Networking for Data Analysts

🚀 Advanced Network Troubleshooting Using the TCP/IP Model 🛠️ Effective network troubleshooting requires a methodical approach, and the TCP/IP model is a perfect framework. Here's how to perform advanced diagnostics, layer by layer: 🔍 1. Physical Layer Start with the fundamentals. Always verify the hardware connection quality. ✅ Action: Inspect all network cables, ensure there are no loose connections, and confirm the integrity of ports. 💡 Pro Tip: Use link-state monitoring tools or hardware diagnostics to detect faulty cabling or port issues that might go unnoticed with a casual check. 🔍 2. Data Link Layer At this layer, network interface integrity is key. ✅ Action: Investigate the functionality of network interfaces (NICs) and switches. Ensure that MAC addressing and duplex settings are appropriately configured. 💡 Pro Tip: Utilize tools like Wireshark to inspect traffic patterns and detect any anomalies at Layer 2, such as broadcast storms or MAC address conflicts. 🔍 3. Network Layer Routing and IP configuration are crucial here. ✅ Action: Assess IP configurations (including subnet masks, default gateways, and routing tables). Ensure proper communication paths. 💡 Pro Tip: Advanced commands like tracert (Windows) or traceroute (Linux) can help diagnose routing issues and pinpoint where packets drop in transit. 🔍 4. Transport Layer Connectivity checks go beyond basic pings. ✅ Action: Test transport protocols (TCP/UDP). Ensure sessions are being properly established and maintained. 💡 Pro Tip: Use tools like netstat to analyze active connections and identify ports being used for communication, revealing potential firewall or service-based issues. 🔍 5. Application Layer Finally, validate that the application protocols are functioning as expected. ✅ Action: Analyze DNS, HTTP/HTTPS, and other services for latency or resolution issues. DNS misconfigurations can often mimic deeper network issues. 💡 Pro Tip: Tools like dig and nslookup can offer insights into DNS query responses. Advanced monitoring solutions such as APM tools (Application Performance Monitoring) can help track application performance bottlenecks. By leveraging these techniques and tools at each layer, you can systematically isolate and resolve even the most complex network issues. 💼💡 #AdvancedNetworking #TCPIP #NetworkEngineering #ITProfessional #TechLeadership #Infrastructure #NetworkSecurity #ITInnovation #CCNA #CCNP

APIs aren't just endpoints for data engineers - they're the lifelines of your entire data ecosystem. Choosing the Right API Architecture Can Make or Break Your Data Pipeline. As data engineers, we often obsess over storage formats, orchestration tools, and query performance—but overlook one critical piece: API architecture. APIs are the arteries of modern data systems. From real-time streaming to batch processing - every data flow depends on how well your APIs handle the load, latency, and reliability demands. 🔧 Here are 6 API styles and where they shine in data engineering: 𝗦𝗢𝗔𝗣 – Rigid but reliable. Still used in legacy financial and healthcare systems where strict contracts matter. 𝗥𝗘𝗦𝗧 – Clean and resource-oriented. Great for exposing data services and integrating with modern web apps. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 – Precise data fetching. Ideal for analytics dashboards or mobile apps where over-fetching is costly. 𝗴𝗥𝗣𝗖 – Blazing fast and compact. Perfect for internal microservices and real-time data processing. 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 – Bi-directional. A must for streaming data, live metrics, or collaborative tools. 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 – Event-driven. Lightweight and powerful for triggering ETL jobs or syncing systems asynchronously. 💡 The right API architecture = faster pipelines, lower latency, and happier downstream consumers. As a data engineer, your API decisions don’t just affect developers—they shape the entire data ecosystem. 🎯 Real Data Engineering Scenarios to explore: Scenario 1: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Challenge: Process 100K+ transactions/second with <10ms latency Solution: gRPC for model serving + WebSocket for alerts Impact: 95% faster than REST-based approach Scenario 2: 𝗠𝘂𝗹𝘁𝗶-𝘁𝗲𝗻𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Challenge: Different customers need different data subsets Solution: GraphQL with smart caching and query optimization Impact: 70% reduction in database load, 3x faster dashboard loads Scenario 3: 𝗟𝗲𝗴𝗮𝗰𝘆 𝗘𝗥𝗣 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Challenge: Extract financial data from 20-year-old SAP system Solution: SOAP with robust error handling and transaction management Impact: 99.9% data consistency vs. 85% with custom REST wrapper Image Credits: Hasnain Ahmed Shaikh Which API style powers your pipelines today? #data #engineering #bigdata #API #datamining

