Tips for Orchestrating Cloud Migrations

Before you move a single SAP system, you need to answer 5 questions. Miss even one and your migration might fail before it starts. Most teams skip this part. They jump straight into provisioning cloud resources, copying environments, and trying to meet a go-live deadline. But that’s like building a train schedule without knowing how many trains you’ve got, or where they’re going. Back when I consulted for large SAP migrations - from Colgate to Fortune 100 manufacturers - we never started with tooling. We started with assessment. Because without a clear understanding of what you’re moving, how it’s connected, and what it impacts - you're flying blind. These are the 5 things I always map before touching a single system: 1. System inventory — what exists, and what’s connected You’d be surprised how many environments have orphaned or undocumented dependencies. Miss one? That’s your failure point. 2. Business criticality — what can’t go down, even for a minute Not all systems are equal. Some run background jobs. Others run revenue. You migrate those differently. 3. Resource constraints — who’s available, when, and for how long Most IT teams are already overloaded. You need to know what talent you have before committing to timelines. 4. Downtime thresholds — what’s the business actually willing to tolerate? I’ve seen 80-hour migration estimates get crammed into 24-hour windows. You don’t negotiate after you start. You plan ahead. 5. Migration sequencing — what moves first, and what moves in parallel Dependencies aren’t just technical — they’re operational. Order matters. Or everything stalls. Assessment isn’t overhead. It’s insurance. And the cost of skipping it? Blown deadlines. Missed shipments. Angry execs. And a team stuck in recovery mode for weeks. Every successful migration I’ve ever led had this phase built in from the start. And every failed one I’ve seen? Didn’t.

🚨 𝗡𝗘𝗪 𝗔𝗥𝗧𝗜𝗖𝗟𝗘 𝗔𝗟𝗘𝗥𝗧: 𝗛𝗼𝘄 𝗪𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗱 𝟰𝟬+ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗥𝗶𝘀𝗸𝘀 𝗗𝘂𝗿𝗶𝗻𝗴 𝗮 𝗖𝗹𝗼𝘂𝗱 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 (And why planning for failure saved the entire project.) Have you ever led a project where a single outage could bring everything to a halt? Where shipping, invoicing, and customer portals were all riding on fragile legacy systems? This edition of 𝗧𝗵𝗲 𝗣𝗠 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 breaks down how we migrated core systems to the cloud without causing chaos. With 600 employees and a live production environment, we didn’t have the luxury of “figuring it out later.” 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗲𝗿𝗲 𝘂𝗽 𝗮𝗴𝗮𝗶𝗻𝘀𝘁: ➝ A 90-day timeline with zero margin for error ➝ Legacy systems with undocumented dependencies ➝ Vendors, data risks, and real-time operations under pressure 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘄𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸: ✅ Created a living risk register with 40+ tracked scenarios ✅ Simulated outages with a Red Team before go-live ✅ Designed rollback paths for every migration step 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻: → How to make risk planning the core of your migration strategy → Why real-time simulations beat assumptions every time → How to coordinate vendors around failure planning → How to deliver under pressure without losing control 𝗪𝗲’𝗿𝗲 𝗮𝗹𝘀𝗼 𝗶𝗻𝗰𝗹𝘂𝗱𝗶𝗻𝗴: 🧠 The risk categories you need to track during cloud migrations 📊 How we resolved live issues in under 2 hours 🚀 Lessons you can apply to any system transition under pressure If you’ve ever lost sleep over infrastructure risks, this one’s for you. 👉 READ THE FULL ARTICLE NOW and drop a comment: What’s the smartest move you’ve made to manage infrastructure risk? 2 Disgruntled PMs Podcast

