manually By Using StructType And StructFileld Class # schema definition Programatically Way. Schema on Write i.e Manually # We can create a Schema By Using The StructType And StructField 1.StructType .Schema is a StructType Made up Number of fields .List Of an array Of structFields It is Collection of structField Objects 2.StructField .It contain Single Field In a Schema Contain Name Type And Boolean Flag .Boolean Flag Indicating whether null Values Are Allowed or Not. .If Allowed Then Set True If Not Allowed Set False from pyspark.sql.functions import* from pyspark.sql.types import StructType,StructField,StringType,IntegerType # Define The Schema Using Struct Type And Struct Field MplSchema=StructType([ StructField("Id",IntegerType(),True),#Name of Field Id Of Type Integer StructField("First_Name",StringType(),True),#Name Field Of Type String StructField("Last_Name",StringType(),True),#Last_Name Field Of Type String StructField("Age",IntegerType(),True),#Age Field Of Type Integer StructField("Team",StringType(),True),#Team Field Of Type String StructField("Runs",IntegerType(),True)# Runs Field Of Type Integer ]) # Creating The DataFrame MplData=[ (1,"Vishal","Jare",26,"Beed",6334), (2,"Vivek","Malawade",25,"Latur",6244), (3,"Saurabh","mamidwar",25,"Nanded",5883), (4,"Ajit","Pawar",26,"Beed",5881), (5,"Shubham","Kadam",27,"Nashik",5536) ] # Here I am Creating dataFrame By using The cretateDataFrame MplDataFrame=spark.createDataFrame(data=MplData,schema=MplSchema) .Show The DataFrame Here I am Trying To get result So Show is Action it will create One JOB and DAG MplDataFrame.show() +---+----------+------------+---+------+----+ | Id | First_Name|Last_Name|Age| Team|Runs. | +-+---------- +------------+---+------+-----+ | 1| Vishal | Jare. | 26 | Beed |6334 | | 2| Vivek | Malawade. | 25 | Latur | 6244 | | 3| Saurabh | mamidwar. | 25 |Nanded|5883 | | 4| Ajit. | Pawar. | 26 | Beed. |5881 | | 5| Shubham| Kadam. | 27 |Nashik|5536 | +---+----------+---------+---+------+-------+

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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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Finally, It's out - Snowflake Academy by Enqurious Academy Why Snowflake? It's one of the fastest growing Data platform that's ridiculously SQL friendly. So, anyone with a SQL background and needs to learn a Data Warehousing solution in an easy way, Snowflake is the best place to start. Why Enqurious? Having upskilled 5000+ data engineers with a certification clearance rate in excess of 95%, we have built learning experiences across domains like Retail, CPG, Insurance, Banking and many more. What do you need to pay to get started? Your attention, commitment and time. It's free otherwise How to start? Visit Snowflake Academy here : https://lnkd.in/gD_ruthp Or join our Slack community here to start : https://lnkd.in/ggaGpcmn Pick a short scenario, a Masterclass or a full length Project, whatever serves your needs or tastes. #learningexperience #DWaaS #SnowflakeAcademy

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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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👋 Hello Data Engineers! In our Spark Optimization Series, we’ve discussed Kafka, PostgreSQL, and MongoDB. Today, let’s explore Parquet optimizations: 1. Cost Savings: Compress data with formats like Snappy to reduce storage needs. 2. Faster Queries: Use features like predicate pushdown and column pruning to process only relevant data. 3. Efficient Organization: Partition data by key columns (e.g., year, month) for better performance. 4. Parquet’s columnar format is perfect for big data, offering improved storage and query efficiency. Missed the series? Check out our previous posts for more insights. Stay tuned for more Spark tips! 📊✨ #datatroops #spark #parquet #bigdata #optimization #datascience

