Ellen Wong

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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Tips a hat to SourseAI - “A narrow ICP definition and deep customer empathy” Tanya Hyams-Young & Paul Jefferies are seeing the fly wheel effect of vertical SaaS. Note to Aussie startups that take this route - think hard about the TAM, here and abroad. You’ll probably conclude you’re going to need to move to “the rest of the world” pretty quickly. See you in a week EMEA! #verticalsaas #ai #decisionintelligence #telecoms #mvno

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The Site Reliability Engineering: How Google Runs Production System book is arguably one of the most prevalent and well-known SRE resources out there, but there are many other great options for folks looking to expand on their learning. We recently spoke to Google's Reliability Advocate, Steve McGhee, in our Humans of Reliability interview series. In addition to his interesting anecdotes on the early days of SRE at Google, and his journey to becoming a Reliability Advocate, he also shared a handful of his favorite SRE resources, which we compiled here into a list: https://lnkd.in/gpUVhyRa

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This is an interesting read. The changes that the broader AI ecosystem will bring to existing business practices and verticals will be a transformational journey for many industries in the years to come. Some of the capabilities highlighted in this article will eventually become mainstream. A few things that the entire tech industry has to work on to make all of the concepts outlined as mainstream are: Data Quality and Governance - Garbage in equals garbage out. There is lot of business "junk" that can lead to model hallucinations, inaccuracies, etc. that require various re-learning techniques and feedback loops to be implemented today. Without improving data quality and governance practices through automation, intelligent systems, you will have low quality outcomes which eventually won't augment your workforce. This vertical becomes inherently more complex with voice and video getting added to text-based data. New business models - A lot of products in the market need to rethink how they optimize for the outcomes and which outcome they want to charge their customers for. Most customers don't want to sign up for an outcome based model which charges for activities which are in the business workflow without achieving the final outcome. And sometimes the final outcome like closing a sale is too far in the cycle especially in the B2B space that it's not sufficient to provide a robust cloud-based solution. Cost-of-goods-sold - AI models are expensive to run and operationalize and over the years, this will evolve and R&D across the industry will help drive down the cost. This would need buyers to re-think how they buy and plan for costs, forecasting and planning and sellers to re-think their business models and monetization strategies. Multi-modal data management - For your models to be efficient from a business context, you need to be able to process all types of data that is generated: data stored in data management systems, voice-related data from calls, presentations and video-related data from trainings, knowledge bases, news, etc. Managing multiple data management systems was complex with the data sets different industries like banking, financial, energy, healthcare, auto, etc. generate. It will be inherently harder to manage the additional types of data in a cost effective manner. We will see a boom of additional cloud computing power and a different level of computing efficiency (performance and price-per-performance) that would have to evolve much more than what we have today in the market. These are just a few of the innovation areas that will define how AI shapes up and becomes mainstream. This is a generational transformation that we are living through and a lot of innovation will have to happen in the above areas in the coming years. #ai #industrytranformation #data

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Have you got your tickets for SREday San Francisco 2024? Join me on June 22nd for a day full of Site Reliability, DevOps and Cloud! Tools and Best Practices from Running Unikernels in Prod for a Few Years abstract: Unikernels have been running in production for years now, and that experience has produced a set of new tools, best practices, and workflows. In this talk, come hear about how and why your peers have been pushing unikernels to production and how new methods have been established. Whether it's cloud-specific monitoring, accessing GPU resources for ML workloads, or applying security policies that Linux workloads can't, we'll show you how companies, large and small, have been heavily investing in unikernel deployment. Also, you can grab a ticket with 20% off with my code IAN20: https://lnkd.in/gagEZDFc #SREday #SiteReliability #DevOps #Cloud SREday

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Data scientists are not trained to understand debits versus credits, CAPEX versus OPEX, accruals, revenue recognition rules, etc. FP&A professionals are not (initially) trained as data scientists. If they are, as Kurt Shintaffer states, they are torching money and time building it themselves. And what happens when the few individuals at your company with this skill set move on? ‼️🤷‍♀️ Check out what Sandeep Madduri and Naresh Nemali built for finance professionals. Precanto is what this generation of finance professionals need. Monetize the data … Transformation starts now! 🚀 (Disclaimer: This bold post is coming to you from recovery room while on pain meds. All is good and colon repaired. Please eat your fiber, folks, and listen to your body! But in all seriousness, Precanto is the next wave of innovation for CFOs and FP&A teams.)

