Sunil Krishna Mandadapu

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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Good Evening All, Here my new blog is up for you to solve the question if you are lucky enough to !!!!!! Read and solve the question !! Waiting for your answer... SQL - The King of Data

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Day 21 - 30-Day Data Engineering Interview Preparation Series Data Warehousing (#Day21): 25 Data Warehousing interview questions that can be relevant for a data engineering role 👇 📍 What is a Data Warehouse? 📍 Differentiate between OLTP and OLAP systems. 📍 Explain the ETL process. 📍 What are the key components of a Data Warehouse architecture? 📍 What is a Star Schema? 📍 What is a Snowflake Schema? 📍 Explain the concept of a Fact Table. 📍 What is a Dimension Table? 📍 What is a Data Mart? 📍 Describe slowly changing dimensions (SCD) and their types. 📍 What are the different types of Fact Tables? 📍 How do you handle data quality issues in a Data Warehouse? 📍 Explain the concept of data normalization and denormalization. 📍 What is the role of a Data Warehouse in Business Intelligence? 📍 Describe the Kimball and Inmon approaches to Data Warehouse design. 📍 What is a surrogate key in a Data Warehouse? 📍 How do you optimize query performance in a Data Warehouse? 📍 What are Materialized Views and how are they used in Data Warehousing? 📍 Explain the concept of Data Staging in a Data Warehouse. 📍 What is the difference between a Data Lake and a Data Warehouse? 📍 How do you handle late-arriving dimensions in a Data Warehouse? 📍 Explain the concept of partitioning in a Data Warehouse and its benefits. 📍 How do you implement incremental data loading in a Data Warehouse? 📍 What strategies do you use for data archival and purging in a Data Warehouse? 📍 Describe the process of data lineage and its importance in a Data Warehouse environment. 🚨 We have kicked off our most affordable and industry-oriented "Complete Data Engineering 3.0 with Azure" Bootcamp. Admissions open! 🔥 👉 Enroll Here: https://lnkd.in/dZSAi93i 🔗Live Classes Starting on 1-June-2024 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary 😎 #DataEngineering #SQL #Hive #Hadoop #Kafka #Databricks #Pyspark #AdvancedSQL #DataWarehousing #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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Title : Explain the ETL process. How does it differ from ELT? Understanding ETL and ELT Processes ETL (Extract, Transform, Load): Extract: This is the first step where data is collected from various sources such as databases, cloud storage, or other systems. The goal is to gather all the relevant data needed for analysis. Transform: In this stage, the extracted data is cleaned, enriched, and transformed into the desired format. This can include filtering, aggregating, and converting data to ensure consistency and compatibility with the target system. Load: Finally, the transformed data is loaded into the target system, such as a data warehouse, where it can be accessed for analysis and reporting. ELT (Extract, Load, Transform): Extract: Similar to ETL, data is extracted from various sources. Load: Instead of transforming data before loading, ELT loads the raw data directly into the target system. Transform: Transformation happens within the target system, leveraging its processing power and scalability. This approach is often used in cloud-based data warehouses where resources can be scaled on demand. Key Differences: Transformation Timing: ETL: Transforms data before loading. ELT: Transforms data after loading. Use Cases: ETL: Preferred for on-premises databases and scenarios where data transformation needs to happen before loading to ensure data quality and compliance. ELT: Ideal for cloud-based data warehouses where you can utilize the powerful processing capabilities to transform large datasets after loading. Performance: ETL: Can be slower for large datasets since transformation happens before loading. ELT: Often faster for big data as the transformation leverages the scalable processing power of the target system. In summary, ETL is best when pre-loading transformations are necessary, while ELT is advantageous for leveraging the scalable compute resources of modern data warehouses. Choose the process that aligns with your infrastructure and data processing needs. #DataScience #ETL #ELT #BigData #DataWarehouse #DataEngineering #CloudComputing #BusinessIntelligence #DataTransformation

