Understanding Advanced Computing

Apache Spark has levels to it: - Level 0 You can run spark-shell or pyspark, it means you can start - Level 1 You understand the Spark execution model: • RDDs vs DataFrames vs Datasets • Transformations (map, filter, groupBy, join) vs Actions (collect, count, show) • Lazy execution & DAG (Directed Acyclic Graph) Master these concepts, and you’ll have a solid foundation - Level 2 Optimizing Spark Queries • Understand Catalyst Optimizer and how it rewrites queries for efficiency. • Master columnar storage and Parquet vs JSON vs CSV. • Use broadcast joins to avoid shuffle nightmares • Shuffle operations are expensive. Reduce them with partitioning and good data modeling • Coalesce vs Repartition—know when to use them. • Avoid UDFs unless absolutely necessary (they bypass Catalyst optimization). Level 3 Tuning for Performance at Scale • Master spark.sql.autoBroadcastJoinThreshold. • Understand how Task Parallelism works and set spark.sql.shuffle.partitions properly. • Skewed Data? Use adaptive execution! • Use EXPLAIN and queryExecution.debug to analyze execution plans. - Level 4 Deep Dive into Cluster Resource Management • Spark on YARN vs Kubernetes vs Standalone—know the tradeoffs. • Understand Executor vs Driver Memory—tune spark.executor.memory and spark.driver.memory. • Dynamic allocation (spark.dynamicAllocation.enabled=true) can save costs. • When to use RDDs over DataFrames (spoiler: almost never). What else did I miss for mastering Spark and distributed compute?

Tell me about QUANTUM COMPUTING in 2-minutes or less, using language my kid can understand. Challenge accepted. This was a question I got recently in a Q&A. I tried to channel my inner Hemingway. Big ideas, small words and short sentences! So if you fancy learning something new today - here's my take, and some useful resources worth checking out if you want a deeper dive. ⬇️ Imagine a computer that doesn’t just think in ones and zeros, like the ones we use today. A quantum computer uses "qubits" instead of bits. A bit can be a 1 or a 0. But a qubit can be both at the same time — this is called "superposition". It’s like flipping a coin and having it be heads and tails until you look.   Quantum computers also use something called entanglement. When two qubits are entangled, what happens to one instantly affects the other, even if they’re far apart. This lets quantum computers connect ideas in powerful new ways.   Because of superposition and entanglement, a quantum computer can explore many answers at once instead of one by one. That makes it super fast for some problems. It could help discover new medicines, protect data (search “quantum safe”), fight climate change, or even train smarter (ethical) AI.   But quantum computers are very hard to build. Qubits are delicate and can lose their power if they get too hot or too noisy. Scientists all over the world are racing to make them stronger and more stable. Quantum computers have to be kept at extremely low temperatures (-459°F) which is even colder than in outer space!   If they succeed, quantum computers could solve problems so big that today’s fastest supercomputers would take thousands of years to finish. Quantum computers won’t replace classical computers – but they will help us to solve many problems that we’ve never been able to solve before.   Quantum computers are not just faster – they give us a whole new way to understand the world. [263 words / 2 minutes] ⬇️ Want a Deeper Dive? 🥶 WATCH: Quantum computers exaplained by MKBHD [17 mins] https://lnkd.in/eNdRycfu 📒 READ: Wired's Easy Guide to Quantum Computing - Why It Works & How It Could Change The World https://lnkd.in/eiuAHxnQ 📖 FREE book "The Quantum Decade" from IBM Institute for Business Value https://lnkd.in/ejMCnKTX 🗺️ FUTURE: The Next 5 Years? Technology Atlas by IBM https://lnkd.in/ePaWdATp 📝 LEARN: 10 FREE courses (Most courses cost $2,500+ These 10 will get you started) https://lnkd.in/eM3k-Dtt

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.