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𝐃𝐞𝐚𝐫 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 🔥 Happy Monday!! Here I come with another PySpark Transformation Challenge 📣 𝐈𝐧𝐟𝐨: You have a DataFrame with two columns: 𝒆𝒎𝒑𝒍𝒐𝒚𝒆𝒆_ 𝒊𝒅 and 𝒄𝒐𝒏𝒕𝒂𝒄𝒕_𝒅𝒆𝒕𝒂𝒊𝒍𝒔. The 𝒄𝒐𝒏𝒕𝒂𝒄𝒕_𝒅𝒆𝒕𝒂𝒊𝒍𝒔 column contains unstructured text with potential phone numbers (Let's assume 10 digits without country code) and email addresses. 𝐘𝐨𝐮𝐫 𝐭𝐚𝐬𝐤 𝐢𝐬 𝐭𝐨: 1. Extract the phone numbers (10 digits) and email addresses from the contact_details column. 2. Create two new columns, phone_number and email_id, based on the extracted values. 3. If no phone numbers or email addresses are found, the corresponding values should be null. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐚𝐦𝐩𝐥𝐞 𝐃𝐚𝐭𝐚𝐟𝐫𝐚𝐦𝐞 (Input Data): 𝘥𝘢𝘵𝘢 = [ ("𝘌001", "𝘑𝘰𝘩𝘯 𝘸𝘰𝘳𝘬𝘴 𝘢𝘵 𝘈𝘉𝘊 𝘊𝘰𝘳𝘱. 𝘊𝘰𝘯𝘵𝘢𝘤𝘵: 9876543210"), ("𝘌002", "𝘈𝘯𝘯𝘢'𝘴 𝘦𝘮𝘢𝘪𝘭 𝘪𝘴 𝘢𝘯𝘯𝘢.𝘴𝘮𝘪𝘵𝘩@𝘨𝘮𝘢𝘪𝘭.𝘤𝘰𝘮. 𝘏𝘦𝘳 𝘱𝘩𝘰𝘯𝘦 𝘪𝘴 9123456789"), ("𝘌003", "𝘕𝘰 𝘤𝘰𝘯𝘵𝘢𝘤𝘵 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦."), ("𝘌004", "𝘙𝘦𝘢𝘤𝘩 𝘮𝘦 𝘢𝘵 9234567890 𝘰𝘳 𝘷𝘪𝘢 𝘮𝘢𝘪𝘭 𝘢𝘭𝘪𝘤𝘦.𝘫𝘰𝘩𝘯𝘴𝘰𝘯@𝘹𝘺𝘻.𝘤𝘰.𝘶𝘬") ] 𝘥𝘧 = 𝘴𝘱𝘢𝘳𝘬.𝘤𝘳𝘦𝘢𝘵𝘦𝘋𝘢𝘵𝘢𝘍𝘳𝘢𝘮𝘦(𝘥𝘢𝘵𝘢, ["𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘦_𝘪𝘥", "𝘤𝘰𝘯𝘵𝘢𝘤𝘵_𝘥𝘦𝘵𝘢𝘪𝘭𝘴"]) 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐝𝐞? (Expected Data) Let me know the answer in the comments 😀 #pyspark #dataengineering

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Spark Lazy Evaluation!! Certainly! Lazy evaluation in Apache Spark is a concept where Spark delays the execution of transformations until it's absolutely necessary. Instead of processing data immediately when a transformation is called, Spark builds up a directed acyclic graph (DAG) of the transformations and waits until an action is invoked to trigger the actual computation. Interviewer: Let's start with the basics. Can you explain what lazy evaluation means in the context of Apache Spark? Candidate: Sure. Lazy evaluation means that Spark doesn't execute transformations right away when they're called. Instead, it builds up a logical execution plan, known as a DAG, to represent the sequence of transformations. The actual execution doesn't occur until an action is invoked on the RDD. Interviewer: Excellent explanation. Now, why is lazy evaluation important in Spark? Candidate: Lazy evaluation offers several advantages. Firstly, it helps to optimize performance by allowing Spark to optimize the execution plan before actually executing it. This optimization can include fusion of operations, predicate pushdown, and other optimizations that improve efficiency. Interviewer: That's spot on. Lazy evaluation indeed enables Spark to optimize the execution plan based on the entire DAG rather than just individual transformations. Can you elaborate on any other benefits of lazy evaluation? Candidate: Certainly. Lazy evaluation also improves fault tolerance. Since Spark maintains the lineage information of RDDs, if a partition of data is lost due to a node failure, Spark can efficiently recover it by recomputing only the lost partition rather than rerunning the entire transformation chain. Interviewer: Good point. Lazy evaluation coupled with RDD lineage indeed enhances fault tolerance, making Spark resilient to failures. Now, let's talk about any potential drawbacks or challenges associated with lazy evaluation. Candidate: One challenge is debugging. Since transformations are not executed immediately, errors may not surface until an action is triggered. This can make it more challenging to pinpoint the source of errors in the code. Interviewer: Absolutely. Debugging can indeed be more challenging with lazy evaluation, especially when dealing with complex transformations. However, proper logging and debugging techniques can mitigate this challenge to a certain extent. Candidate: Agreed. Despite the challenges, the benefits of lazy evaluation in terms of performance optimization and fault tolerance make it a crucial aspect of Apache Spark's architecture. Interviewer: Well said. Lazy evaluation is indeed a fundamental concept in Spark, enabling efficient distributed data processing while ensuring fault tolerance and optimization. Thank you for the insightful discussion. Candidate: Thank you for the opportunity to discuss this topic. It was a pleasure. #spark #bigdata

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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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