Manish Agarwal

Business Central Tip: To use Auth 2.0 for SMTP authentication, consider the following ・All users must be on the same Microsoft Entra tenant. ・You can't use Specific User for the Sender type, as you can with Basic authentication. The reason is that OAUTH 2.0 uses the credentials of the current (signed-in) user. And, if you can't set up OAuth 2.0 authentication, Microsoft encourages you to explore third-party alternatives if you want to use SMTP email in earlier versions. More details: https://lnkd.in/gHkd4jQ7 #Dynamics365 #Dynamics #MSDyn365 #MicrosoftDYN365 #MSDyn365BC #businesscentral

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Data Migration Testing Visualised ! Moving data from a legacy system to a new target application is not just about transfer, it’s about accuracy, consistency, and reliability. Data Migration Testing ensures that business-critical information is moved without corruption, loss, or integrity issues. Here’s how the process flows step by step: Migration Process The journey starts with a structured roadmap: Plan, Design, Test, and Migrate to ensure smooth rollout. Source System - Plan & Analyze: Product and process analysis, data quality checks, and volume/history review. - Design & Develop: Create extraction modules and set up databases. - Test & Iterate: Execute test plans, extract, and validate data. Transformation Layer - Plan & Analyze: Define cleansing and transformation requirements. - Design & Develop: Build cleansing modules and transformation logic. - Test & Iterate: Load and test on staging DB, apply transformations, execute scripts. Target System - Plan & Analyze: Gap analysis and product understanding. - Design & Develop: Create target schemas, tables, and configurations. - Test & Iterate: Validate migrated data, business testing, and approval for go-live. Migration Factory (Central Process) 1. Data Analysis – Understand data and define scope. 2. Data Extract – Pull from source system. 3. Cleanse & Transform – Apply rules and fix inconsistencies. 4. Test & Validate – Check accuracy and completeness. 5. Production Roll-out – Deploy migration with confidence. Successful data migration is not just about moving data, it’s about delivering trust in business-critical systems.

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Dear Database Administrators, Welcome to Module 10 of our Oracle Database Training Series: Data Dictionary Fundamentals. The Oracle Data Dictionary is the heart of database metadata management — a comprehensive set of views and tables that store critical information about every object, user, and process in the system.

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🚀 𝗟𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 - 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗧𝗼𝗼𝗹 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 I am excited to share a new PowerShell utility designed to assist Power BI teams in addressing a common challenge: comprehensive dataset documentation and metadata management across multiple workspaces. 🔍 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Power BI environments grow quickly, making it increasingly difficult to keep track of datasets. Teams often face issues such as: • 🔗 Understanding data lineage across workspaces  • 📋 Documenting tables, columns, and measures for governance • 🔄 Planning migrations or impact analysis  • ✅ Meeting compliance requirements for data inventory ⚡ 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Power BI Extract Metadata is a PowerShell tool that connects to Power BI workspaces via Service Principal and extracts comprehensive metadata, including: • 📊 Complete dataset inventory: Tables, columns, measures, partitions  • 🗺️ Data source mapping: Connection strings, databases, schemas • 📝 Power Query code: M expressions where available  • 🔄 Cross-workspace analysis: Single CSV output across all accessible workspaces  • 🔒 Secure authentication: Service Principal with proper permission management 🛠️ 𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲:  • ⚡ Handles Premium and Premium Per User workspaces with XMLA endpoint  • 🛡️ Graceful error handling for capacity issues (CapacityNotActive scenarios)  • ⏰ Timestamped CSV outputs for audit trails  • 📈 Clean CSV format for further analysis in Excel/Power BI  • 📚 Comprehensive documentation and examples 🤝 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 & 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆-𝗥𝗲𝗮𝗱𝘆: This is a complete package with: • 📖 Detailed README with setup instructions  • 👥 CONTRIBUTING guidelines for developers  • 🤲 Code of Conduct for healthy collaboration  • ⚖️ MIT license for maximum flexibility  • 🎯 Pull request templates and issue workflows 🎯 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗙𝗼𝗿:  • 👨‍💻 Data engineers managing multiple Power BI environments  • 🏛️ Governance teams needing comprehensive dataset documentation  • 💼 Consultants performing Power BI assessments  • 🏢 Organizations planning cloud migrations  • 🤔 Anyone who needs to understand "what's in our Power BI?" 🚀 𝗚𝗲𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱 𝗧𝗼𝗱𝗮𝘆: https://lnkd.in/gnJNjXM7 The repository includes everything needed: masked config template, requirements documentation, and step-by-step setup instructions. Community contributions are very welcome! If this solves a problem you've been facing, please give it a ⭐ and share your feedback! #taik18 #PowerBI #DataGovernance #BusinessIntelligence #OpenSource #Azure #DataEngineering #PowerShell #DataManagement #Analytics #taik18