We’re teaching AI to understand the language of business. Together with Stanford University, our SAP research team has developed a new approach for understanding enterprise data: the Relational Transformer (RT) – a foundation model that learns directly from multi-table, key-linked business data, not just text. The paper (currently under review) explores how RT can read relational databases like humans reason over spreadsheets. The model learns to predict masked cells during pretraining, and once trained, it can be prompted to solve new predictive business tasks, like payment dates, delivery delays, or upsell opportunities for customers, without any task-specific retraining. Why this is important? 💡 ➡️ RT achieves strong zero-shot accuracy on unseen datasets, coming close to fully supervised models – but with a fraction of the compute needed for prompting LLMs. ➡️ Fine-tuning RT is 10-100× more efficient than traditional baselines – making it practical for real-world enterprise data. ➡️ Its relational attention explicitly models rows, columns, and key links – the real structure of business data – rather than forcing it into plain text.   Huge thanks to our collaborators from Stanford University! We’re looking forward to the next steps in the review and publication process with you. This represents a major step toward AI that truly understands how businesses operate.   👉 Read the paper: https://lnkd.in/d4i83Dhc   Rishabh Ranjan Valter Hudovernik Mark Žnidar Charilaos Kanatsoulis Roshan Reddy Upendra, PhD Mahmoud(Reza) Mohammadi Joe Meyer Tom Palczewski Carlos Guestrin Jure Leskovec Johannes Hoffart Markus Kohler Yaad Oren Zhuohan (Mark) Li

Having a strong portfolio is one of the best ways to stand out when applying for a Data Analyst role. But it’s important to choose the right projects that show your skills and creativity. Here’s how you can create meaningful projects:- Don’t work on the same old ideas like simple sales dashboards or stock price analysis. These projects are very common and don’t make you stand out. Instead, try to pick unique and interesting topics that recruiters haven’t seen before. Think about real problems faced by companies. For example, mobility companies like Uber, Ola, or Rapido face issues where some drivers ask customers to cancel rides so they can complete trips offline. This leads to revenue loss for the company. You can take this as example to create a project to analyze this problem, quantify the losses, and suggest solutions. Use multiple tools in a single project to show your versatility. For example, you can use SQL to clean and organize data, Python to analyze it, and Power BI to create dashboards. This shows you can handle an entire process from start to finish. Focus on projects that solve real business problems like reducing customer churn, optimizing marketing budgets, or segmenting customers into different groups. These projects show that you understand how businesses operate and how data can make an impact. Explain how you thought through the problem when you present your project. For example, if you analyzed driver cancellations, explain how you broke the problem into smaller parts, analyzed the data, and came up with solutions. This helps others see your problem-solving approach. Combine multiple related problems into one project to make it more impactful. For example, you could analyze driver cancellations, identify peak times for offline completions, and create a dashboard to monitor revenue loss. Combining ideas makes your project more comprehensive and impressive. Try to find data sets that aren’t commonly used. Instead of downloading the same datasets everyone uses, explore platforms like Kaggle or open data portals, or even create your own data. This will make your projects look fresh and unique. Always share clear and actionable results in your projects. For example, if you worked on driver cancellations, suggest ways to reduce them, like adjusting incentives or monitoring systems. Finish your project with a clear and engaging dashboard to show your findings. By working on unique and meaningful projects, you can show your skills, creativity, and ability to solve real problems. Follow Shakra Shamim for more such posts.

