Cloud Computing Solutions

Demystifying Cloud Strategies: Public, Private, Hybrid, and Multi-Cloud As cloud adoption accelerates, understanding the core cloud computing models is key for technology professionals. In this post, I'll explain the major approaches and examples of how organizations leverage them. ☁️ Public Cloud Services are hosted on shared infrastructure by providers like AWS, Azure, GCP. Scalable, pay-as-you-go pricing. Examples: - AWS EC2 for scalable virtual servers   - S3 for cloud object storage - Azure Cognitive Services for AI capabilities - GCP Bigtable for large-scale NoSQL databases ☁️ Private Cloud Private cloud refers to dedicated infrastructure for a single organization, enabling increased customization and control. Examples:  - On-prem VMware private cloud - Internal Openstack private architecture - Managed private platforms like Azure Stack - Banks running private clouds for security ☁️ Hybrid Cloud Hybrid combines private cloud and public cloud. Sensitive data stays on-prem while leveraging public cloud benefits. Examples: - Storage on AWS S3, rest of app on-prem - Bursting to AWS for seasonal capacity - Data lakes on Azure with internal analytics ☁️ Multi-Cloud Multi-cloud utilizes multiple public clouds to mitigate vendor lock-in risks. Examples:  - Microservices across AWS and Azure  - Backup and DR across AWS, Azure, GCP - Media encoding on GCP, web app on Azure ☁️ Hybrid Multi-Cloud The emerging model - combining private infrastructure with multiple public clouds for ultimate flexibility. Examples: - Core private, additional capabilities leveraged from multiple public clouds - Compliance data kept private, rest in AWS and Azure  - VMware private cloud extended via AWS Outposts and Azure Stack Let me know if you have any other questions!

Until yesterday, no company worth over $500B had ever gained more than 25% in a single trading day. Then came Oracle. In a move that defied both gravity and historical precedent, Oracle stock surged 40% today, adding over $300B in market cap overnight. The company now hovers just shy of the trillion-dollar mark, and Larry Ellison - armed with a 41% stake - woke up as the world’s richest man, suddenly $100 billion wealthier. Yes, Oracle. The perennial punchline of “legacy software.” The company most of us had filed away in the footnotes of tech history is suddenly the market’s cool kid. For those paying attention, this moment has been years in the making. Oracle’s pivot into cloud and AI wasn’t impulsive - it was deliberate, capital-intensive, and decidedly unsexy. They didn’t chase developer mindshare; they banked contracts. And those contracts just hit the ledger all at once. ➰ The Q1 revenue headline - $14.9B, up 12% YoY - wasn’t what lit the fuse. ➰ Even IaaS revenue at $3.3B, up 55% is strong, but not frenzy-worthy. ➰ The magic number was buried deeper: $455B in Remaining Performance Obligations (RPO), up 359% YoY. That’s nearly 8 times Oracle’s current revenue run-rate, a backlog so large it borders on the surreal. RPO isn’t a flashy number. It doesn’t trend on CNBC tickers. But in enterprise software, it’s gospel. It represents revenue already won but not yet recognized. In plain English: Oracle just told Wall Street, “We’ve already signed nearly half a trillion dollars’ worth of business. All that’s left is execution.” Oracle expects cloud infrastructure revenue which came in at $3.3B this quarter to hit $18B this fiscal year and ramp to $144B within four years. They noted that “most of the revenue in this forecast is already booked in our reported RPO”. It’s less of a forecast and more of a countdown at this point. The market isn’t just reacting to a quarter. It’s reacting to a company that rewired its DNA and is now producing receipts. In a space dominated by Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, Oracle carved out an edge not through branding or developer love, but through being the only one willing to say yes to what AI-native enterprises actually wanted: custom infrastructure, multi-cloud deployments, sovereign regions, long-term capacity, and massive scale contracts. What we witnessed today is the rarest thing in markets: a narrative inversion. Oracle went from legacy to legend not by shouting louder but by building slower, selling longer, and letting the numbers speak. The company that once stood for on-prem databases is now one of the most valuable cloud businesses in the world. TikTok and Twitter are obsessing over the ‘Great Lock-In’ without agreeing on what it means. Oracle just showed the only version that matters: half a trillion in contracts, signed and sealed. King of the Lock-In.

