Future Tech Skills

Future Tech Skills

Technology, Information and Internet

C-25, Sector- 58, Noida, Uttar Pradesh 7,408 followers

Learn, Grow, Innovate with IT Training "Empowering Your Tech Future, Today."

About us

At 𝐅𝐮𝐭𝐮𝐫𝐞 𝐓𝐞𝐜𝐡 𝐒𝐤𝐢𝐥𝐥𝐬 , we offer industry-leading courses in 𝗖𝗹𝗼𝘂𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴, 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗲𝘀𝘁𝗶𝗻𝗴, 𝗘𝗧𝗟 𝗧𝗲𝘀𝘁𝗶𝗻𝗴, 𝗘𝗧𝗟 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 ,𝗔𝗣𝗜 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 ,𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗳𝗿𝗼𝗺 𝗕𝗮𝘀𝗶𝗰 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) . Our unique approach equips you with the skills and experience to succeed in the tech industry, With 100% placement assistance to help you land your dream job. 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗨𝘀? * 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Gain practical experience through project-based training. * 𝗘𝘅𝗽𝗲𝗿𝘁 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽: Learn from industry professionals committed to your success. * 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗦𝗸𝗶𝗹𝗹𝘀: Master in-demand technologies with our structured curriculum. * 𝟭𝟬𝟬% 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲: Receive the guidance you need to secure your dream job. * 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Access live online courses and a personalized learning roadmap. *𝐎𝐔𝐑 𝐓𝐑𝐀𝐈𝐍𝐈𝐍𝐆 𝐏𝐑𝐎𝐆𝐑𝐀𝐌𝐒* 1. 𝐒𝐐𝐋 (𝐁𝐚𝐬𝐢𝐜 𝐓𝐎 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝) 2. 𝐏𝐲𝐭𝐡𝐨𝐧 3. 𝐄𝐓𝐋 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 4. 𝐄𝐓𝐋 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 5. 𝐀𝐖𝐒 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 6. 𝐀𝐳𝐮𝐫𝐞 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 7. 𝐄𝐓𝐋 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐐𝐮𝐞𝐫𝐲𝐒𝐮𝐫𝐠𝐞 8. 𝐁𝐢𝐠𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 9. 𝐃𝐚𝐭𝐚 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 10. 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 5 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 11. 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐚 12. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 13. 𝐓𝐚𝐛𝐥𝐞𝐚𝐮 14. 𝐀𝐏𝐈 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 15. 𝐀𝐏𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 "𝗟𝗘𝗧’𝗦 𝗔𝗖𝗛𝗜𝗘𝗩𝗘 𝗧𝗢𝗚𝗘𝗧𝗛𝗘𝗥!" Become part of our vibrant community of learners and innovators. 𝗩𝗶𝘀𝗶𝘁 : https://www.futuretechskills.in 𝗘𝗻𝗿𝗼𝗹𝗹 𝗧𝗼𝗱𝗮𝘆 & 𝗧𝗮𝗸𝗲 𝘁𝗵𝗲 𝗙𝗶𝗿𝘀𝘁 𝗦𝘁𝗲𝗽 𝗧𝗼𝘄𝗮𝗿𝗱 𝗬𝗼𝘂𝗿 𝗙𝘂𝘁𝘂𝗿𝗲!

Website
www.futuretechskills.in
Industry
Technology, Information and Internet
Company size
51-200 employees
Headquarters
C-25, Sector- 58, Noida, Uttar Pradesh
Type
Educational
Founded
2015
Specialties
Market Leading Testing Technologies Trainings, IT Professional Courses , Placement Assistance, and To Get 3X Growth and Opportunity

Locations

Employees at Future Tech Skills

Updates

  • 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗵𝗼𝘁𝘁𝗲𝘀𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗼𝗳 𝟮𝟬𝟮𝟱 Here are top capabilities of Python 𝟭. 𝗪𝗲𝗯 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 – Develop dynamic websites, backend systems, and APIs using frameworks like Django and Flask. 𝟮. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 – Build desktop applications, automation scripts, and enterprise-grade software solutions. 𝟯. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Process large datasets, clean and manipulate data, and generate meaningful insights. 𝟰. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 – Automate repetitive tasks like file handling, web scraping, and system administration. 𝟱. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – Create interactive charts, graphs, and dashboards with libraries like Matplotlib and Seaborn. 𝟲. 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 – Train AI models for tasks like image recognition, chatbots, and language processing. 𝟳. 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 – Rapidly develop and test ideas before full-scale development. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later Join our training programs for getting realtime trainings on live-projects. 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐇𝐞𝐫𝐞 - https://lnkd.in/g9FM_v4N #pythonForEveryone #Learning #upskill #InterviewPreparation #Interview #growth