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𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗙𝗶𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗶𝗻 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 Azure Data Factory (ADF) is a powerful tool that supports a variety of file formats, making it an essential choice for data integration and transformation. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁 𝗶𝗻 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 - 🔹𝐀𝐯𝐫𝐨 🔹𝐃𝐞𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐓𝐞𝐱𝐭 🔹𝐉𝐒𝐎𝐍 🔹𝐏𝐚𝐫𝐪𝐮𝐞𝐭 🔹𝐗𝐌𝐋 🔹𝐎𝐑𝐂 🔹𝐁𝐢𝐧𝐚𝐫𝐲 🔹𝐄𝐱𝐜𝐞𝐥 🔹𝐈𝐜𝐞𝐛𝐞𝐫𝐠 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 - ✔️ ADF allows you to work with multiple file formats, catering to diverse data sources and requirements. ✔️ Handle large volumes of data efficiently, ensuring smooth data processing and transformation. ✔️ Optimize storage and processing costs by choosing the right file format for your data needs. If you’ve explored how Azure Data Factory supports multiple file formats — Avro, Parquet, JSON, ORC, Excel, and more — then you’ve already taken the first step toward becoming a true Data Integration Expert. But understanding formats is just the beginning… To truly master ADF, you need to dive deep into real-world use cases, data pipelines, transformations, and automation techniques used by top data engineers every day. 📘 𝗜𝗻 𝗺𝘆 “𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝟯𝟲+ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀” 𝗰𝗼𝘂𝗿𝘀𝗲, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻 - ✔ How to build and optimize end-to-end ADF pipelines ✔ Real-world integration of multiple file formats (Parquet, JSON, Avro, etc.) ✔ Data flow transformations, triggers, and debugging techniques ✔ Best practices to handle big data efficiently in production environments This course is perfect for Data Engineers, Cloud Developers, and Learners who want to elevate their ADF skills from basic to expert level. 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘁𝗵𝗶𝘀, 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗙𝗶𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 𝗶𝗻 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 𝗣𝗗𝗙 𝘄𝗶𝗹𝗹 𝗴𝗶𝘃𝗲 𝘆𝗼𝘂 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 — 𝗯𝘂𝘁 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗮𝘀𝘁𝗲𝗿𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗮𝗽𝗽𝗹𝘆 𝘁𝗵𝗼𝘀𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗹𝗶𝘃𝗲, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀. 💰 💰 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗢𝗳𝗳𝗲𝗿 — 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗧𝗶𝗺𝗲 𝗢𝗻𝗹𝘆! Enroll today and get 20% OFF on “Master Azure Data Factory with 36+ Real-Time Scenarios” using code AzureDataFactory20 100+ learners have already gained confidence in ADF pipelines, triggers, and error handling. 🎁 Bonus - Lifetime Access + Future Updates 👇 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄 & 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝘇𝘂𝗿𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 https://lnkd.in/dc8yuZbN 👉 Like to show your support. 💬 Comment with your thoughts or questions. 🔁 Repost to share with your network. 👥 Follow Praveen Patel to get such more like this. #AzureDataFactory #DataEngineering #MicrosoftFabric #Azure #DataIntegration #DataPipeline #ADF #ETL #CloudEngineering

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Oracle 19c-Step by Step Manual Data Guard Switchover activity. DR DRILL activity on single instance 19c by manual method. Switchover Operation Procedure:  After configuring data guard, databases would be either primary and standby database role, and we can altered these roles without loss of data or without resetting logs called Switchover. A database can be in one of two mutually exclusive modes (primary or standby). These roles can be altered at runtime without loss of data or resetting of redo logs. Step by Step method of Creating Physical Standby: https://lnkd.in/dvvcV_Gd Step-by-Step Guide to creating new Database from scratch, DR standby new method: https://lnkd.in/dHSHDM9y #DATAGUARD #PRIMARY #STANDBYDATABASE #19C #DBA #ORACLEDBA #oracle #databaseadministrator #linux #installation

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✅ How to Enable and Disable Archive Mode in Oracle Database ➤ Enable Archive Mode Make sure you take a full backup before enabling archive mode. Steps: Shutdown the database: SHUTDOWN IMMEDIATE; Start the database in mount mode: STARTUP MOUNT; Enable archive log mode: ALTER DATABASE ARCHIVELOG; Open the database: ALTER DATABASE OPEN; Verify archive mode: ARCHIVE LOG LIST; You should see output indicating that archive mode is enabled and the archive destination path. ➤ Disable Archive Mode Only disable archive mode if you are certain it's safe and recovery is not needed. Steps: Shutdown the database: SHUTDOWN IMMEDIATE; Start the database in mount mode: STARTUP MOUNT; Disable archive log mode: ALTER DATABASE NOARCHIVELOG; Open the database: ALTER DATABASE OPEN; Verify archive mode: ARCHIVE LOG LIST; You should see output indicating that archive mode is disabled. ✅ Notes: Always backup before switching modes. Archive mode is critical for data recovery in production environments. Disabling archive mode is only recommended for test or development environments where data loss is acceptable. Let me know if you want the exact commands for a specific Oracle version or OS environment!