Migrating databases to cloud-native solutions is a significant step toward achieving better scalability, reliability, and cost efficiency. Recently, I completed a migration project where I moved a MariaDB database to an Amazon Aurora MySQL-Compatible database using AWS Database Migration Service (DMS). Here’s a simplified and educational breakdown of the process. 💡 Key Steps in the Migration Process 1️⃣ Setting Up the Target Database • Created an Amazon Aurora MySQL-Compatible database cluster using Amazon RDS. • Selected appropriate configurations for instance type, storage, and VPC security settings. • Ensured the database was private and accessible only within the designated network. 2️⃣ Preparing for the Migration • Configured a replication subnet group to define where the DMS replication instance would operate. • Launched a DMS replication instance to handle the migration tasks efficiently. 3️⃣ Creating Source and Target Endpoints • Source Endpoint: Configured to connect to the MariaDB database in the source environment. • Target Endpoint: Configured to connect to the Amazon Aurora database. • Verified connectivity for both endpoints before initiating the migration. 4️⃣ Running the Migration Task • Created a migration task in AWS DMS to replicate data from the source to the target database. • Configured table mappings to specify which schemas and tables to migrate. • Monitored the task for progress and completion. 5️⃣ Updating DNS Records • Updated DNS entries to redirect application traffic to the new Aurora database. • Verified the changes to ensure the application connected seamlessly to the target database. 6️⃣ Finalizing the Migration • Confirmed data consistency and application functionality with the new database. • Gracefully shut down the source database to complete the migration process. ⚙️ Tools and AWS Services Used • Amazon Aurora: A managed MySQL-compatible database offering high performance and reliability. • AWS Database Migration Service (DMS): Simplifies data migration with minimal downtime. • Amazon RDS: Automates database operations such as backups and patching. • Amazon VPC: Provides secure connectivity during migration. 🏗️ Best Practices for Database Migration • Plan the Migration: Define the source and target configurations, security, and access requirements. • Minimize Downtime: Use continuous data replication to ensure smooth transition with minimal application disruption. • Secure Connectivity: Use VPC and security groups to maintain strict access controls. • Test Thoroughly: Validate database functionality and application integration post-migration. I would love to mention some amazing individuals who have inspired me and who I learn from and collaborate with: Neal K. Davis Steven Moran Eric Huerta Prasad Rao Azeez Salu Mike Hammond Teegan A. Bartos Kumail Rizvi Benjamin Muschko #AWS #DatabaseMigration #CloudComputing #AmazonAurora #CloudArchitecture #DMS #RDS

Key management: a make-or-break factor in cloud migrations. Migrating data to the cloud is no small feat. While many organizations focus on moving the data, they often underestimate the complexity of encryption and key management. This oversight can leave sensitive data exposed to breaches and compliance failures. Recent research from the Cloud Security Alliance and lead authors Sunil Arora, Santosh Bompally, Rajat Dubey, Yuvaraj Madheswaran, and Michael Roza found that if you want to fortify your migration process, you need to take some key steps to manage encryption keys effectively during cloud migration. 1️⃣ Inventory Your Keys: Document all encryption keys, including their purpose, algorithm, and expiration dates. This ensures nothing slips through the cracks. 2️⃣ Plan Key Transfer Securely: Use customer-managed keys (CMKs) or BYOK (Bring Your Own Key) solutions to maintain control over encryption. 3️⃣ Encrypt Before Transfer: Ensure data is encrypted in transit and at rest. Secure connections (like AWS Direct Connect or Azure ExpressRoute) can minimize exposure risks. 4️⃣ Rotate Keys Regularly: Set automated key rotation policies to limit potential exposure in case of compromise. 5️⃣ Implement Least Privilege Access: Restrict access to encryption keys, enforce role-based permissions, and use monitoring tools to detect misuse. 6️⃣ Validate with Testing: Test key integration with cloud services before migration using unit, integration, and end-to-end testing to avoid surprises post-migration. Cloud migration isn’t just about moving data—it’s about moving securely. #CloudSecurity #Encryption #CloudMigration #CyberResilience #DataProtection Bedrock Security

