Ankit Kandoi

Exploring the Rise of Data Streaming in India: Confluent’s Rubal Sahni explores the transformational power of real-time insights in India’s business landscape. Discover how Confluent leverages Apache Kafka for enhanced operational efficiency and customer experiences. Read More: https://lnkd.in/g4wU4bbb #DataStreaming #BusinessTransformation #TechNews #Innovation #Confluent #ApacheKafka

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It was a grand start to snowflake data cloud summit'24. Wipro is uniquely positioned on the joint partnership with Snowflake and Nvidia in 3 areas: 1. AI and Machine Learning Integration: Wipro's WDIS powered by Snowflake’s data capabilities and NVIDIA’s GPUs to accelerate data processing and analytics, leading to faster insights. • AI Model Training: NVIDIA’s GPUs can significantly speed up the training of AI models on data stored in Snowflake, reducing time-to-market for AI-driven solutions. (Sridhar Ramaswamy spoke about "moving computing to data than data to the computer and Cortex AI next to data" ) • Inference at Scale: Combining Snowflake’s scalable data platform, with Wipro's WEGA framework and NVIDIA’s inference capabilities can enable real-time AI applications 2. Enhanced Data Work Load Processing • GPU-Accelerated SQL: Integration of NVIDIA’s RAPIDS libraries with Snowflake can enable GPU-accelerated SQL operations, along with seamless integration of WDIS • Complex Data Workloads: Leverage NVIDIA’s powerful GPUs to handle complex data workloads with Snowflake’s ecosystem and Wipro's WDIS 3. Industry-Specific Solutions • Healthcare : Use cases where Snowflake’s data sharing and analytics capabilities can be combined with NVIDIA’s AI tools to drive integrated value based care in health. • Financial Services: Wipro's fraud detection, risk management, and high-frequency trading by combining Snowflake’s data capabilities with NVIDIA’s AI technologies. • Retail and E-commerce: Enhance customer insights, recommendation systems, and inventory management using the combined power of Snowflake’s data platform and NVIDIA’s AI and analytics tools. Looking forward to exciting next 3 days and great partnership: Tyler Prince Amy Hirst-Kodl Dan Waters Sundara Ramalingam Nagalingam Scott Sharpley Alvin Da Costa Puneet Khanna Nagendra Bandaru Jason Eichenholz Ravi Seshadhri Narayan Kamat Pushpa Ramachandran Ajay Mangilal Jain Ajay Upadhyay Ashish Sharma Raju Gopalan Balasubramani Harikrishnan Tanusree Saha Anindito De Brijesh Singh Rahul Deshpande

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Nine months into his role as Snowflake CEO, Sridhar Ramaswamy sat down for a candid discussion on the journey so far & what’s ahead. He uncovered details about his transition to Snowflake CEO, the operational changes he’s put in place to accelerate product development, and his bold predictions for AI’s future—along with how he’s guiding a vision for Snowflake to fit into that landscape. Sridhar also reflects on how his leadership has evolved, from leading a major Google unit to founding Neeva (acquired by Snowflake) and now running Snowflake. Some favorite takeaways… → Sridhar’s AI predictions Sridhar predicts that a major shift in AI will be voice-driven interfaces, transforming how we interact with our devices. Every app, and perhaps even our phones, will look different. For example, we could soon say, “I want this kind of car to this location and text my friend when I’ll be there,” and AI could handle all follow-up actions seamlessly through voice commands. At the same time, tech has always been hard to predict. For example, it would’ve been really difficult to foresee Uber from the early internet or even the BlackBerry era. Thus, many aspects of our future with AI are much harder to predict – such as autonomous agents acting on our behalf, arguing with one another, and training better versions of themselves. Regardless, progress is rapid and data transformation is inevitable. → How Sridhar deals with goals at Snowflake Sridhar values clear goals and designated accountability. He allows teams to set their timelines, whether for two weeks or three months, but he hates vague, long-term promises like FY26 goals. Each goal has a fixed responsible person and a set deadline, with bi-weekly check-ins to track progress. If you’re late to the goal, it’s awkward, but you deal with it. Separately, Sridhar is also set up to receive a 5 a.m. daily email with revenue metrics, keeping him informed every day. → The quality vs speed tradeoff should not exist  Many people tend to prioritize either quality or speed. Yet, Sridhar believes the answer should be quality and speed. He insists on achieving both, believing you can do good work quickly while upholding virtuosity in every task. From his experience at Google, Sridhar learned it’s not about having all the answers but about moving faster than anyone else to seize opportunities. Full episode:

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Cloudera announced an integration with Snowflake to bring enterprises an open, unified hybrid #DataLakehouse, powered by Apache Iceberg. Abhas Ricky, Chief Strategy Officer of Cloudera, said, “This will help customers simplify their data architecture, minimize data pipelines, and reduce total cost of ownership of their data estate while reducing security risks. Together, Snowflake and Cloudera are bringing about the next era of data-driven decision making for every modern organization.” “Apache Iceberg is a leading choice for customers who want open standards for data, and Cloudera has been an integral part of the Iceberg project,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “Our partnership expands what’s possible for customers who choose to standardize on Iceberg tables. We are excited to break down silos and deliver a unified hybrid data cloud experience with multi-function analytics to all of our customers.” “Through this collaboration, customers gain access to a unified, robust data management platform that provides a single source of truth for all of their data, whether in the cloud or on- premises,” said Sanjeev Mohan, analyst at SanjMo. “This enables them to streamline and secure their data operations while efficiently analyzing and extracting insights across the entire data lifecycle – from ingestion to AI and analytics. It’s a strategic move from two industry giants to partner in a way that will deliver immediate value to businesses.” . Read in detail at: https://lnkd.in/gAYujQ9p . Remus Lim Mikael Kreij Jeff Healey Rachit Chandra Baasit Sharief Saraswathi Mohan Dhiraj Narang Alpana S. Sridhar Ramaswamy . #smartstateindia #aisolution #cloudera #snowflake

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🎇 Light Up Your Data Insights this Diwali with BigQuery & Vertex AI Agent Builder! 🎇 Tired of fumbling in the dark to find insights in your BigQuery data warehouse 🕶? What if you could search your massive BigQuery datasets as easily as Googling 🤔? This Diwali, illuminate your data with the power of Vertex AI Search 🚀! Dive into my latest blog post to learn how to build a powerful, AI-driven search application for the 'tourism' use case. Link: https://lnkd.in/grt36_fU #Vertex_AI hashtag#Search hashtag#BigQuery Google Cloud

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Thank you Shruti Mantri for sharing some of your learnings especially on the classic use case of Backfilling & how it can help as you work with Real-Time data in Apache Pinot. Keep writing and shining! Also, Startree, the company backing Apache Pinot has rolled out a free forever cluster. Take it for a spin & experience real-time analytics. Sharing the free forever Startree cluster link in comments

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How Snowflake Integration Helped Graas Achieve 10x Customer Growth Read more. #SnowflakeIndia #Graas #AIDataCloud

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Teradata: Trusted data. Trusted AI. 👇👇👇 Without secure, harmonized #data in an open ecosystem, no amount of #AI investment will pay off. What is trusted data? 📊 Trusted data is the ability to seamlessly integrate and harmonize data across an organization. It provides a trusted foundation for reliable, accurate, and well-governed data. Knowing where your analytics solutions' underlying data and models come from will significantly enhance trust. For example, an organization can build an Enterprise Feature Store, based on a trusted data foundation, making it easier to use previously vetted models and improve productivity. This is the type of consistent and reliable management of data—at scale—that will meet any business's evolving needs. What is Trusted AI? 🤖 Trusted AI is the way people, data, and AI work together, with transparency, to create value. See how enterprises are rethinking their data and AI strategies with Teradata—the Trusted AI company: https://lnkd.in/ddBCMKR8 #trustedAI #teradata

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A cool use of the “contribution analysis” ML model to answer the “why” kind of business questions. Combined with Gen AI, it enables quick insights for any enterprise.

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Interesting demo for those who utilize Snowflake and are progressing on their Gen AI journey. #snowflake #cortex #genai #chatbot

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Discover how a unified data platform can accelerate the value delivery..