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🚀 **Understanding Slowly Changing Dimensions (SCD) in Data Warehousing** 🧠 In the world of data warehousing, handling changes to dimensional data over time is crucial. This is where Slowly Changing Dimensions (SCD) come into play! 💡 **What is SCD?** SCD refers to the method of managing and tracking changes in dimension data in a data warehouse. Unlike fact data, which changes rapidly, dimension data tends to change slowly and unpredictably, making it vital to have strategies to manage these changes. 🔍 **Types of SCD:** 1. **SCD Type 0 (Retain Original):** The original data is never updated. Historical data is preserved as-is. 2. **SCD Type 1 (Overwrite):** The old data is overwritten with new data, with no historical data retained. 3. **SCD Type 2 (Add New Row):** A new record is added to the dimension table with a new version of the data, preserving the history. 4. **SCD Type 3 (Add New Attribute):** A new column is added to store the previous value of the data, partially preserving the history. 5. **SCD Type 4 (Add Historical Table):** A separate historical table is created to store the old data. 6. **SCD Type 6 (Hybrid):** Combines SCD Types 1, 2, and 3 to track changes and retain historical data in different ways. 📊 **Why SCD Matters?** Understanding and implementing the right SCD strategy ensures that your data warehouse accurately reflects changes over time, allowing for better analysis and decision-making. #DataWarehouse #SCD #DataEngineering #BusinessIntelligence #DataManagement

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Day 24 - 30-Day Data Engineering Interview Preparation Series GCP BigQuery (#Day24): 20 GCP BigQuery interview questions that can be relevant for a data engineering role 👇 📍 How would you design the schema for a large-scale analytics project in BigQuery? 📍 Explain how you would ingest data from various sources into BigQuery. 📍 What are the benefits of partitioning in BigQuery and how would you implement it? 📍 How does clustering work in BigQuery, and when would you use it? 📍 What techniques would you use to optimize query performance in BigQuery? 📍 How do you work with nested and repeated fields in BigQuery? 📍 Explain the process and use cases for streaming data into BigQuery. 📍 What are federated queries in BigQuery and how would you use them? 📍 How do you manage access control and security in BigQuery? 📍 What strategies do you use to manage and reduce costs in BigQuery? 📍 How would you export data from BigQuery to another storage or processing system? 📍 How do you integrate BigQuery with other GCP services like Cloud Storage, Dataflow, and Pub/Sub? 📍 How would you implement user-defined functions (UDFs) in BigQuery? 📍 What are materialized views in BigQuery, and how do you use them? 📍 How do you handle schema changes in BigQuery? 📍 Explain how you would use BigQuery ML for a machine learning project. 📍 How do you use scheduled queries in BigQuery? 📍 Describe the process of setting up a data pipeline with BigQuery. 📍 How do you monitor and troubleshoot performance issues in BigQuery? 📍 What are the different types of joins available in BigQuery, and how do you choose which one to use? 🚨 Don't miss out! New seats just opened for the Complete Data Engineering 3.0 With Azure course! 👉 Enroll Here: https://lnkd.in/dZSAi93i Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 😎 #DataEngineering #SQL #Hive #Hadoop #Kafka #Databricks #Pyspark #AdvancedSQL #gcp #bigquery #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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Hey there! Keen to learn and practice your SQL skills without paying a penny and having some fun at the same time? I've got your back!! In this blog you'll find step-by-step instructions on getting MySQL Server & Workbench set up on your computer so you can practice along the 5 Parts of the series ‘SQL & Analysis Fundamental Concepts’ We will go through the most basic stuff while dropping more advanced knowledge all around, because that’s how we get the broader picture and gain a deeper understanding. But buckle up, because this isn't your typical course. We will get our hands dirty, explore mistakes, stumble upon wrong codes, and unravel the why on earth am I getting this error. It's all about learning from the ground up, finding multiple solutions to tackle the same problems, and ultimately making sense of SQL with an analyst mindset. Put on your Indiana Jones hat and join me in Coding for Analysis https://lnkd.in/dAnQ2eNN