If you're about to migrate a database: 1. Inventory everything. 2. Sequence by latency sensitivity. 3. Build a rollback plan and assume you'll need it These 3 rules saved us after 3 failed moves. Let me tell you why each one matters. When I was consulting for HP Enterprise, we were tasked with consolidating massive data centers. Not cloud to cloud migrations. This was moving entire enterprise accounts from old EDS data centers to HP's new facilities across the United States. The stakes were enormous. Every move required shutting down production systems, copying terabytes of data, and hoping everything worked when we flipped the switch back on. Rule 1: Inventory everything We learned this the hard way. You can't just look at the obvious stuff like databases and applications. You need to map every single connection, every dependency, every integration that touches your data. That random reporting tool someone built 3 years ago? It's going to break your migration if you don't account for it. Rule 2: Sequence by latency sensitivity Not all data is created equal. Some systems can tolerate a few milliseconds of delay. Others will fail catastrophically if there's any lag between the application and database. We'd spend weeks just figuring out what could move first, what had to wait, and what couldn't be separated at all. Rule 3: Build a rollback plan and assume you'll need it This is where most people get cocky. They think their plan is perfect and skip the "what if this goes wrong" scenario. We had full backup strategies for every single migration. Good thing, because we needed them more often than we'd like to admit. Long maintenance windows would start. We'd shut down systems, start copying data, begin the cutover process. Then something would go sideways. The database didn't sync properly. An application couldn't connect. Performance was terrible in the new environment. When that happened, we'd roll everything back and try again later. Those painful experiences taught me there had to be a better way. That's actually what led me to start Verge Technologies. The idea that you could move stateful systems like databases without massive downtime, without all the planning overhead, and without the constant fear of failure. But even with better technology, those three rules still apply. Inventory everything. Sequence by latency. Plan for rollback. Because no matter how good your tools are, migrations are inherently risky. The difference between success and disaster often comes down to preparation.

I've been part of 7 significant data migrations throughout my career. I'll teach you the key things to be mindful of in 10 minutes: 1. Data migration > Copying data over to the new system A few factors to consider: * Do you need to move historical data? * Are the data types similar between the new and old systems? * Do you have DDLs defined in your code base? 2. Redirecting input sources > Your new system needs to be able to access the necessary inputs A few factors to consider: * Are the input data sources the same? * Do the input sources in the new system have similar or better SLAs? * Are the input sources of the same quality and schema? 3. Moving code > Does your old code work with the new system If you are moving from a primarily SQL-based code base to a dataframe, you'd need lots of new code. A few factors to consider: * How different are the new and old systems in terms of code interface (e.g., pure SQL v Python)? * Does the new system have all (& ideally more) features than the old one? * Does the scale of the new system satisfy your data SLAs? * The better your code tests, the simpler this step 4. Tools > Your systems probably have non-pipeline tools (e.g., GitHub actions, etc), ensure that they work with the new system A few factors to consider: * Do the tools (e.g., dbt elementary -> Spark?) of the old system work in the new one or have better replacements? * If your new system has "another" tool to do similar things, ensure it can! * If your system interacts with external company-wide tools (e.g., GitHub actions), ensure good integration with the new system 5. Validation period > Run the new and old systems for a switch-over period before switching over users to the new systems A few factors to consider: * Keep the old and new systems running for a switch-over period. * Run frequent (ideally scheduled) validation checks between new and old systems during this period. * After enabling end-user access to the new system, keep the old system on in case of rollbacks 6. Permission patterns > Do the end users have the same permissions as the old system A few factors to consider: * Do your current stakeholders have the same access(read-write-create-delete) in the new system? * If you are changing permissions, ensure you provide the end users sufficient time to adapt. 7. Interface layer for end-users > Will the end users be able to access data with the same data asset name and schemas? A few factors to consider: * Does the new systems require the end user to change any of their code/queries? * If you have used an interface layer (usually a view), this should be simple * Will the new data system have the same or better SLAs? 8. Observability systems > Will your new system's observability system work similarly? What other migration tips do you have? Let me know in the comments below. - Enjoy this? ♻️ Repost it to your network and follow me for more actionable data content. #data #dataengineering

