Digital Literacy Training

As hybrid-cloud adoption accelerates, mastering the right 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹𝘀 is essential for building 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 applications.  Here’s a 𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 that breaks down the critical domains and technologies you need to know:  🔴 𝟭. 𝗟𝗶𝗻𝘂𝘅 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 Linux is the backbone of cloud-native systems. 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝗹 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀, 𝗯𝗮𝘀𝗵 𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 like Ubuntu, Red Hat, and Alpine to navigate the cloud world with confidence.  🟢 𝟮. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 Cloud connectivity depends on 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗹𝗶𝗸𝗲 𝗛𝗧𝗧𝗣, 𝗦𝗦𝗟, 𝗧𝗖𝗣/𝗜𝗣, 𝗮𝗻𝗱 𝗗𝗡𝗦. Tools like 𝗪𝗶𝗿𝗲𝘀𝗵𝗮𝗿𝗸 help monitor and secure network traffic. 𝗟𝗲𝗮𝗿𝗻 𝗦𝗦𝗛, 𝗩𝗣𝗡𝘀, 𝗮𝗻𝗱 𝗳𝗶𝗿𝗲𝘄𝗮𝗹𝗹𝘀 to strengthen cloud security.  🔵 𝟯. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀   Cloud is non-negotiable! Whether 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗼𝗿 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱, understand key models like 𝗦𝗮𝗮𝗦, 𝗣𝗮𝗮𝗦, 𝗮𝗻𝗱 𝗜𝗮𝗮𝗦 and how to deploy, scale, and manage workloads effectively.  🟣 𝟰. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Security is a must-have in cloud-native environments. Master 𝗜𝗔𝗠 (𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 & 𝗔𝗰𝗰𝗲𝘀𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁), 𝗢𝗽𝗲𝗻 𝗣𝗼𝗹𝗶𝗰𝘆 𝗔𝗴𝗲𝗻𝘁, 𝗣𝗿𝗶𝘀𝗺𝗮, 𝗮𝗻𝗱 𝗦𝗲𝗰𝗿𝗲𝘁𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗩𝗮𝘂𝗹𝘁, 𝗔𝗪𝗦 𝗞𝗠𝗦) to protect your applications.  🟡 𝟱. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲𝗱 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁! Get hands-on with:   ⚙️ Docker – Build lightweight, portable applications   ⚙️ Kubernetes – Automate deployment & scaling   ⚙️ Istio & Service Mesh – Secure and manage microservices  🟠 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 (𝗜𝗮𝗖) Automate infrastructure with 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺, 𝗣𝘂𝗹𝘂𝗺𝗶, 𝗖𝗹𝗼𝘂𝗱𝗙𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, and configuration management tools like 𝗔𝗻𝘀𝗶𝗯𝗹𝗲, 𝗖𝗵𝗲𝗳, 𝗮𝗻𝗱 𝗣𝘂𝗽𝗽𝗲𝘁. This ensures 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 across environments.  🟢 𝟳. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Monitor, troubleshoot, and optimize cloud applications with:  📌 Prometheus & Grafana – Metrics & visualization   📌 Elastic Stack (ELK) – Log aggregation   📌 OpenTelemetry – Distributed tracing  🔵 𝟴. 𝗖𝗜/𝗖𝗗 – 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 Modern DevOps is 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻! Learn:   ✅ GitHub Actions, GitLab CI/CD, Jenkins – Automate testing & deployment   ✅ ArgoCD & Flux (GitOps) – Declarative Kubernetes deployments  𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗶𝘀 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 – 𝗦𝘁𝗮𝘆 𝗔𝗵𝗲𝗮𝗱!  This roadmap lays the foundation for cloud-native success, but the landscape is constantly evolving.  𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗴𝗼-𝘁𝗼 𝘁𝗼𝗼𝗹 𝗼𝗿 𝗺𝘂𝘀𝘁-𝗸𝗻𝗼𝘄 𝗰𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁? Share in the comments! 👇