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Below is a simplified roadmap for learning Data Science for Free ✅ Share with everyone so that everyone get benifts No Payment required ✅ 🔹 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔺https://lnkd.in/gkDdTTz6 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬: - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/ge4qRdPB 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. Python: https://lnkd.in/gk4aMi6k R language: https://lnkd.in/g92J57iJ SQL: https://lnkd.in/gwEgvWpe MongoDB: https://lnkd.in/gpEVXGCC 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. Probability: https://lnkd.in/g3-qQtrJ Statistics: https://lnkd.in/gjkW-i58 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. 🪢 https://lnkd.in/gFn7fftr 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🪢 https://lnkd.in/gMKZhbkd 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: - Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models. Feature Engineering : https://lnkd.in/ghziZsnF 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: - Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform. - Build web applications with Flask or Django. Microsoft Azure: https://lnkd.in/gMp5CXZJ Google Cloud: https://lnkd.in/gVaZtuC7 Flask: https://lnkd.in/g2PVuY2p Flask Project: https://lnkd.in/gD7-zEuH Django: https://lnkd.in/gRVFmwkQ #datascience #datascienceroadmap #datascience

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10 recently asked SQL questions for Data Engineer 2024. ➡️ How would you use window functions in SQL to calculate a moving average over a set of rows grouped by a specific column? ➡️ Write a query using a CTE to find the nth highest salary in a company. ➡️ Explain how you would optimize a slow-running query. Include your approach to indexing and any specific SQL features you might use. ➡️ Describe how you would design a database schema for a large-scale e-commerce platform, focusing on the order management system. Include considerations for scalability and data integrity. ➡️ Given a specific query, identify potential performance bottlenecks and suggest optimizations to improve its execution time. ➡️ Explain the importance of ACID properties in database transactions and how you would implement them in a SQL environment. ➡️ How would you optimize a query that involves multiple joins and subqueries to reduce execution time without altering the output? ➡️ Discuss the differences between OLTP and OLAP systems. How would you structure a query to efficiently retrieve aggregated data from a large data warehouse? ➡️ Explain the concepts of partitioning and sharding in databases. How would you apply these concepts to manage and query large datasets efficiently? ➡️ Write a SQL query that uses advanced aggregation functions (e.g., ROLLUP, CUBE) to provide insights into sales data, such as total sales per region, per product, and overall. 🚀 Join the Generative AI Projects Bootcamp at Grow Data Skills! We’ve launched a 4-week Bootcamp where you'll learn to build and deploy 4 End-to-End Generative AI Projects—all through live classes. 🔥 Hurry! Secure your spot today and elevate your AI skills! Course Curriculum - https://lnkd.in/d6mZ2wWt Enrollment Link - https://lnkd.in/dyws2EFf Flat 50% OFF Coupon Code: EARLY50 Course Start Date: 28th September 2024 📲 For any queries, call or WhatsApp us at (+91) 9893181542 Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Shubhankit Sirvaiya Sahil Choudhary Aman Kumar

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Hi friends just saw the problem statement of SQL in Ankit Bansal recent youtube video about 'Games of throne' asked by Harsh. I tried to do it in my way before watching whole video and luckily I found my solution similar to Ankit Bansal mentos solution. solution:- (cte+temp table+window ()+ joins) select * , case when attacker_outcome = 1 then attacker_king else defender_king end as winner into a  from battle; with t1 as (                                        select k.house , a.region, count(*) no_of_wins               ,rank() over(partition by region order by count(*) desc) rnk           from a        join king k on a.winner = k.k_no                          group by k.house , a.region       )  select house, region, no_of_wins from t1      where rnk=1  