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  • Selenium Automation Testing Framework Here are the key components : 1. Continuous Integration (CI) Servers: Utilize Jenkins, TeamCity, Bamboo, GitHub, Chef, and CruiseControl for building, running, and initiating tests, ensuring seamless integration and continuous testing. 2. Selenium Automation Framework: - Config File: Centralized configuration management. - Page Objects: Simplify test scripts by modeling UI components. - Utility Libraries: Reusable code libraries for common tasks. - Application Specific Libraries: Custom libraries tailored to specific application needs. 3. Automated Test Suite: Incorporate various scripts and test data, covering business functions, generic functions, and extended functions for comprehensive testing. 4. Input Test Data and Object Repository: Manage test data and object repositories efficiently, ensuring accurate and reusable test artifacts. 5. Application Under Test (AUT): Execute tests across multiple browsers (Chrome, Firefox, IE, Opera) with parallel test execution capabilities. 6. Test Management: Utilize tools like Jira, HP, QTest, QAComplete, TestLink, and Confluence to manage test suites, test cases/user stories, and test execution processes. Follow Future Tech Skills for more such information and don’t forget to save this post for later Join our training programs for getting realtime trainings on live-projects. 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐇𝐞𝐫𝐞 - https://lnkd.in/g9FM_v4N #selenium #automation #testing #InterviewPreparation #Interview #growth

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  • 𝟵 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗣𝗜 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝟭. 𝗦𝗺𝗼𝗸𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Quickly verifies if the API is operational and can handle basic requests without breaking or failing. 𝟮. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Ensures that the API’s functionality matches the specified requirements by comparing input and expected results. 𝟯. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Checks the smooth communication and functionality between different systems or modules via the API. 𝟰. 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Confirms that updates or changes to the API do not disrupt or break existing functionality. 𝟱. 𝗙𝘂𝘇𝘇 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Sends unexpected or invalid inputs to identify vulnerabilities and ensure the API handles errors gracefully. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later Join our training programs for getting realtime trainings on live-projects. 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐇𝐞𝐫𝐞 - https://lnkd.in/g9FM_v4N #apiautomation #api #restassured #PerformanceOptimization #LoadTesting #Scalability #performancetesting #apitesting #testing #qa #interviewpreparation #growth #interivew

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  • ETL Process for Data Analytics: Turning Data into Insights 1. 𝐄𝐱𝐭𝐫𝐚𝐜𝐭: Retrieve and clean raw data from multiple sources, preparing it for further processing and analysis. 2. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦: Standardize, cleanse, and enhance data, ensuring accuracy, consistency, and readiness for analysis. 3. 𝐋𝐨𝐚𝐝: Load transformed data into target systems for reporting, analytics, and decision-making, ensuring data is actionable. This concise ETL process is essential for driving effective data analytics and generating meaningful insights. [Explore More In The Post] Follow - Future Tech Skills for more such information and don't forget to save this post for later Join our training programs for getting realtime trainings on live-projects. Register Here - https://lnkd.in/g9FM_v4N #etl #dataanalysis #interviewpreparation #growth #interivew

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  • 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞! Understanding databases is crucial for any tech professional: ✔ 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 - Structuring data using entities, attributes, and relationships for efficient storage and retrieval. ✔ 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 - Organizing data to reduce redundancy and improve consistency through structured database design. ✔ 𝐒𝐐𝐋 𝐯𝐬 𝐍𝐨𝐒𝐐𝐋 𝐒𝐐𝐋 - Uses structured tables, while NoSQL supports flexible, scalable, schema-less data storage. ✔ 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 - Speed up queries by creating optimized search paths for faster data retrieval. ✔ 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 - Ensure data integrity with ACID (Atomicity, Consistency, Isolation, Durability) principles in databases. ✔ 𝐉𝐨𝐢𝐧𝐬 - Combine data from multiple tables to retrieve meaningful relationships between datasets. ✔ 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠 - Distributes large databases across multiple servers to improve performance and scalability. ✔ 𝐁𝐚𝐜𝐤𝐮𝐩 & 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 - Protects data from loss by ensuring periodic backups and disaster recovery mechanisms. ✔ 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 - Hosted databases offering scalability, security, and availability without on-premises infrastructure management. ✔ 𝐄𝐓𝐋 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝) - Moves and transforms data efficiently from multiple sources into a database. 💡 Whether you're a developer, analyst, or tester-mastering database opens doors to new opportunities! Join our training programs for getting realtime training on live-projects. Register Here - https://lnkd.in/g9FM_v4N Follow - Future Tech Skills for more such valuable posts. Don't forget to save this post for later use. #Database #SQL #DataManagement #CareerGrowth #TechSkills

