Patrick Cuba

Mastering Snowflake Case Studies I'm excited to share our Mastering Snowflake Case Studies—a comprehensive resource showcasing how we’ve helped organizations across Australia, the US, the UK, and Europe transform their data strategies with Snowflake. From rapid deployments to solving complex data integration and security challenges, these case studies highlight real-world success stories. Whether you're in financial services, sports, insurance, or construction, this guide is packed with insights on how you can optimize your data assets, empower your teams, and accelerate your business value. 💡 What’s inside? ✅ Rapid Deployment & Security: Discover how XpressLaunch enabled secure Snowflake setups for seamless operations. ✅ Tailored Expertise: Custom solutions that streamline complex environments. ✅ Comprehensive Coverage: Key strategies for addressing critical areas like data sourcing, engineering, and performance optimization. ✅ Empowered Teams: Training and knowledge transfer that ensures your data team can maintain and optimize your Snowflake environment. Get exclusive access to the case studies now and see how organizations worldwide are unlocking their data potential! 👉 Download your copy here: https://lnkd.in/enytwv5d Simply enter your email to get started. #Snowflake #DataTransformation #XpressLaunch #DataStrategy #DataEngineering #CaseStudies

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Aristotle Metadata is calling on industry experts, data professionals and data users to take part in a global test run of the MAST Certification process. Scheduled across varying dates in Canberra, Australia; Wellington, New Zealand; Nova Scotia, Canada; and Sydney, Australia, participants will receive an exclusive ‘first look’ at the MAST Certification course, along with being able to provide feedback and insights into the course structure and future directions of the MAST Methodology, assisting Aristotle Metadata in its endeavours to seek new ways to close the cultural and framework gap for metadata management and data governance. What to Expect: 🟢 Opportunity to shape industry standards and become MAST Methodology certified. 🌏 Be part of a global test run, providing valuable feedback for final refinement. ❌ Make your mark on metadata and data governance practices. 🤝 Engage with global perspectives with a community of metadata experts from around the world. #MAST #metadata #certification

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Note that the Enterprise Big Data Engineer Text Book is now available free of charge (until September 30). Link available below: https://lnkd.in/gn2hnfHw #datascience #dataengineer #bigdataframework #certification #dataengineering #dataframeworks

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Why are iceberg data tables so hot right now ? - the TLDR before it melts ‘ -#AI needs structured #data of multiple variables to make a personalised offer or recommendation ( Netflix) Fast queries on vast amounts of data without sacrificing atomicity and granularity is key ( what worked for Netflix became Iceberg) .. Data ferreting by engineers and pipelines is a slow moving beast ( where the non Netflix world is ) … Everyone wants to be the Netflix of their industry using AI for personalization with Modern data infra that’s easy to use . #snowflake #databricks #confluent https://lnkd.in/g5V8S6gA

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Speaking of monitoring Spark jobs, someone kindly curated a detailed tutorial with numerous linked pages to assist you in diagnosing problems with your Spark job. Even if you're not currently troubleshooting an issue, simply reading through it will provide valuable insights into Spark architecture. Thank you so much! The next step is to further simplify these monitoring processes into a single, user-friendly UI. 😄 #dataengineering #dataengineer

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Discover the power of metadata management in Databricks with Apache Hive Metastore (HMS). In his latest blog, "Hive Metastore (HMS) and hive_metastore in Databricks", Gentrit Mehmeti explores how Hive Metastore supports large-scale data organization and enhances compatibility with Apache Spark. Gentrit also highlights the evolution of data governance with Unity Catalog, which offers advanced features like auditing and improved access control, shaping the future of data management. Learn how mastering metadata is key to handling today’s data complexity. 🖇 https://lnkd.in/dZrvDnnc #DataManagement #Databricks #ApacheHive #Metadata #UnityCatalog #CloudComputing #ApacheSpark

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Snowflake and Meta Team Up to Host and Optimize New Flagship Model Family in Snowflake Cortex AI “Snowflake’s world-class AI Research Team is blazing a trail for how enterprises and the open source community can harness state-of-the-art open models like Llama 3.1 405B for inference and fine-tuning in a way that maximizes efficiency,” said Vivek Raghunathan, VP of AI Engineering, Snowflake. “We’re not just bringing Meta’s cutting-edge models directly to our customers through Snowflake Cortex AI. We’re arming enterprises and the AI community with new research and open source code that supports 128K context windows, multi-node inference, pipeline parallelism, 8-bit floating point quantization, and more to advance AI for the broader ecosystem.” Dave Lindley E15 Group Ryan K. Hakkōda Matthew Scullion Matillion Kevin Niparko Twilio Denise Persson Barbara Lewis https://lnkd.in/ehYGAG7N #fintech #finance #banking #paytech #payments #fintechnews #paymentsnews

