Anupam Singh

We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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Istio Service Mesh: great to see Ambient moving up to Beta, delta xDS becoming the default, and api-gateway becoming a first class citizen in Istio. https://lnkd.in/gYPmnTuj

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The afternoon Keynote @ Stripe Sessions was Fireside Chat between Patrick Collison (CEO of Stripe) and Jensen Huang (CEO of NVIDIA). Patrick was an excellent moderator — asked Jensen really probing, nuanced questions that Jensen answered with his usual sharp wit and unique (and impressively well rationalized) perspective. A couple of quotes among many that stuck with me: - “Current CPU installed market is $1 trillion of hardware, all of which will be upgraded to GPUs over the next 5 years. And I would estimate about another $1 trillion will be spent on top of that during the same time period as companies try to do more with AI. Add those together — and I think at least $2 trillion will be spent on GPUs in the next 5 years.” - “I like to go after Zero-Billion Dollar Markets — nonexistent markets that I envision & can rationalize well, but will be very hard, expensive & time-consuming to achieve.”

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Interested to see OpenAI acquire Rockset today. Rockset is a realtime search and analytics database built by the team who funded Hadoop Distributed File System (HDFS) at Yahoo and created RocksDB, the open source embedded storage engine used by CockroachDB. I imagine this will form the basis of OpenAI's RAG at scale solution for enterprise data. https://lnkd.in/eDSG2AjX

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Hello FinOpsfolk - "cost allocation" is up in the top left corner of the framework graphic, but I don't recall ever seeing much written on The How of it. This post on finops tooling begins a series on how we handle cost allocation here at my employer. We'll move from basic to advanced allocation and will cover topics like: - tagging resources with your company's technical vocabulary - using FOCUS to unify multi-cloud billing - container cost allocation - shared cost allocation - tying all this over to the rest of the business and more... Much as there were no shortcuts in discovering this material, there will be no shortcuts taken in the writing about it either. This is 100% human-generated content - thanks for reading. https://lnkd.in/egySp3Vb #finops

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When greentech unicorn Enpal needed to automate its solar panels sales process, it turned to custom AI software specialists dida Machine Learning. Taking an abstract mathematical approach to complex ML models, dida used Google Cloud to build an ML solution that cut Enpal’s process time by 87%, saving the company time and reducing costs while improving its customer experience. Learn more on the blog ⬇️

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In the "I Know You Think You Know What I Said But That Wasn't What I Said" category comes news from Intuit that it's laying off 1,800 people. What caught my eye were comments from the company's CEO, published on the firm's blog. After a discussion of the need for the layoffs, in a section titled "Taking care of our people" came this statement: "It is never easy to say goodbye to our colleagues and friends and we are committed to treating everyone who has been impacted by these changes with the compassion and respect they deserve." Pretty standard for the 20th century, no? But that was followed by some bullet points including this one: "We’ve significantly raised the bar on our expectations of employee performance, resulting in approximately 1,050 employees leaving the company who are not meeting expectations and who we believe will be more successful outside of Intuit." HUH? So much for "treating everyone...with the compassion and respect they deserve." Truly feel sorry for a lot of the people impacted. I'm sure that, out of the 1,800 people impacted, there were some "underperformers." But calling them out in public like that? Not only was this the wrong thing to do, it actually demonstrates the shortcomings of the executive team for a # of reasons: 1️⃣ Did no one (other than the CEO) have any sense to proof read the blog post before it was published and maybe suggest a lighter tone regarding the people who are "not meeting expectations"? 2️⃣ More importantly, check out the phrase "we’ve significantly raised the bar on our expectations of employee performance." This implies that management has been operating with sub-par expectations of performance. So whose fault is it if 10% of the people in the organization are suddenly found to be "not meeting expectations"? Answer: Management's. Then, a section titled "Moving forward together" started with this: "By 9:00 AM PT today, all employees impacted by these changes will receive a calendar invitation from their manager titled “Leaving Intuit Discussion”. Those who do not receive an Outlook invitation by 9:00 AM PT can assume they are not impacted and we ask that you support your colleagues as they process this news." Never mind that this isn't exactly "moving forward together." Can you imagine the # of emails and phone calls that this must have kicked off with everyone at Intuit calling and emailing their colleagues to find if they were impacted, and if not, who was? I'm no HR expert, but there had to be a better way to announce this.

