Ravish Kumar

Snowflake Optimization Tip of the Day #5 ➡ Use incremental models in dbt There is no simpler way to reduce Snowflake cost than to process less data. If you are using dbt, try to write every model incrementally. Instead of reprocessing all data every time, incremental models only process new or changed data since the last run. This is achieved primarily by using date stamps or other markers to identify what data has been added or modified since the last run. Full-refreshes are very expensive and should be avoided if possible.

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What is SparkSession and Sparkcontext? SparkContext:- Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. Syntax for SparkContext: from pyspark import SparkContext --Create a SparkContext object with local mode and specifying the number of worker threads sc = SparkContext("local[*]", "example") --Now you can use 'sc' to create RDDs and perform operations on them SparkSession:- It’s a unified object to perform all the Spark operations. In the earlier version of the Spark 1.x there were separate objects like SparkContext, SQLContext, HiveContext, SparkConf, and StreamingContext. However with Spark 2.x all these different objects combine into one i.e. the SparkSession. You can perform all those operations using the SparkSession object itself. This unison of all the objects has made life simpler for the Spark Developers. Syntax for SparkSession from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .appName("Name") \ .getOrCreate() Why should use SparkSession over SparkContext? from Spark 2.0, SparkSession provides a common entry point for a Spark application. Instead of SparkContext, HiveContext, SQLContext, everything is now within a SparkSession. it unifies all of sparks numerous contexts. before version 2.0 need to create separate context per JVM. however with SparkSession this problem has been resolved. Check out this databricks interview questions course: https://lnkd.in/dCjUuEGe

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Recently Asked Data Engineering Interview Questions 👇 🔹 Given a table of sales transactions, write a query to calculate the cumulative sales for each product over time. 🔹 How would you handle NULL values in SQL when performing aggregations? 🔹 Explain the concept of data partitioning and its significance in distributed systems. 🔹 How does Apache Spark handle data processing, and what are its advantages over Hadoop MapReduce? 🔹 Explain the role of HDFS in the Hadoop ecosystem. 🔹 What is the purpose of a message broker like Kafka in a data pipeline? 🔹 Write a Python script to merge two sorted lists into a single sorted list. 🔹 How would you handle large datasets in Python to ensure efficient memory usage? 🔹 What are normalization and denormalization in database design? Provide examples of when to use each. 🔹 Explain the concept of serverless computing and its advantages. 🔹 How would you deploy a data pipeline in a cloud environment? 🔹 What is the Data Catalog in AWS Glue? 🔹 Difference between Athena and Aurora. 🔹 What are the key components of a data governance framework? 🔹 Explain the importance of data lineage and how it can be tracked. 🔹 Explain the CAP theorem and its implications for distributed databases. 🚨 We have started the batch of "Data Engineering With AWS" BootCAMP which is high quality, affordable, practical & industry grade project oriented✌🏻We have included Apache Flink, Hudi & Iceberg too😇 👉 Enroll Here - https://bit.ly/3Y5gCJE 🎉 Dedicated placement assistance & doubt support 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 😎 

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Spark Your Data: Understanding RDD Transformations In our ongoing series, "Spark Your Data: RDD Essentials", we’ve covered what RDDs are, how to load data into them, and explored actions. Today, let’s dive into transformations—one of the most powerful features of RDDs in Apache Spark. What Are Transformations? Transformations are operations that take an existing RDD and produce a new RDD. They allow us to reshape, filter, group, or aggregate data to suit our analytical needs. The original RDD remains unchanged because transformations in Spark are lazy—meaning they are not executed immediately but are only triggered when an action is performed. Key Characteristics of Transformations Lazy Evaluation: Transformations are not executed as soon as they are called. Instead, Spark builds a lineage of transformations, which will only be executed when an action like count() or collect() is invoked. Immutable Operations: RDDs are immutable by design. Every transformation creates a new RDD without altering the original one. This ensures consistency and fault tolerance. Chaining: Transformations can be chained together, allowing complex workflows to be expressed concisely. For instance, you can filter, map, and reduce data in a single pipeline. Types of Transformations Transformations can be broadly categorized into two types: Narrow Transformations: These involve computations that depend only on a small subset of the data. Examples include map() and filter(). Since they require data from only one partition, they are generally more efficient. Wide Transformations: These involve data shuffling across multiple partitions, such as reduceByKey() or groupByKey(). These are more resource-intensive as they require data movement across the network. We’ve only scratched the surface of these categories here. In a later article, we’ll delve deeper into narrow and wide transformations, explaining how they work and their impact on performance. The Power of Transformations Transformations allow you to clean, transform, and prepare your data in an intuitive way. For instance, you can: Filter irrelevant data. Map data elements to a new format. Group data for aggregation. All of these can be combined seamlessly, enabling you to write elegant and efficient data pipelines. Starting from the next article, we’ll explore transformations in detail with code snippets and examples from Databricks. Stay tuned as we bring the concepts to life with practical insights, and don’t miss the upcoming deep dive into narrow and wide transformations! Follow me(Raghu Raman A V) for more tech related content such as Spark, Data Engineering and much more ! #ApacheSpark #RDD #BigData #DataEngineering

