Cloud Computing Solutions

One of the most important laws of frugal architecture is that you can’t optimize what you can’t measure. I learned this long before cloud computing. Growing up in Amsterdam during the energy crisis of the 1970s, we had things like car-free Sundays and rationed energy, but the detail that always stuck with me was closer to home. Households with their energy meter on the main floor of their homes used significantly less energy than those with it hidden in the basement. The same style of house, in the same city, yet dramatically different behaviour. About as clear of a signal as you can get that seeing data changes what you do with it. For years, in the absence of better sustainability metrics, usage (or consumption) was the best proxy we had. The meter was in the basement. With the AWS Sustainability Console, we bring the meter to your “living room”. It gives your builders direct access to Scope 1, 2, and 3 emissions data, broken down by service and Region, exportable via API, without ever touching sensitive cost and billing data. The right data, to the right people, through the right door. When carbon emission becomes just another metric in your observability stack sitting next to latency, cost, and error rates, it stops being a compliance exercise and starts becoming an architectural discipline. The world we are building in the cloud is the world we are leaving to our children. Measure it like it matters. Read more here: https://lnkd.in/efFjU7hG

📌Turning Waste into Warmth: A Smarter Way Forward 🔁🔥 Finland is transforming how cities use energy by integrating sustainability directly into digital infrastructure. New underground data centers in Helsinki are designed not only to host servers but also to recycle the immense heat they generate. Instead of venting this waste energy, it’s captured and redirected into district heating systems that warm nearby homes and buildings. This closed-loop approach allows the same energy that powers cloud computing to heat thousands of apartments, reducing reliance on fossil fuels and cutting urban carbon emissions dramatically. Data centers, once known for their high energy consumption, are becoming key players in renewable urban ecosystems. This is the kind of circular solution modern facilities must aspire to. By integrating technology, engineering, and smart planning, even high-energy systems like data centres can become contributors to a greener city. For facilities and estates professionals, the message is clear: Sustainability isn’t always about new resources — it’s about using what we already have, better. The project underscores Finland’s leadership in green innovation — turning what was once environmental waste into community benefit. As cities worldwide search for climate solutions, this model shows how technology and sustainability can work hand in hand to reshape the future of energy. A powerful reminder of what’s possible when we rethink infrastructure with efficiency and environmental responsibility at the core. Sources: ✍️TechTimes #GreenEnergy #FinlandInnovation #SustainableCities #DataCenters #CleanTechnology #Infrastructure #Environmental #Technology

Yesterday, Reuters reported that OpenAI finalized a cloud deal with Google in May. This might look like routine tech news. It is not. This is a strategic inflection point in the AI infrastructure wars. OpenAI, whose ChatGPT threatens the core of Google Search, is now paying Google billions of dollars to power its growth. This was not a partnership of choice. It was a partnership of necessity. Since ChatGPT launched in late 2022, OpenAI has struggled to meet soaring demand for computing power. Training and inference workloads have outpaced what Microsoft’s Azure alone can support. OpenAI had to expand. Google Cloud was the solution. For OpenAI, the deal reduces its dependency on Microsoft. For Google, it is a calculated win. Google Cloud generated $43 billion in revenue last year, about 12 percent of Alphabet’s total. By serving a direct competitor, Google is positioning its cloud business as a neutral, high-performance platform for AI at scale. The market responded. Alphabet shares rose 2.1 percent on the news. Microsoft fell 0.6 percent. There are only a handful of true hyperscalers in the U.S. AWS, Azure, and GCP dominate, with Oracle and IBM trailing behind. The appetite for compute is growing faster than any one company can satisfy. In this new phase of the AI era, exclusivity is a luxury no one can afford. Collaboration across competitive lines is inevitable. -s

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

In true Silicon Valley fashion, the AI arms race is getting down to the silicon itself.🤺 The Big 3 hyperscalers, Amazon, Microsoft, and Google—traditionally NVIDIA’s biggest customers—are encroaching on its core turf by developing their own AI chips. Meanwhile, NVIDIA, the juggernaut of GPUs, is pushing into hyperscaler territory with DGX Cloud, offering AI infrastructure that could, in theory, make it less reliant on Big Tech clouds. Why does this matter? Because the silicon layer is a battleground for billions. 💸 Hyperscalers are tired of footing NVIDIA's massive GPU bill, so they’re investing big in in-house silicon to cut costs and assert control. Amazon’s Inferentia and Trainium chips, Google’s TPUs, and Microsoft’s Maia project are all about building tech stacks with minimal dependency on outside hardware. The goal? Price control and performance tailored to hyperscaler needs. For NVIDIA, this is about strategic survival. Its business model relies on selling chips that empower the same hyperscalers who are now racing to break free. DGX Cloud and partnerships with Oracle, Google, and Microsoft (ironically) are NVIDIA’s way of expanding beyond hardware sales into high-margin, AI-driven cloud services. NVIDIA is doubling down on services, building out a powerful software ecosystem, and offering a soup-to-nuts solution for enterprises wanting AI access without the infrastructure burden. If hyperscalers get their chips right, NVIDIA's dominance could be challenged. But if NVIDIA’s DGX Cloud gains traction, it’s a warning shot that it can play in hyperscaler territory too—and may lure AI workloads directly onto its infrastructure. The stakes have never been higher, so let the chips fall where they may.🌐