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Short roadmap to learn Tableau 👇 1. Getting Started:   - Download and install Tableau Public (free) or Tableau Desktop (trial version).   - Explore the Tableau interface to get familiar with its components. 2. Data Connection:   - Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services. 3. Data Preparation:   - Understand how to clean and shape data in Tableau using the Data Source tab. 4. Basic Visualization:   - Create simple visualizations like bar charts, line charts, and scatter plots. 5. Calculations:   - Learn about calculated fields and basic functions for more complex data transformations. 6. Dashboards and Stories:   - Explore creating interactive dashboards and stories to present your insights effectively. 7. Advanced Visualizations:   - Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts. 8. Advanced Calculations:   - Master advanced calculations, such as level of detail (LOD) expressions and table calculations. 9. Mapping:   - Learn how to create maps and geospatial visualizations using Tableau's mapping features. 10. Data Blending:   - Understand how to blend data from multiple sources for comprehensive analysis. 11. Performance Optimization:   - Optimize the performance of your Tableau workbooks for larger datasets. 12. Tableau Server:   - If needed, explore Tableau Server for collaboration and sharing. 13. Projects:   - Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage. 14. Certification:   - Consider pursuing Tableau certification for formal recognition of your skills. Grow Data Skills Shashank Mishra 🇮🇳 Sahil Choudhary

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Hello connections! I'm thrilled to share the SQL 8 Week Challenge - Case Study #4: Data Bank... Aim of this Case Study📌: The aim of this case study was to help the management team at Data Bank track how much data storage their customers will need for their digital bank. This case study focuses on calculating metrics, growth, and assisting businesses in analyzing their data intelligently in order to better forecast and plan for future improvements. There were 3 key datasets for this case study: Regions Customer Nodes Customer Transactions Tool Used📌: - POSTGRESQL  Key insights🔍: i) Customer Acquisition and Growth: Tracked the number of customers and identified regions with higher customer acquisition. ii) Data Storage Usage and Forecasting: Analyzed storage needs based on account balances. This helps understand the relationship between account size and storage needs. iii) Customer Segmentation and Targeting: Identified customers with high account balances and potentially high storage needs. Also customer transaction history to identify inactive accounts. Learnings✍: - Aggregate functions - Window Functions - Joins - Common table expressions(CTE's) and subqueries Challenge link :  https://lnkd.in/dWYzkPGJ Shoutout to Danny Ma for such wonderful #SQL challenges. It surely leveled up my SQL skills.  The challenge definitely set me up to keep learning about the different applications SQL has to offer in my journey as a data analyst. #SQLJourney  #30Daysofcode #Dataanalytics #datawithdanny

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PepsiCo Interview Question: 🌟 Implementing a Star Schema: Key Table Types to Know 🌟 Question: In data warehousing, the star schema is a powerful and efficient way to structure your database for analytics. But what are the essential table types you need to make it work? My Answer: 🔑 1. Dimension Table: Usage: Stores descriptive data that provides context to the facts in the fact table. Examples: Product details, customer information, time periods, etc. 🔑 2. Fact Table: Usage: Contains the quantitative metrics or measures of the business. Examples: Sales figures, revenue, quantities sold, etc. Linked to dimension tables, it holds the actual business data we want to analyze. If you’re working on a data warehouse project, mastering these table types is key to delivering scalable and efficient solutions! #DataWarehousing #StarSchema #FactTable #DimensionTable #DatabaseDesign #DataEngineering #Analytics #BI #DataStrategy

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This will help aspirants and career transitioners 👇🏽 Know the difference between Domains and functions [1] 𝗗𝗼𝗺𝗮𝗶𝗻𝘀 are nothing but the industries. ⏩ Banking, Health Care, E-Commerce, FMCG, E-Commerce etc. are some of the fastest growing domains that need data analytics. ⏩They need data analytics to measure and improve their Revenue, Profit, Market Share etc. These are called KPIs (Key Performance Indicators) --------- [2] 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 are the departments in each of these domains. ⏩ HR, Supply Chain, Finance, Sales, Marketing etc. are the functions where data analytics is gaining traction. ⏩ Each of these functions will have their own sub KPIs that will directly or indirectly contribute to revenue/profit/market share etc. ----------- If you have a functional knowledge, you can become one of these analysts DA + HR Knowledge➡️HR Analyst DA + Sales Knowledge➡️Sales Analyst DA + Finance Knowledge➡️Finance Analyst DA + Research Knowledge➡️Research Analyst DA + Marketing Knowledge➡️Marketing Analyst DA + Supply Chain Knowledge➡️Supply chain Analyst and if you have a specific domain knowledge it gives you better career chances For example, ✅ Finance Analyst in FMCG domain ✅Sales Analyst in Healthcare domain If you are a fresher ⏩Build more functional / domain knowledge ⏩By doing more projects & research If you are a careeer transitioner ⏩Learn data skills and ⏩Apply for jobs relevant to your domain/function So that you don't need to apply as a fresher. ----- Start working to improve your knowledge on Domains and functions Adding free resources below 👇🏽