Large Language Models (LLMs) are powerful, but how we 𝗮𝘂𝗴𝗺𝗲𝗻𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 them truly defines their impact. Here's a simple yet powerful breakdown of how AI systems are evolving: 𝟭. 𝗟𝗟𝗠 (𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲)   ↳ This is where it all started. You give a prompt, and the model predicts the next tokens. It's useful — but limited. No memory. No tools. Just raw prediction. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻)   ↳ A significant leap forward. Instead of relying only on the LLM’s training, we 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗳𝗿𝗼𝗺 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (like vector databases). The model then crafts a much more relevant, grounded response.   This is the backbone of many current AI search and chatbot applications. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲)   ↳ Now we’re entering a new era. Agent-based systems don’t just answer — they think, plan, retrieve, loop, and act.   They: - Use 𝘁𝗼𝗼𝗹𝘀 (APIs, search, code) - Access 𝗺𝗲𝗺𝗼𝗿𝘆 - Apply 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝘀 - And most importantly, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁 These architectures are foundational for building 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀. The future is not just about 𝘸𝘩𝘢𝘵 the model knows, but 𝘩𝘰𝘸 it operates. If you're building in this space — RAG and Agent architectures are where the real innovation is happening.

Large language models change their ethical decisions based on a single demographic detail. We tested this in 492,480 prompts with 9 models. The pattern was clear. High-income descriptors nudged models toward utilitarian reasoning. Cues about marginalized groups pulled them toward autonomy. These shifts happened even when the demographic information was irrelevant to the scenario. If this happens in triage or resource allocation, it’s not just an academic curiosity. It has real-world consequences. Vera Sorin, MD, CIIP Panagiotis Korfiatis Jeremy Collins Donald Apakama Mahmud Omar Ben Glicksberg @Mei-Ean Yeow @Megan Brandeland Girish Nadkarni https://lnkd.in/dy7FbrBb

This year, India’s defense sector unveiled advancements in AI that are reshaping military strategies & boosting national security. Here’s what the data tells us: --> AI is now central to defense modernization. --> Collaboration across sectors is driving innovation. Let’s explore these in detail. 1️⃣ AI-Powered Technologies Transforming Defense India’s armed forces are deploying AI across critical areas: ➤ Autonomy in operations: AI-enabled systems like swarm drones & autonomous intercept boats enhance mission precision, reduce human risk, & improve tactical outcomes. ➤ Intelligence, Surveillance, & Reconnaissance (ISR): AI-based motion detection & target identification systems provide real-time alerts for better situational awareness along borders. ➤ Advanced robotics: Silent Sentry, a 3D-printed AI rail-mounted robot, supports automated perimeter security & intrusion detection. Example: Swarm drones use distributed AI algorithms for dynamic collision avoidance, target identification, & coordinated aerial maneuvers, providing versatility in both offensive & defensive tasks. 2️⃣ Collaboration as the Catalyst for Innovation India’s AI advancements are the result of partnerships between the government, private industries, & research institutions. ➤ Indigenous solutions: 100% indigenously developed systems like the Sapper Scout UGV for mine detection. ➤ Startups and SMEs: Innovative contributions from tech firms and startups have fueled projects like AI-enabled predictive maintenance for naval ships and drones. ➤ Global export potential: Systems like Project Drone Feed Analysis and maritime anomaly detection tools are export-ready, positioning India as a major global defense tech player. 3️⃣ The Data-Driven Case for AI ➤ Efficiency: AI-driven systems exponentially improve surveillance coverage and reduce operational time. For example, the Drone Feed Analysis system decreases mission costs while expanding surveillance areas. ➤ Safety: Predictive AI systems in vehicles and maritime platforms enhance safety by identifying potential risks before failures occur. ➤ Economic impact: AI-powered predictive maintenance for critical assets like naval ships and aircraft maximizes uptime while minimizing costs. Real Impact ➤ Swarm drones: Affordable, scalable, and capable of BVLOS operations, offering precision in combat. ➤ AI-enabled maritime systems: Detect anomalies in vessel traffic, securing trade routes and protecting economic interests. ➤ AI-driven mine detection: Enhances soldier safety while automating high-risk tasks. What does this mean for defense organizations? AI isn’t just modernizing defense; it’s placing it firmly in the global defense innovation market. With bold policies, dedicated budgets, and a growing ecosystem of public and private sector players, this will help lead the next wave of AI-driven defense technologies. But the question remains: How do we ensure these technologies are deployed ethically and responsibly? Agree?