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Only last week we celebrated Karpenter graduating to beta with AWS. Now I wrote a post on how to maximize your Karpenter ROI with smart pod right-sizing and continuous optimization. TLDR: - If you're not using Karpenter - now is the time to migrate. - Karpenter can be 30-50% more efficient when running on optimized clusters https://lnkd.in/dBUXjd4E

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Performance is key! As we cannot share the detailed work with (former) customers of Rockset (acquired by OpenAI), we decided to implement the Rockbench benchmark to check how streaming ingest between CrateDB and Rockset (acquired by OpenAI) compares. The results are very insightful. We see: ✅ CrateDB achieves 6-9x lower latencies than Rockset for streaming ingest. ✅ CrateDB outperforms Rockset on the same hardware while saving about 20% costs. ✅ When volumes increase, the latency increased linearly in Rockset, while remaining mostly flat in CrateDB. As a plus: 1️⃣ Achieve full high availability due to the distributed nature of CrateDB (benchmark results are achieved including replication in CrateDB) 2️⃣ Achieve higher throughput on the same amount of CPUs as Rockset (at 50% of memory needs) 3️⃣ Full open-source solution that prevents you from market shifts On side note: If you are thinking to switch to Elastic/OpenSearch, this benchmark has been created to compare Rockset vs. Elastic, so CrateDB is also faster than Elastic for streaming ingest cases! Read the full comparison here: https://lnkd.in/d_nEMDkx

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🧠 From the intelligence of Andi to the curiosity of AskCodi, 🔮 From the magic of AWS Q to the insights of Bing AI, 💻 From the innovation of Codeium to the conversation with ChatGPT, 🔥 From the collaboration with Cody to the assistance of GitHub Copilot, 🚀 From the intelligence of Google Gemini to the lightning-fast Lightning AI, 🌈 From the creativity of Metaphor to the insightfulness of Neeva AI, 💡 From the coding prowess of OpenAI Codex to the mystery of Perplexity AI, 🔍 From the discovery with Phind to the enlightenment with Quora Poe, 🌟 From the assistance of Replit Ghostwriter to the knowledge of WolframAlpha, 📊 From the security of Snyk Code to the suggestions of Tabnine, 🌟 From the intelligence of Visual Studio Intellicode to the whispers of Whispr AI, 🔎 From the personalization of You to the assistance of my very own... We're asking about it all in the Developer Survey - share what you've worked with and what you want to work with in the future. https://lnkd.in/gdz3ZxKB (post content assisted by AI - which is perfectly meta)

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Anthropic just launched Claude 3.5 Sonnet, which beats both OpenAI GPT-4o and Anthropic’s previous paid model Claude 3 Opus on most benchmarks. Claude 3.5 Sonnet can execute and debug code, and has improved image understanding capabilities, including text transcription. A new feature called “Artifacts” enables users to view generated code, documents and websites in a separate window next to the chat. Claude 3.5 Sonnet is available for free to all users, but paid users get higher usage caps. Did Anthropic just take the lead back from OpenAI? It seems that ChatGPT’s Code Interpreter tool, which used to be one its main advantages, has finally met its match, but ChatGPT still has the advantages of real-time Internet access and custom GPTs which Claude lacks… What do you think? https://lnkd.in/d_hk86cf

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Top 5 Strategies to Reduce Latency 10 years ago, Amazon found that every 100ms of latency cost them 1% in sales. That’s a staggering $5.7 billion in today’s terms. For high-scale user-facing systems, high latency is a big loss of revenue. Here are the top strategies to reduce latency: Database Indexing Caching Load Balancing Content Delivery Network Async Processing Data Compression What other strategies to reduce latency have you seen? blog.bytebytego.com Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 #backend #fullStack #developer #Csharp #github #EFCore #dotnet #dotnetCore #programmer

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The past two years at StarTree, working with the Apache Pinot community, have been fantastic. I’ve been able to get to know some incredibly talented people, and it’s validated my perspective that the advent of event-driven architecture is real and pressing, and even more interesting than I realized. Looking back, I’d never spent that much time focusing on analytics and the huge set of tools and concerns that attend it. It’s a critical ecosystem with a history deeper than digital computing itself, and I anticipate a lot of fruitful work with the Pinot community in the months and years to come. That being said, it’s ironic that my time working with this community has crystallized an important idea I’d been developing since before I joined it. The disruption introduced into our world by the perfect storm of the cloud, microservices, and ubiquitous Kafka has created an entirely new approach to building business software. It’s new, uncharted territory to be explored—and I want to help map out as much of it as possible, together with the hundreds and thousands developers who are doing the work of building applications in this new world. That’s why I’ve decided to transition from StarTree, and return to Confluent as VP of Developer Relations. The most valuable thing we can do as developers is write differentiated code that provides meaningful value to our customers or users. That code sits atop a huge stack of what is, with respect to their businesses, undifferentiated software—various kinds of frameworks and infrastructure components—that solves hard problems that application developers shouldn’t have to spend time on. They shouldn’t build that software, but they need to know how to navigate the terrain and make choices in the space they can trust. The problem is that the event-driven revolution has thrown a somewhat stable stack into relative disarray, so some of us are lured into creating little bits of data infrastructure in that unknown area between our code and Kafka. Those of us who are able to avoid that temptation still aren’t totally clear on what all that terrain looks like, and what infrastructure should even live there. History suggests this could take another five to ten years to stabilize. It will require the input of many communities, vendors, and a world full of developers. In my opinion, Confluent is the company that is best positioned to significantly move the needle on this problem, and that’s why I’m excited to (re)join them. One company can’t do it all, but their vision of the Data Streaming Platform is exactly what I’m talking about here. The opportunity to lead a world-class developer relations team at the company which has pioneered the journey so far, and is building the most comprehensive vision for its next stage, is a tremendous one. I’m delighted to be back, and eager to do my part to help chart our course to the future. It is, as I am wont to say, a wonderful time to be alive.