Every developer should know that tenant isolation is not a database problem. It’s a blast-radius problem. I learned this the hard way. One missing tenant filter. That’s all it takes to turn a normal deploy into a security incident. Every multi-tenant system eventually picks one of three isolation levels. Each one trades safety, cost, and operational pain in different ways. 1. Database per tenant This is the strongest isolation you can get. Each tenant lives in its own database. No shared tables. No shared state. The upside is obvious. A bug in one tenant cannot leak data from another. Audits are simpler. Compliance conversations are shorter. When something breaks, the blast radius stays small. The downside shows up later. Operational overhead grows fast. You manage hundreds or thousands of databases. Migrations become orchestration problems. Costs scale with tenant count, not usage. This model works when tenants are large, regulated, or high-risk. It breaks down when you try to apply it blindly to long-tail customers. 2. Schema per tenant This is the middle ground most teams underestimate. All tenants share a database, but each one gets a separate schema. Tables stay isolated, but infrastructure stays manageable. You get clearer boundaries than row-level isolation. You avoid the explosion of databases. Audits remain reasonable. Most accidental data leaks disappear. But complexity still creeps in. Migrations must run across many schemas. Cross-tenant reporting becomes awkward. Automation is not optional anymore. Without it, this model collapses under its own weight. This approach works well when tenants vary in size and you want isolation without full separation. 3. Row-level isolation This is the cheapest and most dangerous option. All tenants share the same tables. Isolation lives in a tenant_id column and your queries. Infrastructure stays simple. Costs stay low. Scaling is easy. The risk is brutal. One missing filter equals a data leak. One refactor can break isolation. One rushed hotfix can expose everything. Security depends on every layer doing the right thing every time. This model only works when you add heavy guardrails: strict query scoping, database policies, service-level enforcement, and tests that actively try to cross tenant boundaries. Without those, you’re betting the company on discipline. Tenant isolation is not a storage choice. It’s a trust decision. Learn this, it's a classic Interview question.

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

Legacy systems are holding HR leaders back. 61% of HR leaders cite outdated tech as a barrier to business goals (McKinsey). And tools like Workday and SAP SuccessFactors, that were once revolutionary, now face challenges like inefficiency, poor integration, and outdated user experiences. The time for transformation is now. 2025 offers HR Directors the chance to reimagine their tech strategies: > AI & Automation: Automate tasks like payroll and onboarding, allowing HR teams to focus on strategic initiatives. > Integrated Platforms: Cloud-based systems streamline operations, improving efficiency by 20% and cutting costs by 15% (Forbes). > Enhanced Employee Experience: Personalised tools and feedback systems boost engagement and retention. > Data-Driven Insights: Advanced analytics provide actionable insights into workforce trends and decision-making. 4 key considerations to make when evaluating HR Tech: 1) Focus on Scalability: Choose solutions that integrate well and grow with your organisation. 2) Embrace Automation: Free up time for strategic HR work. 3) Prioritise Employee Experience: Invest in tools that enhance learning, wellbeing, and communication. 4) Leverage Analytics: Use data to drive smarter talent and business decisions. 2025 is the time for HR leaders to modernise their tech infrastructure. Outdated systems no longer meet the demands of a dynamic, data-driven workforce. By embracing advancements in technology, HR departments can deliver greater efficiency, employee satisfaction, and business outcomes. What HR tech changes are you looking to make in 2025? #FutureOfHR #HRTech #HRInnovation #FutureOfWork P.S. This is 1 of 9 trends featured in our new report, Shaping the Future of HR. Find all 9 on the TalentMapper website 🔍 (1/9)