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🚀 Top 10 Trending Oracle DBA Interview Questions & Answers to Nail Your Next Interview! 💻  Ready to land your dream Oracle DBA role? Stay ahead of the curve with these top 10 trending interview questions for 2025! At Oracle DBA Online Training, we’re here to equip you with the knowledge and confidence to succeed. Let’s dive in:  🔹 Q1: How do you optimize a slow-running query in Oracle? Answer: Analyze the execution plan with EXPLAIN PLAN, identify bottlenecks, and use indexing, partitioning, or query rewriting. Leverage SQL Tuning Advisor for recommendations.   🔹 Q2: What are the key differences between RAC and Data Guard? Answer: RAC (Real Application Clusters) provides high availability via multiple nodes sharing workload, while Data Guard ensures disaster recovery with standby databases for failover.   🔹 Q3: How do you handle database backup and recovery? Answer: Use RMAN for incremental backups, configure Fast Recovery Area (FRA), and test recovery scenarios like point-in-time recovery to minimize downtime.   🔹 Q4: What is the purpose of the UNDO tablespace? Answer: UNDO tablespace stores before-images of data for rollback, read consistency, and flashback operations. It’s managed via parameters like UNDO_RETENTION.   🔹 Q5: How do you monitor database performance? Answer: Use tools like Enterprise Manager, AWR reports, and ADDM to track metrics like CPU usage, I/O wait times, and top SQL queries for optimization.   🔹 Q6: What is a tablespace in Oracle, and how is it different from a datafile? Answer: A tablespace is a logical storage unit for database objects, while a datafile is the physical file storing the data. Multiple datafiles can belong to one tablespace.   🔹 Q7: How do you manage database security? Answer: Implement user authentication, roles, and privileges. Use Virtual Private Database (VPD), encryption, and audit trails to secure sensitive data.   🔹 Q8: What is the significance of the redo log files? Answer: Redo logs record all changes made to the database for recovery purposes. They ensure data integrity during crash or media recovery scenarios.   🔹 Q9: How do you handle a database deadlock? Answer: Detect deadlocks via trace files or Enterprise Manager, identify conflicting sessions, and resolve by terminating one session or redesigning the application logic.   🔹 Q10: What is Oracle Grid Infrastructure? Answer: It’s a software layer providing cluster management and storage services for Oracle RAC and ASM, ensuring high availability and scalability.     Join Oracle DBA Online Training to master these concepts and more! 📩 DM us or visit our page to enroll today and take the first step toward your Oracle DBA career! 💼  🔗 Register Now: https://lnkd.in/dNprq7RY or 📞 Call/WhatsApp: +91 8169158909 📩 For Queries: 📧 Email: [email protected] 📞 Call/WhatsApp: +91 8169158909 #OracleDBA #DatabaseAdministration #TechCareers #InterviewPrep #OracleTraining #CareerGrowth

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ADF Practice Questions: Scenario 1: Data Transformation Pipeline Scenario: Azurelib e-commerce collects sales data from multiple regional databases (SQL and NoSQL). The data needs to be cleaned, transformed, and stored in Azure Data Lake for analysis. The pipeline must also check the data’s integrity before loading it into the Data Lake. If data is incomplete or contains errors, it should be sent to a different storage location for reprocessing. Q1: How would you design an Azure Data Factory pipeline to handle this scenario? Explain which activities you would use and how you would manage errors. Scenario 2: Copying Data from On-premises to Azure Scenario: Azurelib retail business has a legacy on-premises SQL Server database containing product and inventory data. The company wants to migrate this data to Azure SQL Database on a daily basis for reporting and analytics. The pipeline should minimize downtime and avoid impacting the on-premises server’s performance. Q2: How would you design a pipeline in Azure Data Factory to copy this data from on-premises SQL Server to Azure SQL Database? Which ADF components would you use to ensure minimal downtime? Scenario 3: Parameterized Pipelines Scenario: You are working for an Azurelib e-commerce company that collects product reviews from different platforms daily. Each platform has a different API endpoint for data retrieval, and you need to ingest this data into Azure Data Lake. The process must be automated so that the pipeline dynamically fetches data based on the platform being processed. Q3: How would you implement a parameterized pipeline in ADF to dynamically handle multiple data sources? Which parameters and activities would you configure? Scenario 4: Data Lake Partitioning by Date Scenario: Your organization stores large amounts of transactional data in Azure Data Lake, and the data is loaded daily from various sources. To improve query performance, you are tasked with partitioning the data in Azure Data Lake based on the date of the transaction. Q4: How would you configure an ADF pipeline to partition data in Azure Data Lake by date? Which ADF activities and transformations would you use to achieve this? Scenario 5: Failure Handling and Pipeline Monitoring Scenario: You have set up a data pipeline that processes customer orders and loads them into Azure SQL Database. Sometimes, due to network issues or data corruption, the pipeline fails mid-execution. The business needs to be notified immediately if the pipeline fails, and the pipeline must be automatically retried a few times before stopping completely. Q5: How would you configure failure handling and monitoring for this pipeline? What features of ADF would you use to send alerts and implement retries in case of failures? Do share and follow Deepak Goyal for more.

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