I often get asked, “What digital tools do you use daily?” Here’s what works for me! First, my AI “stack”: On my computer, I’ve developed a system where my workflow automatically channels prompts across multiple AI models—including various iterations of ChatGPT and Claude. This “swarm” approach takes out my query to multiple models and finds the best model to answer my specific question. In the future, I assume many agents will work similarly. Aside from personal agents, I frequently use ChatGPT voice mode as a learning and dictation engine, and leverage coding models like Replit to quickly turn ideas into working prototypes. My non-AI “stack”:  Although I’ve tried to leverage cutting-edge tools, we all have more “old-school” tools we just don’t want to give up. I continue to use Evernote because it’s incredibly effective for capturing detailed meeting notes and preserving all the nuances of ongoing conversations. At the same time, when I dive into project-based work, I lean on OneNote. I’ve learned that aligning the type of tool with the nature of the task can make a significant difference. Using different platforms helps reinforce the context and association between the work and the tool itself, ultimately enhancing organization and clarity. And for hardware:  At home, my dual-screen setup is a productivity booster that lets me manage multiple streams of information simultaneously. However, this setup means that when I travel, I often end up juggling multiple laptops to maintain that same level of efficiency. It’s a reminder that progress isn’t always about simplifying our technology—it sometimes means handling a more diverse array of devices to keep up with our evolving needs!

If you're graduating in 2024, 2025, or 2026 and want to build a career in Cloud, here’s how you can get started: 1. Understanding Cloud Computing: Cloud computing is like storing your files on Google Drive or Dropbox, but much more powerful. Instead of storing just files, companies use cloud computing to run their apps, store huge amounts of data, and do heavy computing tasks. It's like having a supercomputer you can access from anywhere. 2. Why Cloud Computing? Cloud computing is the future. Many companies are moving to the cloud because it’s cheaper, faster, and safer. Jobs in cloud computing are growing fast, and they pay well. If you learn cloud computing, you'll be ready for these jobs. 3. Basic Skills You Need: - Programming: Learn a programming language like Python. It’s easy to start with and widely used in cloud computing. - Linux: Many cloud services run on Linux. Knowing basic Linux commands is very helpful. - Networking: Understand how the internet works—things like IP addresses, DNS, and HTTP. These are important when working with cloud services. 4. Start with Cloud Platforms: - AWS (Amazon Web Services): The most popular cloud platform. Start with their free tier to get hands-on experience. - Google Cloud: Another major player in cloud computing. Google Cloud also offers free credits for students. - Microsoft Azure: Popular in many businesses, especially those using Microsoft products. 5. Get Certified: Cloud certifications prove that you have the skills to work in cloud computing. Some beginner certifications to consider: - AWS Certified Cloud Practitioner - Microsoft Certified: Azure Fundamentals - Google Associate Cloud Engineer These certifications are designed for beginners, and they show employers that you’re serious about cloud computing. 6. Hands-On Practice: The best way to learn cloud computing is by doing. Set up small projects, like hosting a website or creating a virtual machine, using the free tier of any cloud platform. Practice will help you understand how things work in the cloud. 7. Build a Portfolio: Showcase your skills by creating a portfolio. Include your projects, certifications, and anything else that shows your knowledge of cloud computing. A strong portfolio can help you stand out when applying for jobs. 8. Start Applying: Once you have the skills, certifications, and portfolio, start applying for internships or entry-level positions in cloud computing. Even if you don’t get the job right away, keep trying. The experience of applying and interviewing will prepare you for future opportunities. By starting early and focusing on these steps, you can build a strong foundation in cloud computing and increase your chances of getting a job in this exciting field. For more valuable content like this, follow Vikram Gaur #CloudComputing #CloudEngineer #CloudDeveloper #AWS #Azure #GoogleCloud #google #GoogleCloudPlatform #gcp #GoogleCloudArcade #Amazonintern #DevOps #DevOpsEngineer #placement #internships

Here’s how you can manage a remote team like a pro. Remote teams can be 25% more productive with proper management and tools. Mayank and I have managed hundreds of developers remotely. Here are key strategies that we at Supersourcing have discovered to boost productivity and foster a thriving remote culture: - Define clear communication channels. This will help avoid misunderstandings and keep your team aligned. -Set up virtual team-building activities to foster connections and camaraderie, even from afar. -Implement Regular check-ins. Very important to ensure everyone stays on track and feels supported. -Use the right collaboration tools and streamline workflows to boost efficiency. -Establish clear goals and metrics to measure progress and success. -Promote a culture of trust and autonomy by encouraging team members to take ownership and deliver results. -Invest in continuous learning and development to support skill growth and stay updated with industry trends. Creating a successful remote team goes beyond just hiring the right talent. It's about creating an environment where your team can excel, no matter where they are. Effective communication, team-building, regular check-ins, and the right tools can transform remote work from a challenge into a strength. What’s your top challenge in managing remote teams? Share your experiences and let’s discuss how we can overcome them together.