“𝗧𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗶𝘀 𝘀𝗹𝗼𝘄.” Every Network Engineer has heard it. 😅 But Wireshark gives us something better than assumptions: 𝗽𝗮𝗰𝗸𝗲𝘁𝘀. I put together this 𝗪𝗶𝗿𝗲𝘀𝗵𝗮𝗿𝗸 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 → 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗰𝗮𝗿𝗼𝘂𝘀𝗲𝗹 to show how I approach packet analysis and troubleshooting—from understanding what’s inside a packet to following a real production issue all the way to root cause. Inside the carousel: 🔹 How to read a packet without getting overwhelmed 🔹 Capture filters vs. display filters 🔹 TCP 3-way handshake and connection failures 🔹 Retransmissions, Duplicate ACKs & Zero Window 🔹 RTT, packet loss, resets & DNS delays 🔹 A real-world “𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗹𝗼𝘄” investigation 🔹 Advanced TCP analysis & troubleshooting decision flow 🔹 A final Wireshark cheat sheet for quick reference The biggest lesson? 𝗗𝗼𝗻’𝘁 𝘁𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀. 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲. Sometimes the network is the problem. Sometimes it’s DNS, the firewall, the server, or the application. The packets help you prove which one. 🔎 💬 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀𝘀𝘂𝗲 𝘆𝗼𝘂’𝘃𝗲 𝗲𝘃𝗲𝗿 𝗳𝗼𝘂𝗻𝗱 𝘂𝘀𝗶𝗻𝗴 𝗪𝗶𝗿𝗲𝘀𝗵𝗮𝗿𝗸? Save the carousel for your next troubleshooting session. 🔖 #Wireshark #NetworkEngineering #NetworkEngineer #Networking #PacketAnalysis #NetworkTroubleshooting #TCPIP #TCP #CyberSecurity #NetworkSecurity #ITInfrastructure #Troubleshooting #CCNA #CCNP #TechLearning #ITCareers #NetworkMonitoring #InfrastructureEngineering #TechCommunity #LearningInPublic

I can't tell you the last time I looked at a resume in over 12 years of hiring, and I've hired many people. It's not even a nonstarter. I don't even think about it. I know others will disagree with me, but I don't find any value in the PDF version of your LinkedIn page. I'll just go to LinkedIn. But don't despair; there are many ways to garner attention, be introduced, or stand out. 1. Follow-up notes - it's incredible how often I do not receive a follow-up message after a conversation. It's so easy to do. Here's my opinion on the best follow-up message setup: (1) Thank the person for their time. (2) Bullet point a couple of things you learned from the conversation (3) Ask a question to re-engage. Send the follow-up within an hour of the interview. Send a note to each participant if it's a group meeting or panel. IMPORTANT: If you don't get a response after your first note, could you send a couple more? People are busy, and the inbox is even busier. 2. Use video - I always appreciate it when an applicant uses Loom or another video provider to send an introduction or thank you video. It's a rare occurrence that surprises me due to its ease of use. 3. Please research the role and be sure you are a fit. Are you framing your qualifications to match what the hiring managers are looking for? Ensure you fully understand what you are applying for. 4. Research the team and understand the company. Who are your hiring manager's peers? Who else would you want to meet? If you are interviewing with the CMO, contact the CRO or VP of Sales and try to schedule a meeting. It doesn't hurt to ask. There is no excuse not to research with tools like LinkedIn available to you. 5. Get an introduction before applying - Once you research and meet a couple of people from the company, ask for an introduction. I can count on both hands how often I've received an introduction from a team member for a role I am hiring for. Even better, if you are a referral from a trusted peer, you go to the top of the list, no matter what. 6. Prepare with questions—Spend an inordinate amount of time listing out questions you want to ask the interviewer. Discuss the role expectations, but don't forget to ask questions specifically about them, such as "Why did you choose to work here?" etc. Hiring great people isn’t about resumes but connections, preparation, and effort. Don’t rely on the same old playbook if you want to stand out. Get creative, do the work, and show why you’re the best fit for the role. The good news? Most people won’t do these things. That’s your edge.