🔘 Difference between IAM & PAM In Azure, Identity and Access Management (IAM) and Privileged Access Management (PAM) are both critical for securing resources, but they address different aspects of identity and access control. Here's a breakdown of their differences: 🔑 IAM (Identity and Access Management) Focuses on managing users, groups, and roles to provide appropriate access to Azure resources. Ensures that the right users have access to the right resources at the right time. 🛡️ PAM (Privileged Access Management) Deals specifically with managing and securing privileged roles and access, which have elevated permissions (e.g., Global Administrator, Owner). Aims to minimize risks associated with excessive or unnecessary high-level access. 1️⃣ Scope ◾ IAM ▫️ Broad in scope: includes all users, devices, groups, and their access levels to resources. ▫️ Applies to regular users, service accounts, and even external identities like partners or guests. ◾ PAM ▫️ Narrower focus: targets users and roles with elevated privileges. ▫️ Ensures administrative accounts are not always active or exposed to minimize potential misuse or breaches. 2️⃣ Key Features ◾ IAM Features: ▫️ Role-Based Access Control (RBAC): Assign roles to users/groups to control access to Azure resources (e.g., Reader, Contributor). ▫️ Conditional Access: Enforce access policies based on device, location, or risk level. ▫️ Identity Protection: Detect and remediate identity-based threats (e.g., compromised credentials). ▫️ Integration with Azure Active Directory (Azure AD): Centralized user identity management, Single Sign-On (SSO), and federation. ◾ PAM Features: ▫️ Azure AD Privileged Identity Management (PIM): Manage, monitor, and audit access to privileged roles like Global Administrator or Resource Owner. Temporary and just-in-time (JIT) access to reduce exposure. ▫️ Approval Workflow: Require approvals for activating privileged roles. ▫️ Access Reviews: Periodically review and certify privileged access. ▫️ Audit and Alerts: Track privileged role activations and alert unusual behavior. 3️⃣ Use Cases ◾ IAM Use Cases: ▫️ Granting a user Reader access to a specific resource group. ▫️ Enforcing Conditional Access to require MFA for all users logging in from untrusted networks. ▫️ Assigning external partners Guest access to collaborate on specific projects. ◾ PAM Use Cases: ▫️ Activating Global Administrator privileges only when needed for specific tasks. ▫️ Requiring approval for assigning the Subscription Owner role to a user. ▫️ Enforcing JIT access for a developer needing Contributor permissions for troubleshooting. 4️⃣ Security Goals ◾ IAM ▫️ Ensure every identity has only the minimum access needed to perform their job. ▫️ Protect regular users' credentials and access pathways. ◾ PAM ▫️ Protect administrative access from being exposed or overused. ▫️ Reduce the attack surface by ensuring elevated access is not permanently assigned.

Tape and VTL were built for a world that no longer exists. Enterprises today run hybrid ecosystems, and mainframes sit right in the center. The challenge is how to modernize storage without disrupting decades of stability. That is where BMC Software's AMI Cloud Data with Cloud Data Sets (CDS) bridges the gap between legacy reliability and cloud agility. Check out the video here – https://lnkd.in/grWMRFP7 Here is what makes it so impactful: -- Direct integration with the cloud. CDS writes and reads directly from object storage, removing unnecessary steps and complexity. -- Parallel processing built in. Large datasets move seamlessly at gigabytes-per-second speeds through advanced parallelism. -- Smarter cost control. No more expensive FICON, encryption key, or tape management licenses. You free up budget for innovation. -- Security from the start. Immutability ensures data cannot be altered, and encryption protects it end-to-end. -- zIIP-enabled efficiency. Offloading tasks to zIIP engines cuts CPU usage and lowers IBM software licensing costs. BMC’s short video shows how CDS helps enterprises modernize with confidence, not disruption. Watch here: https://lnkd.in/gb6jj7Sv Modernization is not about replacing your mainframe. It is about making it ready for what comes next. #data #ai #security #mainframes #cloud #bmi #ibm #bmc #theravitshow

🌐 Demystifying Network Protocols: A Quick Guide! 📊 Network protocols function as the main pillars that enable communication between devices over a network. Understanding major networking protocols is important for IT professionals. However, there are a lot to remember, in this piece we'll break down some of the most important ones. 🔌 TCP/IP (Transmission Control Protocol/Internet Protocol) This protocol is the underlying method of how information is passed between devices on the internet. While IP is responsible for addressing and routing data packets, TCP takes care of assembling the data into packets, as well as reliable delivery. 🌐 HTTP (Hypertext Transfer Protocol) When accessing websites, HTTP plays a crucial role. It's responsible for fetching and delivering web content from servers to end-users. 🔐 HTTPS (Hypertext Transfer Protocol Secure) An enhanced version of HTTP, HTTPS integrates security protocols (namely TLS) to encrypt data, ensuring a secure and confidential exchange between browsers and websites. 📂 FTP (File Transfer Protocol) As the name suggests, FTP is used for transferring files (uploading and downloading) between computers on a network. 📧 UDP (User Datagram Protocol) A more streamlined counterpart to TCP, UDP transmits data without the overhead of establishing a connection, leading to faster transmission but without the guarantee that the data will be delivered or in order. 📬 SMTP (Simple Mail Transfer Protocol) The driving force behind email communication, SMTP manages the formatting, routing, and delivery of emails between mail servers. 🔒 SSH (Secure Shell) Secure Shell is a cryptographic network protocol that ensures safe data transmission over an unsecured network. It provides a safe channel, making sure that hackers can't interpret the information by eavesdropping. 🚀 Understanding these protocols is crucial for anyone in the IT and networking field. They are the building blocks of the internet and digital communication. 💬 I'd love to hear your thoughts. Are there any other protocols or concepts you'd like to add to this list?