7 Cloud Migration Strategies Every Cloud Engineer Should Know (with scenario questions for interviews) Cloud migration can originate from on-premises infrastructure or from another cloud provider. And it goes beyond just moving data. It's about strategically deciding the best approach for each application and workload. The goal is to optimize performance, cost, and long-term viability in the cloud. Here’s a simple breakdown of the key strategies you should focus on: 1/ Retain (Revisit later) ↳ Keep workloads on-prem if they aren’t cloud-ready or are still needed locally. Scenario : You have a critical legacy application with custom hardware dependencies. How would you initially approach its cloud migration? 2/ Retire (Decommission) ↳ Eliminate outdated or unused parts to reduce cost and simplify the system. Scenario : During an assessment, you identify an old reporting tool used by only a few employees once a month. What's your recommendation? 3/ Repurchase (Drop & Shop) ↳ Replace legacy apps with SaaS alternatives, a fast and cost-effective solution. Scenario : Your company's on-premise CRM system (example) is outdated and costly to maintain. What quick cloud solution might you consider? 4/ Rehost (Lift & Shift) ↳ Move your application to the cloud as-is, with no code changes needed. Scenario : A non-critical internal application needs to move to the cloud quickly with minimal disruption. What strategy would you prioritize? 5/ Replatform (Lift, Tinker & Shift) ↳ Make light optimizations before migration, for better performance with minimal effort. Scenario : You're migrating a web application, and a small change to its database will significantly improve cloud performance. What strategy does this align with? 6/ Relocate (Many Providers) ↳ Change the hosting provider without modifying the app, a quick and simple approach. Scenario : Your current cloud provider is increasing prices significantly for a specific set of VMs. How might you address this without rewriting applications? 7/ Refactor (Re-architect) ↳ Redesign your application for cloud-native capabilities, making it scalable and future-ready. Scenario : A monolithic, highly scalable customer-facing application is experiencing performance bottlenecks on-prem. What long-term cloud strategy would you propose?. Beyond these strategies themselves, successful cloud migration also focuses on: - thorough assessment, - understanding dependencies, - meticulous planning, - and continuous optimization Just remember: successful migration isn't just about the tools, but the approach. Very important to understands the "why" behind each strategy — not just the "how." Dropping a newsletter this Thursday with detailed scenario based questions (and example answers) for each of these patterns — subscribe now to get it -> https://lnkd.in/dBNJPv9U • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well

I've ensured 100+ AWS migration projects succeed. Found key reasons why migrations could fail. (This is how we solved it, and you can too) 1. Ever-changing migration plans Constantly changing your migration plan, like 'Lift and Shift', 'Re-platforming', 'Re-hosting' etc., is a red flag. This inconsistency can lead to unforeseen dependencies and legacy system issues. To mitigate this, conduct thorough application dependency mapping and discovery before planning migration phases. 2. Inconsistent migration methods In a multi-tier web application migration project, using different methods like 'Re-hosting', 'Re-platforming', and 'Refactoring' for different applications will prove inefficient. It can lead you to integration issues and performance bottlenecks. Avoid it by proper standardization, defining clear target architectures, and grouping similar applications together. 3. Ineffective escalation process In a large data warehouse migration project, you can face issues with data consistency and integrity. These technical issues need to be promptly escalated to the right team for quick resolution. As a solution, establish a strict governance structure and communication plan to ensure blockers reach the right teams promptly. 4. Late emerging migration issues While doing CRM system migration, unforeseen data migration complexities can surface late, causing delays and significant rework. To address this, implement mechanisms like early design processes, tools, and escalation paths to identify issues sooner and maintain project momentum. 5. Lack of stakeholder alignment This can usually be faced while undergoing an ERP system migration. Stakeholder buy-in can prove to be critical. Without alignment, miscommunication between the migration team and business stakeholders can lead to roadblocks. Ensure alignment early by highlighting how AWS benefits specific objectives, fostering strong support throughout the migration process. Just remember that the future is unpredictable. But if planned well, then things are manageable! In the same way, Murat Yanar, Director at Amazon Web Services (AWS), once said, “You may not be able to predict the future needs of your business precisely. But the AWS cloud provides services to meet these ever-changing demands and help you innovate flexibly and securely.” Curious to know: What’s your biggest challenge when it comes to AWS migration? #aws #database #scalability #softwareengineering #simform