🚨 LLMs Could Describe Complex Internal Processes that Drive Their Decisions. Determinism plus interpretability: that is the real foundation of trustworthy AI. This new paper shows something remarkable: with the right fine-tuning, LLMs can accurately describe the internal weights and processes they use when making complex decisions. Not just outputs, but the actual quantitative preferences driving those outputs. Even more, this “self-interpretability” improves with training and generalizes beyond the tasks it was trained on. Why it matters: - It moves beyond black-box probing or neuron-level reverse engineering. - It suggests that models have privileged access to their own internal processes, and can be trained to report them. - It could open a new path for interpretability, control, and safety—complementing the determinism breakthroughs we saw with Thinking Machines. Caveats: - Explanations may still drift toward plausible narratives rather than ground truth. - The cost of fine-tuning and generalization limits need more evidence. - Self-reports remain a proxy, not direct transparency. Still, this is a step forward. Deterministic outputs are essential—but equally essential is knowing why a model chose what it did. Self-interpretability could be the missing bridge. You can read the full paper here: https://lnkd.in/dY94qq4H #AI #ArtificialIntelligence #GenerativeAI #LLM #LargeLanguageModels #MachineLearning #DeepLearning #AIinBanking #AIinFinance #FinTech #BankingInnovation

Are you struggling to understand basic concepts of Spark? The best way to learn concepts as a beginner is to convert concepts into a storytelling format: Imagine you are running a Pasta Restaurant. 1️⃣ The Big Kitchen = Spark Cluster Your kitchen is huge - cooking stations, ovens, staff. That’s the Spark Cluster - a network of machines working together. -- 2️⃣ You = Driver Program You're the head chef. You write down the recipe plan: "Boil pasta. Add sauce. Deliver to East zone." You coordinate, not cook. -- 3️⃣ Kitchen Staff = Executors The cooks do the work - boiling, mixing, plating, delivering. They’re your executors, each working on a part of the task in parallel. -- 4️⃣ Order Slips = RDDs Each pasta order is copied into the station’s cooking log - not just the final dish, but the exact steps used to make it. If a dish is ruined or lost, the chef doesn’t ask for a new order - they just retrace the original steps and remake it exactly the same. That’s an RDD: Resilient (can recover), Distributed (spread out), Dataset (your raw orders) -- 5️⃣ Recipe Steps = Transformations You jot down steps: ✔️ "Remove pineapple orders" ✔️ "Add cheese to penne" These are lazy No one's cooking yet - you're just planning. That’s Lazy Evaluation. -- 6️⃣ “Go!” = Action You finally say: "Start cooking!" Now all the prep becomes real work - boiling, plating, serving. That’s an action, like .show() or .collect(). -- 7️⃣ Smart Assistant = Catalyst Optimizer Your sous-chef (Catalyst) says: “Why wait to boil water after chopping vegetables? Let’s boil water while chopping to save time.” That’s Catalyst Optimizer - reorders steps, eliminates redundancy, and combines tasks for max efficiency. -- 8️⃣ High-Speed Kitchen Tools = Tungsten Engine Once the plan is ready, your top-tier ovens and machines (Tungsten) jump in. They execute the plan fast, using efficient memory & CPU. Catalyst decides what to do, Tungsten executes it fast. -- 9️⃣ Spill in Kitchen? = Fault Tolerance One batch of pasta falls on the floor. No panic - you know exactly how it was made, so you remake just that. Spark does the same: it tracks all steps (lineage), and recomputes only what failed. -- 🔟 Pasta Counters = Partitions You split orders across sauce, pasta, topping counters. That’s partitioning - parallel processing across stations. -- 1️⃣1️⃣ Rearranging Plates = Shuffle You now need all Alfredo dishes sent to South zone. Plates are picked from multiple stations and regrouped. That’s a shuffle - and it slows you down. Minimize when possible. -- 1️⃣2️⃣ Ingredient Tracker = DataFrames You switch from scribbled orders to a neat chart: “Pasta | Sauce | Topping | Zone” That’s a DataFrame - structured, optimized, and easier to process. -- 1️⃣3️⃣ Pre-cooked Pasta = Caching Alfredo is super popular. So you make a big batch in advance and re-use it all day. That’s caching/persistence - storing results in memory to avoid rework.