Everyone talks about scalability. Very few talk about where the latency is hiding. I once worked on a system where a single API call took ~450ms. The team kept trying to “scale the service” by adding more replicas. Pods were multiplied. Autoscaling was tuned. Dashboards were made fancier. But the request still took ~450ms. Because the problem was never about scale. It was this: - 180ms spent waiting on a downstream service. - 120ms on a database round-trip over a noisy network hop. - 80ms wasted in JSON -> DTO -> Internal Model conversions. - 40ms in logging + metrics I/O. - The actual business logic: ~15ms. We were scaling the symptom, not the cause. Optimizing that request had nothing to do with distributed systems wizardry. It was mostly about treating latency as a budget, not as a consequence. Here’s the framework we used that changed everything: - Latency Budget = Time Allowed for Request - Breakdown = Where That Time Is Actually Spent - Gap = Budget - Breakdown And then we asked just one question: “What is the single biggest chunk of time we can remove without changing the system’s behavior?” This is what we ended up doing: - Moved DB calls to a closer subnet (dropped ~60ms) - Cached the downstream call response intelligently (saved ~150ms) - Switched internal models to protobuf (saved ~40ms) - Batched our metrics (saved ~20ms) The API dropped to ~120ms. Without more servers. Without more Kubernetes magic. Just engineering clarity. 🚀 Scalability isn’t just about adding compute. It’s about understanding where the time goes. Most “slow” systems aren’t slow. They’re just unobserved.

AWS has 200+ services. Most data professionals only need 15. (Once you know these, AWS stops feeling overwhelming) I've seen too many people bounce between random tutorials and give up halfway. The problem isn't AWS. It's not having a mental model. Most data systems, no matter how complex, are built on just five layers: Storage → Processing → Analytics → Machine Learning → Security Once that clicks, everything becomes logical. Here are the 15 AWS services every Data Analyst and Data Scientist should know: 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 & 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐬 ↳ S3: Your data lake foundation. Raw files, CSVs, Parquet - everything starts here. ↳ RDS: Managed PostgreSQL/MySQL for relational workloads. ↳ Redshift: Cloud data warehouse for SQL on massive datasets. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐄𝐓𝐋 ↳ Glue: Serverless ETL across sources. ↳ Athena: Query S3 directly with SQL. No infrastructure. ↳ EMR: Spark and Hadoop for large-scale processing. ↳ Lambda: Event-driven compute for pipeline automation. 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐈 ↳ QuickSight: Native BI for dashboards and visualizations. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 ↳ SageMaker: End-to-end ML platform for building and deploying models. ↳ Bedrock: Access foundation models like Claude and Llama. ↳ Comprehend: NLP insights from text without custom models. 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 & 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 ↳ Kinesis: Ingest and process streaming data. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐀𝐜𝐜𝐞𝐬𝐬 ↳ IAM: Define who can access what. ↳ KMS: Manage encryption keys. ↳ Secrets Manager: Store and rotate API keys and credentials. 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐨𝐮𝐭? 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐢𝐬 𝐩𝐚𝐭𝐡: S3 → Athena → Glue → Redshift → SageMaker Master this flow and you'll understand how most modern data platforms on AWS are built. 𝐅𝐫𝐞𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝: 1. AWS Skill Builder (free tier): https://skillbuilder.aws/ 2. freeCodeCamp AWS Cloud Practitioner: https://lnkd.in/dJc6Eybc 3. AWS Documentation & Tutorials: https://lnkd.in/dqzSmhCd Which AWS service are you learning right now? 👇 ♻️ Repost to help someone feeling overwhelmed by AWS 📘 Preparing for data analyst interviews? Check out the book I co-authored with Pritesh and Amney with 150+ real questions: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share tips on data analytics & data science in my free newsletter. Join 23,000+ readers → https://lnkd.in/dUfe4Ac6