🚀 Agentic AI Identity and Access Management: A New Approach In my View ..... "Architectures are going to change ; Approach to Development is going to change ; In Secure First and Automation First Era , we need to work Digital 1st , Intelligent 1st Approach to avoid rework ..." ▬▬▬▬▬▬▬▬▬▬▬▬▬ 🌍 Let's find how we can it with Identity and Access Management .. #AgenticAI is pushing the boundaries of automation, autonomy, and decision-making at machine speed. But traditional identity and access management (IAM) protocols, designed for static applications and human users, can’t keep up. This publication from the Cloud Security Alliance (CSA) introduces a purpose-built Agentic AI IAM framework that accounts for autonomy, ephemerality, and delegation patterns of AI agents in complex Multi-Agent Systems (MAS). It provides security architects and identity professionals with a blueprint to manage agent identities using Decentralized Identifiers ( #DIDs), Verifiable Credentials ( #VCs), and Zero Trust principles, while addressing operational challenges like secure delegation, policy enforcement, and real-time monitoring. 🞕 Let's understand - ➟ Identify shortcomings of OAuth 2.1, SAML, and OIDC in agentic environments ➟ Define rich, verifiable Agent IDs that support traceable, dynamic authentication ➟ Apply decentralized and privacy-preserving cryptographic architectures Enforce fine-grained, context-aware access control using just-in-time credentials ➟ Build zero trust IAM systems capable of scaling to thousands of agents ▬▬▬▬▬▬▬▬▬▬▬▬▬ 🎯 Bottomline - With detailed guidance on deployment models, governance consideration, and threat mitigation using the MAESTRO framework, this publication lays the foundation for secure identity and access in the next generation of AI systems. ▬▬▬▬▬▬▬▬▬▬▬▬▬ Its wake-up call for existing Identity and Access Management frameworks and companies.... Excellent Read for Weekend !! #Security #Identity #AI #Automation #Technology

If your story doesn't hit in the first 5 seconds It's Over You don’t get minutes to earn attention anymore. You get moments. That’s why the best ads today don’t start by selling. They start by storytelling, fast. Take this campaign: It opens like a zombie thriller. Not a product demo. Not a stat dump. Not a polished brand shot. But a story that grabs your brain before it even knows what it's watching. So why does it work so well? 📌 It uses genre to create instant tension Within seconds, we’re in a world. It’s not just an ad, it’s a scene. A story. One you can’t look away from. 📌 It anchors emotion before explanation We feel before we understand. That’s what powerful stories do 📌 It educates through narrative By the time we realize the message (synthetic materials take 200+ years to decompose), we’re already emotionally invested. 📌 It aligns cause with creativity This isn’t preachy. It’s precise. The storytelling is the message. The product is the punchline. Want to build content that hits like this? Here’s a storytelling framework to try: 1️⃣ Hook with conflict Every good story starts with tension. Show us something broken, scary, or just plain weird. Make us lean in. 2️⃣ Introduce transformation What changes? What insight or solution comes next? Keep us moving through the arc. 3️⃣ Reveal your message last Don’t start with “what”, start with “why care.” Let the product or idea emerge from the emotion. 4️⃣ Make it feel cinematic Use sound, visuals, pacing, not to show off, but to bring your audience into the moment. 5️⃣ Keep it short, sharp, and story-first We’re in the TikTok era. But attention spans haven’t died, they’ve just gotten pickier. Stories still win. Always. The best storytelling doesn’t sell the product. It sells the belief behind the product. And if you want your brand to rise above the noise Stop pitching. Start telling better stories. #storytelling #branding #sellwithstories #marketingtips I share storytelling and creativity to help you and your company sell more and grow. Let's Connect! 1. Try my other course on LinkedIn Learning: https://lnkd.in/gTh8R5Mc 2. Join 10,000 others learning weekly growth tips at: https://lnkd.in/eCDKabp2 Use the 3-Act E.P.I.C Structure to turn stories into sales: https://lnkd.in/e9_eczTG 3. 3 Ways To Grow Guide: https://lnkd.in/gZaq56hT (no sign-up needed)