Unlocking Data Potential: Seamless Migration to Snowflake #Data Warehouse In today's data-centric landscape, businesses rely on robust data management solutions to drive insights and innovation. One of the most transformative steps an organization can take is migrating its data from various source systems to the Snowflake #Data Warehouse. Here’s why and how your business can benefit from this migration. Why Choose Snowflake? 1. Unmatched Scalability: Snowflake's unique architecture allows for independent scaling of storage and compute resources. This ensures your data warehouse can handle growing data volumes and complex queries efficiently. 2. Cost-Effective: With Snowflake’s pay-as-you-go model, you only pay for the resources you use. This flexible pricing, combined with data compression techniques, significantly reduces storage costs. 3. Seamless Integration: Snowflake easily integrates with various data sources, including cloud storage, on-premises databases, and third-party applications. This flexibility ensures all your data is consolidated into a single, unified platform. 4. Enhanced Security: Snowflake prioritizes data security with features like end-to-end encryption, network isolation, and compliance with industry standards, ensuring your data remains protected. #Steps to a Successful Data Migration 1. Assess Your Data: Start by evaluating your current data landscape. Identify the types and sources of data you have and any potential challenges, such as data quality issues. 2. Develop a #Migration Plan: Create a detailed plan outlining each step of the migration process, including timelines and required resources. A phased approach can minimize disruption to your business operations. 3. Use #ETL Tools: Leverage Extract, Transform, and Load (ETL) tools like #Talend, #Informatica, #Airflow, and #Apache NiFi to streamline the data migration process. These tools help extract data from source systems, transform it into the desired format, and load it into Snowflake. 4. Validate and Optimize: After migration, validate the data to ensure accuracy and completeness. Optimize your Snowflake setup by organizing data into schemas, setting up performance enhancements, and configuring security measures. 5. Train Your Team: Provide comprehensive training for your team to ensure they can effectively use Snowflake. Set up support mechanisms to address any post-migration issues. Conclusion Migrating to Snowflake is a strategic move that offers significant benefits in performance, cost efficiency, and security. By following a structured approach and leveraging Snowflake's capabilities, you can unlock the full potential of your data. Are you ready to transform your data management with Snowflake? Let's talk about how we can make your migration journey smooth and successful. #DataMigration #Snowflake #DataWarehouse #CloudComputing #BigData #DataAnalytics #BusinessIntelligence #AWS #Azure #GCP

The secret to data migrations that ship on time? Leave your ugly data exactly as it is. Yes. I’m saying lift-and-shift > rearchitect as you migrate. ‘But Gleb,’ you say, ‘That means I’m consciously saddling myself with mountains of technical debt.’ I know. And it makes me uncomfortable, too. So uncomfortable, in fact, that I did not follow this strategy when I last had to make the choice. And I paid for it with a three-year migration that nearly cost me my career. I get that urge to rearchitect as you migrate. You can deal with your technical debt and start fresh on a new warehouse. Finally, you can fix ugly code, optimize queries, and build [your favorite data modeling paradigm]. And if not now, when?! But please, as well-meaning as it is, resist the temptation to rearchitect. And if you do, you will save time and create goodwill. Here’s why: 👉 There’s no consensus to drive. -> Team doesn’t need to make decisions to start the project. Just start translating the legacy code. -> There are no new definitions, models, or metrics to socialize with stakeholders. Everything stays the same. -> Progress is evident. You’re not convincing your CEO about the project status in esoteric terms. 97% of rows match between tableA-legacy and tableA-new. Great! We know we’re 97% there for that table. 196/200 tables are fully matching. Great! Our migration is 98% complete. 👉 You don’t actually need to do this yourself. -> Because the definition of success is so simple and clear, you can outsource most of the migration work. Not much business context is needed if you optimize for parity. -> For the same reason, code translation and validation can be automated and completed in days instead of years. What then, should we do about all of the technical debt you’re taking with you? All of that terrible, inefficient code full of ancient patterns, inconsistent naming, and the data model that makes you want to cry? Tackle it next. It’s okay. You can always take care of that once the migration is over. Wouldn’t you rather take on those improvements when you’re on a new platform with a better developer experience, when your execs aren’t breathing down your neck, and you’re not working against the clock?