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A successful systems design and delivery strategy begins by assuming that change is likely the only constant. Be adaptable, or fail.

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Introducing rindexer, an open source, no-code blazing fast EVM indexing tool built in rust 🦀. rindexer supports any EVM chain out of the box. rindexer is also a framework for building more complex indexers using rust. https://lnkd.in/e8PAs87n Docs - rindexer.xyz

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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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After Intuit's #CEO recently announced large layoffs, it was revealed that #AI will be used to replace many of them. That may be a first. From ERE Intuit’s CEO goes all-in on #AI after announcing layoffs. Intuit’s CEO, Sazan Goodarzi, sent a letter to employees announcing layoffs, which has gone viral and is facing sharp criticism on social media platforms. Specifically, his statement about employees "not meeting expectations" has sparked negative reactions everywhere. This development is likely to significantly impact Intuit’s #reputation and #recruitment efforts. The company framed the layoffs as part of an AI-focused reorganization plan, stating that it expects to hire at least 1,800 new employees in fiscal 2025 to support the integration of more AI into its products and services. A recent article in Inc. described Goodarzi's memo as one of the worst ever seen and pointed out several issues with the #communication, especially its insensitive tone and lack of empathy towards the affected employees. However, Some affected employees have shared their experiences on social media, highlighting their accomplishments and long tenure with the company, which contradicts the "underperformer" label.

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Interesting article from Bloomberg about life at Google's X in a time of layoffs and cost reduction at https://lnkd.in/gmw3GG7C. X is a component of Alphabet (Google's parent company) that incubates new ideas. They have been responsible for some great successes - getting Waymo and a lot of Google's AI work started - but they've also had projects that didn't survive - like Google's attempt to provide Internet access via weather balloons. When you work at a company that has hundreds of billions of dollars in revenues, it's hard to get a lot of interest in an entirely new business idea that brings in hundreds of millions. If you have a project that improves ads revenue by that amount, or decreases capital expenditures, you can do it, but if you want to, say, build wearable tech, it becomes hard to attract the attention of marketing or product. So, new ideas often die on the vine. For the most part, this is good and proper. Companies may have the financial wherewithal to branch out, but more often than not, they consider it a distraction. And they are probably right - it's hard to argue that Meta was right to divert such a huge chunk of their resources to chase after the metaverse. They've (rightfully) pulled back, and are more focused on their core business again. On the flip side, if you are in the tech sector, and you don't innovate, you are likely to get left behind at some point. So, you need to make and manage these investments somehow. When I joined Google, the CEO had a 70-20-10 rule - 70% of effort went to core businesses, 20% to broader efforts to improve Google, and 10% to stuff that's completely blue-sky. This is probably not completely right, but it is also probably not completely wrong. X is the division of Alphabet that is *specifically* designed to bring blue-sky products to life. Unsurprisingly, they've had to pivot and rethink their strategy many, many times. The article interviews a number of people who have worked at X over the years; it's well worth a perusal, for folks who are interested in the story.

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Tradescoop is built on a headless architecture. Here’s the client REST API v0.1.0: tradescoop.github.io . You can interact with Tradescoop data via the api. Some endpoints require a bearer token you can obtain when you signup.