When I first started my data journey, I struggled to figure out which tools were used for each part of the data process. It was overwhelming so many options, and no clear roadmap! But over time, I learned how each tool fits into the data workflow. Here’s a breakdown that helped me navigate this: 📌 Data Collection & Storage: The foundation of every data process. Databases: MS SQL Server, MySQL, MongoDB Data Warehouses/Lakes: Snowflake, Google BigQuery, Amazon S3 📌 Data Orchestration: Automating workflows to streamline data movement. Tools: Apache Airflow, Prefect 📌 Data Cleaning & Preparation: Because dirty data leads to messy results. Tools: Python (pandas, NumPy), Excel, OpenRefine 📌 Data Analysis: Finding patterns, trends, and actionable insights. Tools: SQL, Excel, Python (pandas, matplotlib), R 📌 Data Visualization: Transforming numbers into stories people can understand. Tools: Power BI, Tableau, Looker, Python (seaborn, matplotlib) 📌 Machine Learning: Building predictive models and automations. Tools: Python (scikit-learn, TensorFlow), PyTorch, R 📌 Version Control: Collaborating and tracking changes in your work. Tools: Git (GitHub, GitLab) 📌 Documentation & Collaboration: Making work accessible and organized. Tools: Jupyter Notebooks, Confluence ✏️ Here’s What I Did: I started small, picking just one tool from each category. For example, I began analyzing small datasets in Excel and SQL, moved on to Python for cleaning, and eventually explored Power BI for storytelling through visuals. Tackling one step at a time helped me build a strong foundation without getting lost in the sea of tools. 🔆 Which tools are you using for your data journey? Do you have any favorites or alternatives to these tools? Comment below I’d love to learn from you! 🌐If you found this helpful, like and repost to reach others who might need it. ✳️Follow for more daily content!

I can see you're on mute again, Sarah..." Why 78% of Remote teams are failing? (And How to Fix It) Have you ever finished a virtual meeting and realized no actual decisions were made, despite an hour of everyone staring at their screens? The reality: We've mastered the tools of remote work without mastering the human element of collaboration. After coaching over 500 professionals through remote transitions, I've identified the hidden obstacles that even the most tech-savvy teams miss: 1. The Trust Deficit When you can't see someone working, the primitive part of your brain fills the void with assumptions. "Did they even look at that document?" "Are they really working or watching Netflix?" This trust gap creates a cycle where managers over-monitor, employees feel micromanaged, and psychological safety plummets. 2. The Collaboration Paradox Remote teams often swing to dangerous extremes: Too many meetings, creating Zoom fatigue and no focus time Too few synchronous touchpoints, creating silos and duplication of work Stanford research shows that collaborative overload reduces productive output by up to 42%, while insufficient collaboration leads to 26% more project delays. 3. The Digital Culture Vacuum The spontaneous moments that build culture in offices—grabbing coffee, celebrating small wins, quick hallway conversations—disappear in remote settings. Without intentional replacement, team cohesion disintegrates within 4-6 months. This framework has worked wonders for teams that were struggling with managing remote work and also reduced meeting time by 22%. Step 1: Establish Trust Through Clarity, Not Control Replace arbitrary "online hours" with clear outcome metrics Institute "no-questions-asked" flexibility alongside non-negotiable deadlines Create transparent dashboards that focus on results, not activity Step 2: Design Your Collaboration Architecture Implement "Meeting Tiers"—distinguish between decision meetings, working sessions, and updates Create "Deep Work Zones"—4-hour blocks where no meetings are scheduled (team-wide) Adopt asynchronous-first documentation that reduces meeting dependency Step 3: Engineer a Digital-First Culture Launch "Virtual Watercooler" moments (15-min team check-ins with no work talk) Use "Culture Buddies" to pair team members weekly across departments Create "Celebration Channels" focused exclusively on wins and milestones Remote work is supposed to be enjoyed by both parties. If that’s not the case, you need to address it right. What's one aspect of your remote collaboration that needs immediate attention? Follow Priya Narang Nagpal for more Career & Corporate training strategies! Repost 🔁 if found useful. #careergrowth #jobsearch #corporatetrainer #softskills #resumewriter #interviewcoach