AI is no longer just a chatbot. It is becoming part of real battlefield decision making. During the recent Israel Iran conflict, reports suggest that US Central Command used Anthropic’s AI model Claude to assist in intelligence analysis, target evaluation and battle simulations. The AI was not pulling a trigger, but it was processing massive volumes of data to support military decisions where seconds matter. And yet, despite being used in active operations, the US government has now terminated its contract with Anthropic and labeled the company a supply chain risk. Why? Because Anthropic refused to remove certain safety guardrails. The company has taken a clear position that its AI should not be used for mass domestic surveillance or fully autonomous lethal weapons. In other words, AI can assist humans, but it should not replace human judgment in life and death decisions. The Pentagon sees this differently. From a national security perspective, any restriction that limits operational flexibility is a concern. In times of conflict, speed and technological advantage can define outcomes. This is not just a contract dispute. It is a defining moment in the balance of power between governments and AI companies. Who sets the red lines for artificial intelligence? Should private tech firms have the authority to refuse certain military applications? As AI becomes more deeply embedded in defense systems, these questions will shape not only the future of warfare but also the future of governance, democracy and global power structures. We are entering an era where algorithms sit closer to the center of strategic decision making. The debate has only just begun.

Yesterday's Arm announcement is not just a chip story, it is shaping data strategy and AI 💙 What’s being introduced is a compute designed for continuous AI and agentic systems. These are workloads that fundamentally reshape how data needs to flow, persist, and be accessed; they make us, as data people, stop and think! As a data industry practitioner and academic, this is a data and AI infrastructure pivotal moment, and here’s why: 1. From model-centric to system-centric AI This isn’t about accelerating individual models. It’s about enabling systems of agents that continuously reason, act, and adapt. →Think of a customer service platform: not a single chatbot answering queries, but multiple agents handling detection, resolution, escalation, and follow-up, sharing context in real time. This requires persistent memory and coordinated data access, not isolated model calls. 2. Always-on AI changes the data lifecycle We are moving from episodic workloads to continuous execution. Data pipelines can no longer be batch or even event-driven; they must become stateful, streaming, and context-aware by design. →Think fraud detection: instead of flagging anomalies hours later, systems now evaluate transactions as they happen, using live behavioural context to block risk instantly. 3. Data gravity becomes the architecture driver These workloads don’t tolerate latency. Compute must move closer to where data is generated across edge and cloud. → Consider smart manufacturing: AI models running on factory floors analyse sensor data in real time to prevent defects. Sending everything to the cloud is simply too slow and costly. 4. We are entering the era of AI operating on data continuously This is infrastructure built not just for humans querying models, but for AI systems interacting with data in real time, at scale. → Think of supply chain optimisation: AI agents continuously adjusting inventory, routing, and demand forecasts not based on static reports, but on live signals across the network. And that leads to a more important question: 👉 Is your data strategy designed for static models… Or for autonomous systems that will continuously operate on your data? Have I got you excited about the announcement as I am? This is pivotal for us working in the data and AI space! #AI #DataStrategy #AgenticAI #EmergingTech #DataArchitecture #DigitalTransformation