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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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🗓 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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Day 15 - 30-Day Data Engineering Interview Preparation Series Databricks (#Day15): 20 Databricks Fundamentals interview questions that can be relevant for a data engineering role 👇 📍 What is Databricks, and how does it differ from Apache Spark? 📍 Explain the key features of Databricks. 📍 What are the main components of Databricks architecture? 📍 How do you set up a Databricks workspace? 📍 Describe the process of creating a cluster in Databricks. 📍 What are the different cluster modes available in Databricks? 📍 How do you import data into Databricks? 📍 What are the supported file formats for data import/export in Databricks? 📍 How do you export data from Databricks? 📍 Explain the concept of Delta Lake in Databricks. 📍 What are Delta tables, and why are they useful? 📍 How do you create a Delta table in Databricks? 📍 What are the different operations you can perform on Delta tables? 📍 How do you manage and optimize Delta tables? 📍 Explain the role of PySpark notebooks in Databricks. 📍 How do you create and run a PySpark notebook in Databricks? 📍 What are the common PySpark operations you perform in Databricks? 📍 How do you execute PySpark code on a Databricks cluster? 📍 Explain the concept of workflows in Databricks. 📍 How do you create and manage workflows in Databricks? 🚨 Last 4 days left to join my most affordable and industry oriented "Complete Data Engineering 3.0 With Azure" Bootcamp, ADMISSIONS ARE OPEN for limited seats🔥 👉 Enroll Here (Last Few Seats): https://lnkd.in/dZSAi93i 🔗 Code "DE300" for my Linkedin connections 🚀 Live Classes Starting on 1-June-2024 📲 Call/WhatsApp on this number for career counselling and any query +91 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary 😎 #DataEngineering #SQL #Hive #Hadoop #Kafka #Databricks #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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🚀 Exicted to know about Data Skewness in Pyspark 👉 Spark has data loaded into memory in the form of partitions. Ideally, the data in the partitions should be uniformly distributed. Data skew is when one or some partitions have significantly more data compared to other partitions. Data-skew is usually the result of operations that require re-partitioning the data, mostly join and grouping (GroupBy) operations. 👉 Join operations (except Boradcast-Hash join) can cause data-skew because they require moving data with same join keys in both datasets being joined to the same executor. 👉 Grouping and aggregating on one or more columns requires all data for each value of the column to be present on the same executor, and this requires re-partitioning of the data by grouping columns. But why is it so bad? It depends on the extent of the data-skew, but if one or more partitions have significantly more data than the rest, the result can be performance bottlenecks, and wastage of resources. It can cause the following problems: 🔷 Straggling tasks: 👉 The tasks that are processing these larger partitions can lag-behind others due to the amount of work they have to perform, and can prevent the whole job from moving forward with next stages while they complete their work. 👉 This can result in delays as well as wastage of resources on the cluster because most of the resources become idle waiting for these straggling tasks to complete. 🔷 Spills to Disk and Out of Memory errors: 👉 If data in those partitions do not fit in the memory of the executors that are processing them, Garbage-collection may become a problem. 👉 Spark may have to spill the data on the disk, further slowing down these tasks. In worst case, this can result in Out of Memory (OOM) exceptions and cause jobs to fail. Solutions: ✅ Repartition and Coalesce ✅ Salting Techniques(mostly used) ✅ Iterative broadcast Join Share your thoughts too and tackle data engineering challenges with confidence. #pysparkdataskewness #efficientsolution #Dataengineering

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Scenario Based Pyspark Interview Question!! Part 1 for Salting - Interviewer: Let's discuss some advanced data storage techniques in PySpark. Can you explain what salting and bucketing are, and when you might use each technique? Candidate: Certainly! Salting and bucketing are both methods used for optimizing data storage and querying performance in distributed systems like PySpark. Interviewer: Can you start by explaining what salting is? Candidate: Of course! Salting involves adding a random or pseudo-random value to each key before storing the data. This random value, known as a "salt," helps distribute the data more evenly across partitions, reducing data skew and improving parallelism during query execution. Interviewer: Could you provide an example of how salting works with numbers? Candidate: Sure! Let's say we have a dataset of user IDs ranging from 1 to 1000, but we notice that a few users (IDs 1-10) are generating a disproportionate amount of data. To address this, we can add a random salt value between 1 and 5 to each user ID before storing the data. For example: from pyspark.sql import SparkSession from pyspark.sql.functions import col, concat, lit import random # Create a SparkSession spark = SparkSession.builder \   .appName("Salted User IDs") \   .getOrCreate() # Create a DataFrame with user IDs user_ids = spark.range(1, 1001).select(col("id").alias("User ID")) # Define the number of partitions num_partitions = 5 # Generate random salt values for each user ID salt_values = spark.range(num_partitions).select((random.randint(1, num_partitions)).alias("salt")) # Broadcast salt values to all partitions salt_values_broadcast = salt_values.crossJoin(spark.range(1, 1001)) # Add salt to user ID to create salted user ID salted_user_ids = salt_values_broadcast.withColumn("Salted User ID", concat(col("id"), lit("_"), col("salt"))) # Display the first few rows of the DataFrame salted_user_ids.show() # Stop the SparkSession spark.stop() | User ID | Salted User ID | |---------|----------------| | 1    | 1_3      | | 2    | 2_4      | | 3    | 3_2      | | 4    | 4_5      | | 5    | 5_1      | | 6    | 6_3      | | 7    | 7_4      | | 8    | 8_2      | | 9    | 9_5      | | 10   | 10_1      | | ...   | ...      | | 1000  | 1000_5     | #pyspark #bigdata #dataengineer

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

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

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

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