From BASIC to ADVANCED data engineering skills, begin your journey here 👉 The data engineering roadmap: 📍 Beginner Skills: a) Data Modeling:   • Understand relational database concepts   • Learn basic programming/SQL querying   • Practice creating Entity-Relationship Diagrams (ERDs) b) ETL Basics:   • Grasp the concept of Extract, Transform, Load (ETL)   • Use tools like Apache NiFi or Talend for simple data pipelines c) Data Storage:   • Familiarize with different database types (SQL, NoSQL)   • Learn basic data warehouse concepts d) Version Control:   • Master Git for code management   • Understand branching and merging strategies 📍 Intermediate Skills: a) Big Data Processing:   • Learn Apache Spark for distributed computing   • Understand batch vs. stream processing b) Data Warehousing:   • Implement star and snowflake schemas   • Use tools like Amazon Redshift or Google BigQuery c) Data Integration:   • Master Change Data Capture (CDC) techniques   • Implement data quality checks and data cleansing d) Cloud Platforms:   • Gain proficiency in AWS, GCP, or Azure data services   • Implement cloud-native ETL solutions 📍 Advanced Skills: a) Data Governance:   • Implement data lineage tracking   • Ensure compliance with regulations (GDPR, CCPA) b) Real-time Analytics:   • Design streaming data architectures   • Use technologies like Apache Kafka or Apache Flink c) Machine Learning Operations (MLOps):   • Design data pipelines for ML model training and deployment   • Implement feature stores for ML d) Data Mesh Architecture:   • Understand domain-driven design for data   • Implement data products and self-serve data platforms Key Architectural Considerations: 1. Scalability 2. Performance 3. Security 4. Cost-Effectiveness 5. Data Quality 6. Metadata Management 7. Interoperability Some important data platforms to explore are Snowflake, dbt, Databricks, and Matillion. As a data engineer, Good data engineering practices lay the foundation for trustworthy analytics and machine learning initiatives. If you're looking for a good list of resources and projects, find them below in the comments section!👇 It won't happen overnight. 𝗢𝗻𝗲 𝘀𝘁𝗲𝗽 𝗮 𝗱𝗮𝘆 𝗸𝗲𝗲𝗽𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝗴𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝘄𝗮𝘆. Happy Learning :) Stay tuned for more! 🙂 #data #engineering #bigdata #cloud

Most professionals get stuck in reporting mode. You know, endless charts, dashboards, and status updates. But real impact happens when you show: Why it happened. What’s next. ...not just what happened last week/month/quarter. Here’s the ladder to level up your data skills: Level 1: Reporting You build dashboards, clean data, make charts. Tools: Excel, Sheets, Power BI. Make no mistake. This is foundational. This is called "Descriptive Analytics," and your leaders must have it. However, think of it like electricity. They'll only appreciate it when it's gone. Level 2: Exploratory Analysis Now you're asking: • What patterns are in the data? • What metrics truly matter? • Where are the outliers? This is where you get to why something happened. Tools: Excel, SQL, Python. Leaders value explanations - especially when things aren't going well. Level 3: Pattern Discovery (Unsupervised ML) You start finding structure in messy data. No labels. Just hidden groupings. Examples: • Customer segments • Product groupings Tools: K-means & DBSCAN. Start delighting leaders with your new insights. Use Python in Excel to get started. Level 4: Predictive Modeling (Supervised ML) Now you’re using data like a crystal ball: • Will a customer cancel? • Will a loan default? • Will a deal close? Tools: Decision trees & Random Forests. Successful predictions provide the "why." It's magical. Use Python in Excel to get started. Level 5: Mindset Are you already good at Excel?  You’re closer than you think. Steps 1 & 2?  You’ve probably got that down. Time to step up into 3 & 4. Remember - it isn't a leap. It's just the next rung on the ladder.