  • 𝐌𝐚𝐬𝐭𝐞𝐫 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐁𝐚𝐬𝐢𝐜𝐬 Covers Big Data concepts, architecture, and challenges in testing large-scale data environments. 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Includes validation, ETL, migration, performance, security, and regression testing for data consistency and reliability. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 & 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞 Explores Hadoop, Spark, Kafka, and NoSQL databases for data transformation and processing. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Ensures data integrity from multiple sources, schema validation, and handling missing data. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Focuses on aggregation, partitioning, pipeline validation, and business rule enforcement. [Explore More In The Post] Follow Future Tech Skills for more such information and don't forget to save this post for later #BigDataTesting #BigData #Testing #InterviewPreparation #Interview #Growth

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  • 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴? It ensures data accuracy, consistency, and reliability in a data warehouse by validating ETL processes and business intelligence insights. 𝗪𝗵𝘆 𝗶𝘀 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁? It prevents incorrect reports, optimizes performance, detects anomalies, and ensures regulatory compliance for data security. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Data Validation Testing: Verifies data types, formats, and consistency between sources and targets. - ETL Testing: Ensures correct extraction, transformation, and loading of data. - Data Integrity Testing: Confirms referential integrity and validates key constraints. - Data Quality Testing: Identifies missing, duplicate, or incorrect records. - Performance Testing: Evaluates query execution speed and system scalability. - Regression Testing: Ensures updates do not break existing functionality. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later #Datawarehousetesting #etltesting #InterviewPreparation #Interview #growth

  • 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 𝘃𝘀 𝗥𝗘𝗦𝗧: A Quick Comparison 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 GraphQL uses a single endpoint and allows clients to request specific data, reducing over-fetching. It provides flexibility by returning only the necessary information. 𝗥𝗘𝗦𝗧 REST relies on multiple endpoints for different resources and returns data in a fixed format, which may include unnecessary information. It follows standard HTTP methods like GET, POST, PUT, and DELETE. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 - GraphQL enables precise data fetching with a single request, while REST often requires multiple calls. - REST follows a predefined structure, whereas GraphQL gives clients control over data selection. - GraphQL is efficient for complex queries, while REST is widely adopted with simpler implementation. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later

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  • 𝗢𝗔𝘂𝘁𝗵 & 𝗧𝗼𝗸𝗲𝗻-𝗕𝗮𝘀𝗲𝗱 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗢𝗔𝘂𝘁𝗵 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻? OAuth is a framework for secure access without exposing credentials, commonly used for Single Sign-On (SSO) and API security. Instead of passwords, OAuth issues tokens for controlled access. 𝗢𝗔𝘂𝘁𝗵 𝟮.𝟬 𝗙𝗹𝗼𝘄 OAuth follows a four-step process: user authorization, token issuance, API request, and token validation, ensuring secure access without revealing passwords. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗢𝗔𝘂𝘁𝗵 𝗧𝗼𝗸𝗲𝗻𝘀 OAuth includes access tokens for API requests, refresh tokens for renewing access, and ID tokens for authentication. 𝗢𝗔𝘂𝘁𝗵 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗥𝗶𝘀𝗸𝘀 Common vulnerabilities include token leakage, weak encryption, replay attacks, improper scope restrictions, and lack of token revocation. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later

  • 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: Key Differences and Use Cases 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 A database handles transactional operations in real-time, while a data warehouse is designed for analytics, reporting, and historical data storage. 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 Databases operate with low latency for fast transactions, whereas data warehouses process large datasets with higher latency. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Databases store structured data in relational tables, while data warehouses support structured, semi-structured, and unstructured data for analytics. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Databases scale vertically by adding resources, while data warehouses scale horizontally across multiple nodes for large data processing. 𝗗𝗮𝘁𝗮 𝗩𝗼𝗹𝘂𝗺𝗲 & 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 Databases store real-time transactional data, while data warehouses manage large historical datasets optimized for analytics. [Explore More In The Post] Follow Future Tech Skills for more such information and don’t forget to save this post for later #datawarehouse #database #testing #etltesting #bigdatatesting #InterviewPreparation #Interview #growth

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