𝗜 𝗷𝘂𝘀𝘁 𝗺𝗮𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗠𝗮𝗱𝗹𝗮𝗻𝗴𝗮 𝗖𝗼𝗺𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸: 52 individuals, 75 relationships spanning criminal cartels, politicians, and compromised law enforcement. The complexity is staggering – and it perfectly illustrates why our police agencies desperately need data analysts. As experts noted after reviewing both the Madlanga Commission and Ad Hoc Committee findings: the problem is "even bigger and more comprehensive than what was previously known." But here's the critical question Heather Ahlschlager raises: What happens AFTER the final report? Commissions expose. Reports get written. Recommendations sit on shelves. 𝗪𝗵𝘆? Because revelation without analytical capability = no sustained change. Criminal syndicates probably already use: → Network analysis to identify targets → Pattern recognition to evade detection → Financial intelligence to corrupt systems Law enforcement is fighting back with paper documents and spreadsheets. Data analysts can provide: ✓ Network mapping across jurisdictions ✓ Pattern detection linking cases ✓ Financial trail analysis ✓ Predictive corruption risk models ✓ Evidence integration and visualization Whould you like me to continue visualising the findings as they unfold? #DataAnalytics #LawEnforcement #SouthAfrica #MadlangaCommission #AntiCorruption 𝗗𝗶𝘀𝗰𝗹𝗮𝗶𝗺𝗲𝗿:  𝘈𝘭𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘵𝘩𝘪𝘴 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘪𝘴 𝘥𝘦𝘳𝘪𝘷𝘦𝘥 𝘧𝘳𝘰𝘮 𝘱𝘶𝘣𝘭𝘪𝘤 𝘤𝘰𝘮𝘮𝘪𝘴𝘴𝘪𝘰𝘯 𝘵𝘦𝘴𝘵𝘪𝘮𝘰𝘯𝘺, 𝘮𝘦𝘥𝘪𝘢 𝘳𝘦𝘱𝘰𝘳𝘵𝘴, 𝘢𝘯𝘥 public 𝘴𝘵𝘢𝘵𝘦𝘮𝘦𝘯𝘵𝘴. 𝘛𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘴 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘥𝘰𝘦𝘴 𝘯𝘰𝘵 𝘤𝘰𝘯𝘴𝘵𝘪𝘵𝘶𝘵𝘦 𝘭𝘦𝘨𝘢𝘭 𝘢𝘥𝘷𝘪𝘤𝘦 𝘰𝘳 𝘰𝘧𝘧𝘪𝘤𝘪𝘢𝘭 𝘧𝘪𝘯𝘥𝘪𝘯𝘨𝘴.