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I will be speaking tomorrow at the Data Architecture Sydney Conference.  My talk will focus on going beyond strategy slides when it comes to data governance.  In addition to my session, there will be other talks on topics such as data mesh, cloud data platforms, creating a single view of the customer, and AI readiness. I am excited to meet some of you there! #dataarchitecturesyd

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Let's transform raw page view data from Apache Kafka into aggregated insights directly loaded into Databricks Delta tables. 🧱 We explore real-time data aggregation, demonstrating how to downsample page view data across a 10-second time window using just a few SQL queries. By processing data before it reaches Databricks, you can dramatically reduce storage and compute costs while minimizing latency. It's all here 👇 https://lnkd.in/g7QwbSUD

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$62 Billion Valuation. $10 Billion Raised. Databricks Has Set the Bar! What Does This Mean for Businesses? When massive funding like this happens, it’s not just a number - it’s a signal. A signal that the rules of the game are changing. ✅ For companies: It’s a reminder to ask, Are we truly prepared for the next era? The way businesses innovate, operate, and grow is being rewritten. Those who stay ahead will thrive. The rest? They’ll be playing catch-up. Here’s what sets Databricks apart: 1️⃣ Clarity of Vision: Databricks didn’t start out chasing trends. They built something foundational - solutions that power how the world works with data. 2️⃣ Customer-Centric Innovation: They didn’t just create products; they created ecosystems. Databricks is about empowering others to innovate, making them indispensable. 3️⃣ Scaling Smart: They grew fast but never compromised on their core. Their ability to combine innovation with execution is why they’re a market leader today. At CareerXperts Consulting , we believe that moments like these are more than headlines. They are reminders that bold decisions today shape lasting legacies tomorrow. The conversation isn’t about what’s happening - it’s about what you do next. So, what’s your next move? #Leadership #CareerGrowth #BusinessInsights #Databricks #Funding #VentureCapital #VCs #Startups #Resilience #BillionDollarStartups #Technology #Innovation #Data #AI #FundingRounds

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It's Day 2 in our Snowflake mini-series. Since being founded in 2012, it's fair to say Snowflake has been pretty transformative to data teams' lives worldwide. Here are some of the ways how Snowflake revolutionised Big Data: 1️⃣ Separation of Storage and Compute: - Before Snowflake, businesses had to deal with monolithic architectures where storage and compute were tightly coupled, leading to inefficiencies and high costs - Snowflake’s architecture decoupled storage from compute resources, allowing businesses to scale independently. This means you only pay for what you use, optimising costs and performance. 2️⃣ Data Sharing and Collaboration: - Sharing data across organisations used to mean cumbersome data transfers and security risks. - Snowflake’s secure data-sharing features allows for instant, controlled access to live data without the need to move or copy it. - This has enabled real-time collaboration across organisations and created a dynamic ecosystem where partners, vendors, and customers can seamlessly interact. 3️⃣ True Multi-Cloud Capability: - With Snowflake, you’re not tied to a single cloud provider. Businesses can deploy across AWS, Google Cloud, and Microsoft Azure, leveraging the strengths of each. - This multi-cloud strategy ensures high availability, disaster recovery, and vendor independence, offering unparalleled flexibility and global reach. - Previously, organisations were often locked into a single cloud provider, limiting flexibility and increasing risk. If you want to find out how Orchestra can further revolutionise life for Snowflake users, check out for yourself down below! ➡ https://lnkd.in/eM6uqgCU #BigData #CloudComputing #Snowflake #Innovation

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Why should Machine Learning teams use Feature Stores within Databricks? 🤔 This question and more were answered by Ben and Sepideh at Databricks when they spoke to Gavi in our most recent Advancing AI video: https://hubs.ly/Q02vyj4R0 #MachineLearning #ML #AI #FeatureStores #Databricks

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Cloud is superb but sometimes it can be disastrous if there's no proper planning, monitoring and day to day execution. I heard many cases of organizations & individuals spending too much money or overspending on cloud during their early cloud onboarding. As per my experience, clouds provider frequently advises on the usage cost, use services that only matters for your business process, scale your services based on your operation volume ... 'pay-as-u-go'. But sometime bad things happened, cloud user might forget or put too much trust on the cloud automation. Automation is good, though it must be integrated with some sort of "safety-net" to ensure this service bring more good than bad. I don't know maybe cloud providers could do some sort of analytics on their client usage pattern and trigger an alert to them when anomalies detected? Maybe they have implemented this, but I wonder why this incident happened. https://lnkd.in/gBqCMM7H 

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Snowflake releases arctic-embed models, a family of 5 text embedding models that range from 23 to 334 million parameters. The models are the result of search knowledge, and research and development that Snowflake acquired last May via Neeva. 💵 The models outperforms retrieval performance, having one-quarter of the estimated parameters and one-third of the dimensions compared to Open AI. 📊 https://lnkd.in/gjJy9Yte Some more insights on how the technical achievements are detailed in this blog 🦾https://lnkd.in/g_aYgwrH

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