Hyperscalers are fighting the cloud wars. Startups are fighting for compute. Our latest business relationship data shows GCP taking 38% of new AI startup relationships, AWS with 30%, Azure with 8%, and… 25% of AI startups taking a multi-cloud approach. In most cases, this isn't a sophisticated infrastructure strategy. It's a response to a fundamental supply constraint: there simply aren't enough GPUs to meet demand. Hyperscaler strategies: Google Cloud is making an aggressive play for startups by embedding Gemini into its solutions, creating native AI builders on GCP. Their recent deal with Cipher signals they're serious about expanding compute capacity to support this strategy. If you're trying to capture more of the value chain by betting on startups building with your LLM, you need to invest in the infrastructure to back it. AWS has scale and the largest startup footprint, but without a proprietary LLM driving demand, they're competing primarily on compute access and availability. When GPU supply is tight everywhere, being “Switzerland” has its advantages. Microsoft largely sits out the early-stage startup battle. With existing enterprise distribution and key partnerships with OpenAI and now Anthropic, they can focus on inference and deployment rather than competing for training workloads. Many early-stage AI startups are locked to a single provider because of startup programs, credit packages, and early partnerships. But, as compute costs scale and availability remains constrained, that 25% multi-cloud number is likely to grow. Companies are increasingly willing to add operational complexity if it means access to the compute they need. Compute remains the bottleneck. Navigating the supply-demand imbalance – through infrastructure investment, partnerships, and strategic positioning – will determine the next 12, 18, 24 months of growth for both startups and cloud providers.

One thing is clear in Accenture’s latest report on building an AI‑ready cloud foundation; organizations aren’t just modernizing their tech stacks; they’re redefining how they create value.     What we’re seeing now is a shift from cloud as an efficiency play to cloud as the backbone of continuous reinvention. AI is accelerating that shift, but AI can only deliver its full potential when the underlying architecture is ready for it.      The companies pulling ahead are the ones treating cloud, data, and AI as one integrated system, not separate investments. They’re simplifying core operations, creating flexible digital foundations, and empowering their people with the skills and tools to move with speed and confidence.     This isn’t about chasing every new technology. It’s about building the resilience and adaptability to keep reinventing, again and again as the environment changes. At its core, an AI‑ready cloud foundation is about preparing the enterprise for what’s next, not just optimizing for today. The leaders who understand this will set the pace for their industries.    https://lnkd.in/gvrX4rqp    Andy Tay, Lan Guan, Jason Dess Jefferson Wang, Shalabh Kumar Singh 

Excited to share insights from our latest Next at Chase blog post by Praveen Tandra and Sudhir Rao where we dive into the transformative journey of migrating our data ecosystem from Hadoop to AWS. This shift is a game-changer for our data strategy, addressing tech debt and setting the stage for future innovation. Key insights from our journey: • Migration Milestone: We're moving our Data Lake from on-premises Hadoop to AWS, embracing a flexible and future-proof cloud solution. • Tackling Tech Debt: Addressing challenges like data duplication, metadata drift, and platform incompatibilities to streamline our data processes. • Adopting Open Standards: Transitioning to Apache Parquet for efficient, open-format data storage, enhancing interoperability and performance. • Project Metafix: A collaborative effort to reconcile and adapt decades-old metadata, ensuring seamless migration and data integrity. • Lineage 2.0: Mapping data movement end-to-end, providing a clear view of data assets across legacy and target platforms. None of this may be groundbreaking, but for a 225-year-old company, this migration is more than just a tech upgrade—it's a strategic leap forward in how we manage and utilize petabytes of data at Chase. Stay tuned for part two, as we continue to share our journey and the innovations driving our data transformation. So proud of all of our teams driving this forward, boom! Question for You: How do you see cloud migration impacting the future of data management? Share your thoughts below! #DataTransformation #Innovation

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

Waaaaay back when I started my career, a personal organizer was my only option for keeping my schedule straight. I remember handwriting everything from business meetings to grocery lists - and if that little notebook was ever misplaced, I would have been lost! I've been through the hieroglyphics of the Palm Pilot to the small keyboard of the Blackberry. These days, tech makes scheduling and connecting with people far easier (and with far less risk of me losing valuable information). But none of this would be possible without data centers - they’re the backbone of our digital lives. We rely on them constantly, without even realizing it (for instance, you’re using a data center right now to read this!) These are some of the ordinary tasks I depend on them for: 📱 Video chatting with my daughter, who’s away at college 🏀 Confirming the time of my son’s basketball game ☔ Double checking the weather 🎁 Using Amazon's Rufus to buy just the right gift for a family member 🎟️ Downloading tickets for a game at Climate Pledge Arena Every single one of those moments - the connections, the peace of mind, the celebrations - wouldn’t happen without a data center. And we’re committed to using sustainable practices and systems in the data centers we operate. Some examples of what that looks like:   ▶️ Improving energy efficiency. Just like car manufacturers can design engines to get the most out of every gallon of gasoline, we design our data centers to get the most out of every unit of electricity - minimizing waste and maximizing computing power. That’s measured through Power Usage Effectiveness (PUE). A “perfect score” PUE is 1.0. Amazon Web Services (AWS) data centers achieved a global of 1.15 in 2024, with our best-performing site reaching 1.04! Innovating in water efficiency. We lead the industry in water efficiency, and are 53% of the way to our goal of returning more water to communities than we consume across our data center operations. ▶️ Thoughtful data center construction. We're using lower-carbon concrete and steel in construction, reducing embodied carbon by up to 35%. ▶️ Carbon-free energy. We’ve invested in over 600 renewable energy projects globally. ▶️ Reuse and recycling. We're extending equipment lifespans and recovering materials, with over 99% of decommissioned racks are diverted from landfills. Data centers help make modern life possible, and I look forward to the new year bringing even more new innovations. You can learn about our progress here: https://lnkd.in/gkynx6aY How do you use a data center in daily life? Drop it in the comments!