Our client pivoted from Sales to Data Analytics. They did it with no formal data experience. Here are 6 strategies they used to make it happen: Context: When our client reached out, they were stuck. They had spent months applying to data analyst roles with no success, despite completing a data analytics course. They had even received a verbal offer that was later rescinded. Frustration was building, and they were considering a return to account management. We teamed up with them, and things started to change: 1. They Clarified Their Target Role Before working with us, their approach was to just apply to any and every data analytics role that popped up. We helped shift that mindset to focus more of our energy on a smaller set of highly-aligned companies. They used this clarity to create a “Match Score” for each opportunity—filtering out roles that didn’t align with their ideal job. 2. They Optimized Their LinkedIn For What Employers Wanted To See Before joining, they weren’t getting any outreach for roles on LinkedIn. We revamped their LinkedIn headline and profile to include keywords specific to the Data Analytics space as well as projects that illustrated their capabilities. Then the inbound messages began to roll in. 3. They Shifted Their Time From Online Apps To Networking Instead of just applying online, they reached out to alumni from an analytics bootcamp they attended. They specifically focused on people who had successfully transitioned into data roles. One alum gave them insider insights into the hiring process at a target company and even suggested key skills to emphasize their application. 4. They Built A Consistent Outreach System They started sending 5 personalized LinkedIn messages per day to data professionals. They focused on asking for advice, then taking action on it and using it to open the door for a follow-up. This helped build rapport and trust, which led to multiple referrals and interviews. 5. They Went Deep On Interview Prep They knew that other candidates would likely have more “traditional” experience to lean on, so they went deep on interview prep. For technical interviews, they built a portfolio project analyzing Airbnb data to showcase SQL and visualization skills. For behavioral interviews, they prepared answer examples that tied directly into the company’s biggest needs and goals. 6. They Stayed Persistent & Flexible Originally, the recruiter who reached out was asking about a business analyst role. After pitching their SQL and Python skills, our client convinced the recruiter to get them in the door for a data analytics position. Then they used their networking to gain insider info on goals and challenges which they pitched in their interview. That approach secured the offer.

𝗪𝗲 𝘂𝘀𝗲 𝗮 𝗩𝗣𝗡. That's a good start—but it's not an OT remote access strategy. VPN, Jump Host and PAM are not competing technologies. They solve different problems, and the strongest OT architectures use them together. 🔹 𝗩𝗣𝗡 • Encrypts the communication path to the OT network. • Authenticates remote users. • Does not control privileged activities after the connection is established. 🔹 𝗝𝘂𝗺𝗽 𝗛𝗼𝘀𝘁 • Provides a single hardened entry point into the OT environment. • Centralizes remote access and improves session visibility. • Helps reduce opportunities for lateral movement. 🔹 𝗣𝗔𝗠 (𝗣𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁) • Protects privileged credentials through credential vaulting. • Enables just-in-time and time-bound privileged access. • Provides session recording, auditing and accountability. 💡 𝗔 𝘀𝗶𝗺𝗽𝗹𝗲 𝘄𝗮𝘆 𝘁𝗼 𝗿𝗲𝗺𝗲𝗺𝗯𝗲𝗿: 🚪 VPN → Gets you to the door. 🏢 Jump Host → Controls which room you can enter. 📝 PAM → Records what you did and ensures you only had the right privileges for the right time. 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗢𝗧 𝗿𝗲𝗺𝗼𝘁𝗲 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Vendor / Engineer ⬇ VPN ⬇ Jump Host ⬇ PAM ⬇ OT Assets (PLC • HMI • Historian • Engineering Workstation) 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: A VPN secures the connection. A Jump Host secures the entry point. A PAM solution secures privileged access. Together, they provide a far more resilient remote access architecture for OT than any one technology alone. #OTSecurity #ICSSecurity #IndustrialCybersecurity #RemoteAccess #PAM #JumpHost #ZeroTrust #VendorAccess #IEC62443 #CriticalInfrastructure

Graph traversal of healthcare claims I've been building this component by component at falcon health, and today I finally got to test it out fully. We built a graph (or network) traversal tool for our AI agent. Basically, when you see a claim from one provider, it's not just one claim. The member in the claim might have seen other providers, and those providers might also have seen other members. Yes, you can traverse this bipartite graph of members and providers back and forth. And when you hop around these networks multiple times, you might end up coming back to the starting point. Yes, you've made a circle. This circle can mean nothing, but sometimes it can be a critical discovery. Like the one below that we just found as I was testing the tool. These two providers were billing skin grafts with just the right intervals between them. One bills it, then the other bills it, and so on. If you're looking at one provider's claims alone, you might not realize there's any overbilling. You might think the provider is billing skin graft claims with proper intervals. But using network analysis, you actually find a connection—they might be "working together." And the best thing is you can just let AI explore these relationships. I'll play around with this a bit more over the next few days. If you're already a Falcon client, you can just ask "do some network analysis of Provider XYZ"!