Tomer Shiran

Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

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➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

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🌟 From Raw Data to Behavioral Predictions—Effortlessly What if AI deployment didn’t require months of preparation? With BaseModel.ai now on the Snowflake Marketplace, you can transform behavioral data into predictions in just a few clicks. ✅ Rapid Deployment: Build and deploy models in a week.  ✅ Scalable Flexibility: Create multiple models for diverse use cases.  ✅ Privacy Guaranteed: Data never leaves the secure Snowflake environment. Say goodbye to complexity and hello to actionable insights. AI for behavioral data has never been this simple. 👉 Try BaseModel.ai Today  #AI #MachineLearning #BehavioralData #BaseModel #Snowflake #Synerise 

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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Seismic’s AI enablement assistant, Aura Copilot, is now available for early access. Teams can now get faster access to answers and content, automate tedious tasks, and gain insights for better business decisions. Aura also now integrates with Microsoft Teams and Microsoft 365 Copilot to give teams in-the-moment answers and content recommendation. https://lnkd.in/d_mSbDXK

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Much has been made since last week's announcement of Apple Intelligence at Apple's WWDC. Many have been pontificating on what Apple can do from a training perspective with the vast amount of data being collected on iPhones, and this incredibly comprehensive view they have of an individual consumer. But it's very important to remember that *most* of the valuable data on an iPhone is in third-party apps, the most important of which are already owned by Google, Meta, and other giants. While Apple sanctions all apps that can be installed on iPhones through the App Store's monopoly, it does not have the right to use the application specific or consumer specific data being collected by those apps. Evidence of breaching this confidence would quickly become a massive problem. Much like Google's acquisition of Hubspot in order to easily incorporate all of an enterprises' historical content for each of its customers to build customer-specific AI models (eg think an Accenture-specific model trained on all historical hubspot-housed Accenture marketing data, and all other Google data Google about Accenture, available with the click of a button), it will be interesting to see if Apple begins to methodically buy up mobile app companies to acquire their underlying data sets for use in training. Given Apple's penchant for vertical integration, it would seem a more relevant approach than attempting to do licensing partnerships as OpenAI has to get at underlying training data. If Apple wants to own the consumer from a GenAI and personal assistant perspective, they'll need to get at the data inside the myriad apps on the iPhone. https://lnkd.in/eH-BcfNe

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Apollo.io is tanking. Deliverability is a joke and data quality is going down. I've been an enterprise Apollo user for years. I loved the platform. However, the data seems to just be getting worse and worse and deliverability is essentially non-existent. We're switching Identity Matrix to Smartlead. Any other suggestions from my outbound friends? #outboundsales #sales

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Really interesting article on how to build #serverless data pipelines with Kestra, Modal, dbt, and BigQuery. It got me digging deeper into Kestra and I’m seriously impressed by their open-source orchestration platform. The workflow UI is super slick and you can easily integrate and build automations with just about any data stack you can think of. They’re also working on a cloud version that looks like it’ll be completely serverless! 🙌 https://lnkd.in/epZfuPrg

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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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Databricks disclosed that they are expecting to cross $2.4B in revenue run-rate growing ~65% Y/Y by H1’25 (Jan FYE). This was alongside top-tier metrics, including 80%+ GMs, 140%+ NDR, and a multi-product story where ~50% of customers use 6+ products (incl. their Data Warehouse product which generated $400M in revenue up 4.0x Y/Y). Given their accelerated growth, Databricks is among the few scaled software companies, alongside hyperscalers, capitalizing on the AI boom. Equally noteworthy is their market share gain against competitors. Considering their growth trajectory, we explored various scenarios to determine when Databricks will surpass Snowflake in scale. The question is not if, but when, and we believe it is sooner than most anticipate.

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🚀 Launch day! Very excited to share that you can now programmatically access resource-level cost data through the Vantage API! This API allows customers to access hundreds of supported resources (i.e. S3 Buckets, EBS Volumes, EC2 instances, etc.) across 8 different providers like Amazon Web Services (AWS), Microsoft Azure and Google Cloud. Some really interesting aspects about this API: - You can use Vantage Query Language (VQL) for ad-hoc filtering of resources. So if you wanted to enumerate over all resources that had a particular cost allocation tag, that's supported out of the box (including Virtual Tags). - This API also supports management of Resource Reports. So if you had a Resource Report for all unattached EBS volumes that are over a certain cost threshold you can hit that Resource Report endpoint to see all the latest resources that meet that criteria on an ongoing basis. - We've integrated with hundreds of List/Describe APIs across all the providers to not only provide cost information in the API but also metadata from the service APIs themselves. Using that same EBS example above, this allows for you to see other attributes like iops or volume size with the cost data in a singular API. Extensibility through the API has always been part of the core ethos of Vantage from the very beginning. Over the last four years we've invested in having near-full coverage of all features available via the API and Terraform. This new resource API opens up an entirely new set of use-cases and future integrations that are going to be very interesting so....stay tuned. #finops #boom https://lnkd.in/eaq8hd2Y

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Some major upgrades to the SciPhi cloud are now live 1.) Free RAG completions through gpt-4o-mini (w/ documented support for other providers like anthropic, openai, etc.) 2.) Fast document ingestion 3.) Agentic RAG 4.) Control and customizability - bring your own docker w/ R2R

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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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"Every business wants to be an AI business" - I had an opportunity to attend the Data+AI Summit last week and was blown away by the pace of innovation and the business outcomes the companies can drive for their customers. I put down some of my thoughts in this blog post. I couldn't be more excited about our mission and the platform we are building to help our customers in this journey along with our partner Databricks.

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Something that stuck with me during my interview with Naveen Rao, VP of Generative AI at Databricks: "I take the hard path." He built an AI-focused chip company Nervana in 2014 when machine learning was still academic theory and the chip industry was deeply out of favor. Naveen saw computing architecture would need to fundamentally change for ML workloads. And then in 2019, observing the power of LLMs like BERT, Naveen foresaw the need for enterprise-grade services to manage their training, deployment, and customization. That vision led to a $1.3B acquisition by Databricks in 2023. What I love about Naveen's approach is his willingness to build for markets that don't exist yet. "There isn't a market, employees don't even know about it. So how the hell do you build a company?" You educate. You convince. You get people to see what you see. It's brutal work, but it's important work. You're reshaping how people think about problems.

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AI continuously provides sellers with the opportunity to take back time to focus on the most important part of their work - the customers. At Seismic, we recognize that importance and incorporated those AI capabilities into Aura Copilot, a product part of our Spring/Summer launch earlier this year. Seismic's “Aura copilot” prioritizes the crucial need that businesses need to be in control and that AI, as a Copilot, needs that human element to eventually enable the sellers and buyers. Read more of my insights into AI, Seismic’s latest product release, and our continued company growth in the San Diego Business Journal piece below: https://lnkd.in/gsVmT8yw #AI #GenerativeAI #ProductLaunch #SalesEnablement

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I'll be at HLTH next week talking with folks about RWD strategy and how modern technologies like Spindle's open token and de-identification engine unlock best-in-breed data assets customized for your use cases. If you're heading to HLTH and RWD is key to your org's strategy, reach out and let's find some time. #HLTHUSA #healthcare

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Interesting notes from Intuit's recent webinar on LLMs. What evaluation processes do you deploy? at tasq.ai our approach is to integrate the AI models with diverse, global human guidance, to support #responsibleai faster and near real time.

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Hard-coded model routing takes away intelligence from your system instead of adding to it! Interesting read below on how Ema Unlimited's Fusion model is a potent solution for the heterogeneous multi-model future of AI in the enterprise.

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