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7 step process to visualise data on tableau Elite Global AI day 8 of internship 1. Download and install Tableau Desktop. 2. Select your data source, such as Excel or SQL. 3. Clean and structure your dataset by removing duplicates and handling missing values. 4. Drag dimensions and measures onto the canvas to create bar charts or line graphs. 5. Combine multiple visualizations into a cohesive dashboard for a comprehensive view. 6. Implement filters and actions for dynamic data exploration. 7. Save your work and share it via Tableau Public or Server. For instance, imagine having to visualize sales data by creating a bar chart that compares the profits across different regions. This approach not only enhances understanding but also drives informed decision-making!

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Day 22: 30-Day Data Engineering Interview Preparation Series Snowflake (#Day22): 20 Snowflake Fundamental interview questions that can be relevant for a data engineering role 👇 📍 What is Snowflake and how is it different from traditional data warehouses? 📍 Can you explain Snowflake's architecture? 📍 What are the key features of Snowflake? 📍 How does Snowflake handle data storage and processing? 📍 What is the concept of virtual warehouses in Snowflake? 📍 How does Snowflake's data sharing work? 📍 Explain the concept of micro-partitions in Snowflake. 📍 How does Snowflake ensure data security and compliance? 📍 What are Snowflake stages and how are they used? 📍 Can you describe the Snowflake query execution process? 📍 What is Time Travel in Snowflake and how does it work? 📍 Explain the concept of zero-copy cloning in Snowflake. 📍 How does Snowflake support semi-structured data? 📍 What are some best practices for optimizing queries in Snowflake? 📍 How does Snowflake's pricing model work? 📍 What are the different types of Snowflake accounts? 📍 How do you handle data loading and unloading in Snowflake? 📍 Can you explain Snowflake's data retention policies? 📍 What is the role of the Information Schema in Snowflake? 📍 How does Snowflake integrate with other data tools and platforms? 🚨 We have kicked off our most affordable and industry-oriented "Complete Data Engineering 3.0 with Azure" Bootcamp. Admissions open! 🔥 👉 Enroll Here: https://lnkd.in/dZSAi93i 🔗Live Classes Starting on 1-June-2024 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary #DataEngineering #SQL #Hive #Hadoop #Kafka #snowflake #Databricks #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐰𝐡𝐞𝐧 𝐭𝐡𝐞 𝐜𝐥𝐨𝐮𝐝 𝐟𝐢𝐫𝐬𝐭 𝐭𝐨𝐨𝐤 𝐬𝐡𝐚𝐩𝐞? 𝐎𝐫 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐮𝐬𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭? 🤔 ➡ Before the cloud, businesses depended on physical servers and on-premise data centers to store and manage their data. This approach was time-consuming, expensive, and required constant maintenance. ➡ But everything changed with the advent of the cloud in the early 2000s. Suddenly, companies could access unlimited storage, scale resources on demand, and reduce costs—all without owning a single server. In this post, we’ll explore how the cloud began and what came before it. Whether you’re a tech fan or just curious, you’ll gain a quick, clear understanding! Thank you to each member: ANALYTICSWITHANAND , Sayantan N.,Ayush Tijare, Nilakanta S, Namita kumari , Vishal Shinde, Dhanashree More, Vaibhav Limkar, Rupesh Jadhav, Shubham Awari, Pratik Bele,Rohit Pratap Singh Special thanks to Anand Jha Sir for his expert guidance and to ANALYTICSWITHANAND classes for their invaluable support. #DataAnalytics #Cloud #PowerBi #AWS #CloudComputing #TechHistory #DigitalTransformation #DataCenters #Azure

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