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🚀 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐀𝐈 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 𝐓𝐡𝐞 𝐂𝐚𝐬𝐞 𝐟𝐨𝐫 𝐒𝐦𝐚𝐥𝐥, 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞 𝐎𝐒𝐒 𝐌𝐨𝐝𝐞𝐥𝐬 🚀 In the rapidly evolving landscape of artificial intelligence, aligning AI solutions with specific business needs is critical for maximising efficiency and effectiveness. Both large-scale SaaS and smaller OSS models offer compelling benefits, but it's crucial to recognise that one size does not fit all. Common ground between Avi Chawla and Jon Cooke is that both approaches emphasise the importance of strategic alignment with business objectives. Whether you're a large enterprise or a startup, the primary goal remains the same: integrating AI in a way that supports and enhances your operational goals. The decision should always be driven by what best fits the company's specific requirements, usage patterns, and long-term strategic ambitions. But for many organisations, particularly those with unique, specialised needs or limited budgets, small, composable OSS models offer a more practical solution. Here's why: 💰 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀: Smaller OSS models can significantly reduce upfront costs and ongoing expenses. They allow businesses to avoid the hefty fees often associated with large-scale SaaS models, making them accessible and sustainable even for startups. 🔧 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻: These models provide the agility needed to adapt quickly to changing business needs. They can be tailored specifically to the tasks at hand, avoiding the one-size-fits-all approach and improving performance where it counts. 🔒 𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: With OSS models, companies maintain control over their data, a critical consideration in industries where confidentiality and data security are paramount. This level of control is harder to achieve with third-party managed SaaS solutions. 🌱 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Operating smaller models generally requires less computational power, aligning with sustainability goals by reducing the energy footprint compared to large-scale models. While large SaaS models are undoubtedly powerful and beneficial for certain high-volume, high-complexity scenarios, small, composable OSS models are uniquely suited to organisations that value flexibility, cost efficiency, and data sovereignty. They allow for a more tailored approach that can dynamically adapt to an organization’s specific needs without the extensive resource commitment of large models. I'm looking forward to the next Data Product Workshop webinar on Thursday 25 April at 5pm UK when Jon Cooke of Dataception Ltd is presenting "𝗖𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗹𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: 𝙒𝙝�� 𝙮𝙤𝙪 𝙙𝙤𝙣'𝙩 𝙣𝙚𝙚𝙙 𝟭𝟬𝟬𝙠 𝙂𝙋𝙐𝙨" and will be expanding these four points and explaining even more! 🧐 ᴇᴠᴇɴᴛ https://lnkd.in/eAZmh5Hy #AI #LLM #LLMs #GenAI #GenerativeAI #OSS

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For a complete solution to realtime voice transcription and translation. Explore how to host the Whisper model with streaming mode on Amazon EKS. Check out this blog by Darren Lin, Frank Fan, Bonnie Ng, Shawn Zhang, and Eunice Tsao. The post provides a detailed guide on leveraging Ray Serve to create a scalable, low-latency architecture for real-time audio transcription. This setup ensures efficient model serving with auto-scaling capabilities using Karpenter on Amazon EKS. Dive into the technicalities and streamline your Automatic Speech Recognition applications today! Read more: https://lnkd.in/ghHGUG_P #AWS #EKS #RayServe #MachineLearning #ASR #Containers

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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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WirelessCar Revs Up Performance with AWS Lambda SnapStart Discover how WirelessCar, a leader in connected car services, achieved a 10x improvement in startup performance using AWS Lambda SnapStart for Java. This innovation significantly enhances the user experience for millions of drivers worldwide. Reduced initialization duration by 90% for a faster, more seamless experience. Implemented seamlessly with virtually no downtime. Explore more on the AWS official site. And if you want to find out more about cloud cost optimization visit https://umbrelly.cloud/!

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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Unapologetically Technical's newest episode is now live! In this episode of Unapologetically Technical, I interview Hubert Dulay the author of Streaming Data Mesh and Developer Advocate at StarTree. We talked about his early experience with web backends like CORBA and SOAP and how those prepared him for data work. He shares his advice for those with web development skills to transition into data and what it's like for a person leaving a company after a long tenure there. We discuss his time at Cloudera and Confluent and how the data industry has changed over time. We talk about his recent experience with Flink and StarTree. We go in-depth into streaming data mesh, covering what it is and how to create real-time data products. We discuss the ways teams can create joined or enriched data products across domains. We round out the discussion with some tips on how to get data mesh adoption. Watch the full episode here:

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"Females can act like glue within a team, contributing to the overall success the same as men, while injecting a different perspective." - Jian Cheng, Senior Full Stack Developer at Edge Signal. We had the pleasure of speaking with Jian and other female leaders within our ecosystem in celebration of #IWD2024. With 15+ years of software programming experience, Jian is in a unique situation – being a senior developer, female, Chinese, an immigrant, and currently the only female on a small start-up team that is tasked to build an AI-powered edge computing platform, Edge Signal. Read the article to learn more: https://lnkd.in/eUyBJrVg. #tech #femalesintech #womenintech #edgesignal

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