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Teradata announces a breakthrough data architecture for #AI. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗢𝗽𝗲𝗻 AI adoption has driven a rapid consolidation of data warehouses, #analytics, and data science workloads into unified lakehouses. Breaking down all the data access and performance barriers slowing AI innovation, Teradata now offers a 𝘀𝗶𝗻𝗴𝗹𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 for 𝘂𝘀𝗲 𝗯𝘆 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝘀 and tools without the need to move, replicate, or transform large datasets. And with its 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, and 𝗾𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 of shared data, the platform is designed to deliver the best performance in the market. https://lnkd.in/g8xcDr-P

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It is very apparent that open LLMs are gaining more popularity, and Kubernetes is the most scalable serving choice. There are two serving frameworks that support full Kubernetes deployment: Kserve (CNCF project) and RayServe. Additionally, as a physical inference server, NVIDIA Triton serves open LLMs extremely well. #knative #kserve #rayserve #kuberay #nvidiatriton Oftentimes, I get questions about which one to use for Kubernetes inference, and the answer is that it depends. Rather than focusing on the framework, it’s better to focus more on custom load balancing and autoscaling of serving, gather key performance metrics, use KV cache utilization, and other performance utilization metrics. Gathering LLM performance data and solving accordingly is the most critical part of MLOps. #kubernetes #mlops #openllm

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Snowflake's recent appointment of Vivek Raghunathan as Senior VP of Engineering marks a pivotal moment in the company's ambitious AI journey. With a robust background in leading AI initiatives, Raghunathan is set to streamline engineering and enhance AI product offerings, including exciting new tools like Document AI and an AI-powered SQL copilot. This strategic shift reflects Snowflake's commitment to not only maintaining its competitive edge against rivals like Databricks but also capitalizing on the explosive growth of AI across industries. Investing $50 million in GPU hardware and planning to hire 1,000 AI-focused talent demonstrates Snowflake's willingness to put its money where its mouth is. The future is bright for Snowflake as it aligns its engineering and AI efforts. In a world increasingly driven by data, this could be the leap needed to redefine cloud data services. Will this leadership change put Snowflake ahead in the AI race? Let's watch closely!

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Shift into overdrive with Tableau for the auto ancillary industry 🚗 Accelerate your supply chain, boost efficiency, and drive profitability. Don't just follow the road, lead the way with Uneecops. ✈ w/ Ketan Jain Brijesh K Bhatia Abhishek Gupta Siddhant Sharma Chitral Chadda Uneecops Technologies Ltd. #autoindustry #supplychain #businessintelligence #tableau #AI #automotive #analytics #datadriven #autoancillary #tableausoftware #tableautool #data #dataanalysis #dataanalytics #autobusiness #dataviz #datavisualization #dashboards #autodashboards

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🎇 Light Up Your Data Insights this Diwali with BigQuery & Vertex AI Agent Builder! 🎇 Tired of fumbling in the dark to find insights in your BigQuery data warehouse 🕶? What if you could search your massive BigQuery datasets as easily as Googling 🤔? This Diwali, illuminate your data with the power of Vertex AI Search 🚀! Dive into my latest blog post to learn how to build a powerful, AI-driven search application for the 'tourism' use case. Link: https://lnkd.in/grt36_fU #Vertex_AI hashtag#Search hashtag#BigQuery Google Cloud https://google.smh.re/4VRT

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Get to know Gemini in Databases: an #AI-powered assistant that helps across all stages of the database journey and simplifies all aspects of database operations with features like Database Insights, Database Migration Service, and more. #GoogleCloudNext

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One of the many benefits of using ML on #Snowflake is that you can trace the data flow through all your ML objects and artifacts with our new Public Preview of ML Lineage. Ravi Kumar Suresh Babu and I show you how in our new blog https://lnkd.in/g-JA5cQj

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Interesting read on Databricks Fundamentals. Delta Lake and Photon are two pivotal technologies within the Databricks ecosystem, revolutionizing the way organizations manage and analyze their data. 1. Delta Lake - Open-source storage layer that brings ACID Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data workloads. It provides reliability, performance, and data management capabilities for big data analytics. Other Key Features in Delta Lake - Schema enforcement prevents users from accidentally polluting their tables with mistakes or garbage data. - Schema Evolution - Delta Lake supports schema evolution, allowing schemas to evolve over time while maintaining backward compatibility. This enables flexibility in data ingestion and schema management, reducing development overhead and improving productivity. 2. Photon: Photon is a distributed query engine optimized for Databricks Delta Lake. It accelerates query performance and improves resource utilization for analytics workloads by leveraging advanced query optimization techniques and in-memory processing. https://lnkd.in/gpxTGEDm

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