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Sonnet 3.5 has been makingn waves. Personally I have found that perhaps it lacks breadth of knowledge ( not able to comprehensively answer many questions as well as openai) but its definitely faster , mostly gives better code and provides projects and artifacts features which are reason enough for switching to it for techies doing using it for programming tasks.

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I want to highlight a new feature in our latest release also included in our free Community edition ! Offset-Based Retrieval in Kamiwaza's Full Gen AI Stack: Seamless RAG Flow at PB+ Scale! A significant advancement in our Full Gen AI stack at Kamiwaza: out-of-the-box offset-based retrieval support, tailored for Enterprise-scale Retrieval-Augmented Generation (RAG) flow. #### What Does This Mean? - **Seamless Pagination:** Offset-based retrieval enables effortless pagination through massive datasets. This feature lets your enterprise handle enormous data scales, efficiently delivering specific subsets of data based on your application's requirements. - **Optimized Performance:** Our implementation ensures that even with petabyte-plus data volumes, your queries are streamlined for speed. Retrieve exactly what you need, without overloading your infrastructure or consuming unnecessary resources. - **RAG Flow Ready:** Retrieval-Augmented Generation (RAG) is critical for enterprises leveraging AI to augment their workflows. Our stack’s native offset-based retrieval ensures that RAG systems can navigate vast datasets, retrieving relevant information with unparalleled efficiency. #### Why Offset-Based Retrieval? Handling large-scale data is challenging, particularly when your AI systems require real-time responses. Offset-based retrieval allows you to: - **Navigate Efficiently:** Skip irrelevant data and jump straight to the relevant subset for your application. - **Scale Seamlessly:** Manage and manipulate data even at petabyte-plus scale, keeping your AI applications responsive and efficient. - **Maintain Consistency:** For use cases like search, recommendations, and dynamic data exploration, consistency is key. Offset-based retrieval helps manage this seamlessly. #### What to Expect? With Kamiwaza’s Full Gen AI stack, enterprises can: - **Empower AI Workflows:** Quickly and efficiently support retrieval-augmented workflows, ensuring your AI models access the most relevant data. - **Enhance Decision Making:** Our optimized data retrieval enables faster, more informed decisions driven by the power of your data. - **Streamline Operations:** Offset-based retrieval reduces the complexity of managing vast data landscapes, allowing your teams to focus on deriving insights. We believe that this feature will significantly enhance the performance of our AI stack, helping enterprises unlock new levels of productivity and data-driven insights. #GenAI #1TrillionInference #KamiwazAI #EnterpriseAI Check out https://lnkd.in/gAwbGdGH for the latest updates!

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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Google Cloud Vertex AI is rolling out multiple updates to make AI accessible for every enterprise. Up to 85% lower input costs and 80% lower output costs, and 100+ new languages coming to Gemini 1.5. #GoogleCloud #VertexAI #Gemini

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In the next few weeks, Snowflake and Databricks will host their annual summits - back-to-back - in San Francisco. The burden on the larger tech community is real: relationships will be tested. Dogs will go unwalked. Extended childcare will be arranged. Strict diets will be neglected, exercise foregone. Why are we doing this to ourselves? Because in 2024, these ecosystems are THE most important growth drivers. These data platforms largely dictate the fortunes of anyone in their orbit. No one is more trusted than the data platform. No technology is more central to more mission-critical workloads. As a result, no one is more influential in driving technology decisions. A primary data platform implies a primary ecosystem. And a primary ecosystem’s primary event is a big deal. At these events, seismic announcements will be made. Feature announcements that reshape reality for partners. Joint announcements with partners that drive critical awareness and pipeline. In parallel, decision makers will be exposed to new apps. New data providers. New changes in the SI landscape. New preferred SaaS tools for each workload (e.g. analytics, ETL, machine learning, dev ops, martech). For partners, these events are a pivotal time. They’ve spent the year building up the two critical aspects of ecosystem credibility: quality and volume of work. Now is the time to reap what has been sewn, and to plant for the forthcoming season. Year over year, outcomes from these events can and should improve. For those pursuing ecosystem-led growth - and, in 2024, it should be EVERYONE - navigating these events is an instructive microcosm for broader ecosystem participation: look for gaps within the platform’s ecosystem. Win with initial projects. Continue to establish yourself in the ecosystem with more marquis projects. Promote successes effectively within the ecosystem. And, importantly, don't bite off more than you can chew. Be disciplined and measured. Credibility within the ecosystem is built over years, and can be lost in a day. Personally, I’m very much looking forward to seeing all of you at both of them. Maybe even at the dingy hotel gym this year. I swear I’m gonna make it this time… #snowflake #databricks #ecosystem #data

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