Cube

Ready to uplevel your analytics game? It starts here—at "Building Better Data Experiences: Embedded Analytics Deep Dive!" 🔗 Register now and join us for this session 👉 https://hubs.la/Q02ZtxcG0

AWS re:Invent is here! Come by booth #691 or meet with the Cube crew 🔗https://hubs.la/Q02YLtD80

Excited to be in Las Vegas this week with the Cube team for AWS re:Invent! 🌟 This year, the AWS Solutions Architecture team is hosting a workshop on how to use Cube with AWS data sources. Mark your calendars: 🗓 Thursday, Dec 5 ⏰ 12:30 PM 📍 MGM Grand, Level 3, Premier 311 If you're here at re:Invent, stop by our booth or DM me—I’d love to catch up and chat about all things data and semantic layers!

Our upcoming webinar, "Building Better Data Experiences: Embedded Analytics Deep Dive," is your inside pass to unlocking the potential of modern embedded analytics. Here's what you'll gain from attending: ✨ Benefits Galore: Discover why integrating analytics directly into your applications is the game-changer for creating unforgettable user experiences. ☁️ Cloud Revolution: Understand how cloud technology powers innovation in the embedded analytics space. 🛠 Tools That Matter: Learn how to pick the perfect tools for your project needs—because one size never fits all. 📦 Your Data Stack Blueprint: Get an in-depth understanding of the key components of a modern, future-ready embedded analytics setup. This is your moment to stay ahead in analytics and future-proof your strategies. Don't miss out— Reserve your spot today! https://hubs.la/Q02ZtKyw0

Are you ready to uplevel the way your users interact with data? Join us for an exclusive deep dive webinar: "Building Better Data Experiences: Embedded Analytics Deep Dive" 🌟 Understanding and leveraging the modern data stack is no longer optional—it's essential. This webinar will unlock the secrets to embedding analytics seamlessly into your applications, amplifying your user experience like never before. 🎯 What’s in it for you? Discover how cloud technology is reshaping the embedded analytics landscape and learn to choose the right tools that align with your unique project goals. Take advantage of this opportunity to elevate your data strategy. 🗓️ Mark your calendar and secure your spot today! Sign up now to take a step closer to mastering embedded analytics 👉 https://hubs.la/Q02Ztxcz0

🌟 Calling all AWS enthusiasts! Join us for Data Rock and Roll at the Brooklyn Bowl! Connect with innovators, engage in discussions about cloud and data, and compete in friendly bowling matches. It’s the perfect mix of fun and networking! 🎶🍻 🗓️ Tuesday, December 3rd 🕒 6:00pm - 10:00pm 🔗 https://hubs.la/Q02YLD8R0 #Bowling #Networking #AWSreInvent

Data modeling is one of the most essential tasks. It's where business requirements meet engineering, forming the foundation of any data project. But why don't we take more care of it? Why did we write the same metrics differently across departments? Why do we keep reinventing data models with each new tool we adopt? This got me thinking: Wouldn't it be convenient to separate the presentation from the storage through a data modeling layer? A place where we can declaratively define our metrics, measures, and business KPIs to update them confidently and version them. Today, we have the beginning of such a separation of concerns with a Semantic Layer, and I make my case in this article (https://lnkd.in/ew-etp6u). --- In case you have never heard or didn't fully understand what a semantic layer is and why you should have one, this is my article. I compare it to the MVC (Model View Controller), a term popularized in the 70s and used to this day. I wondered why and took parallels between the data world and MVC, including Active Records, the intermediary between presentation (view) and data source (model). Translating and implementing valuable business logic, the ORM maps the code to its database table. Wouldn't it be nice to have an *ORM for data*, too? A modeling language that defines metrics, facts, and dimensions and applies them to the model, the heterogeneous data sources. This allows for better maintainability, separation of concerns, and, above all, the ability to model the data logically to map the business requirements to our databases. Additionally, we get even more: 🎨 #DataModeling: Unified, consistent metric definition in a declarative manner. 📈 #APIs (GraphQL, REST, SQL, MDX, AI): Integrate with any endpoint and deliver trusted data. 👮🏻 #AccessControl: Centralize and fine-grain governance and security policies. 🏃🏻♂️ #Caching: Query optimization, pre-aggregations; deliver faster, more cost-efficient results. --- We can use the DRY principle, create complex business KPIs once, and provide convenient access through out-of-the-box APIs. We can reuse and build on top of each measure and business metric. Ultimately, it enables better data governance and simplifies data modeling in large organizations. Read more in the article; I hope you enjoy it. I'm excited to hear what you think about this comparison.

Data modeling is one of the most essential tasks. It's where business requirements meet engineering, forming the foundation of any data project. But why don't we take more care of it? Why did we write the same metrics differently across departments? Why do we keep reinventing data models with each new tool we adopt? This got me thinking: Wouldn't it be convenient to separate the presentation from the storage through a data modeling layer? A place where we can declaratively define our metrics, measures, and business KPIs to update them confidently and version them. Today, we have the beginning of such a separation of concerns with a Semantic Layer, and I make my case in this article (https://lnkd.in/ew-etp6u). --- In case you have never heard or didn't fully understand what a semantic layer is and why you should have one, this is my article. I compare it to the MVC (Model View Controller), a term popularized in the 70s and used to this day. I wondered why and took parallels between the data world and MVC, including Active Records, the intermediary between presentation (view) and data source (model). Translating and implementing valuable business logic, the ORM maps the code to its database table. Wouldn't it be nice to have an *ORM for data*, too? A modeling language that defines metrics, facts, and dimensions and applies them to the model, the heterogeneous data sources. This allows for better maintainability, separation of concerns, and, above all, the ability to model the data logically to map the business requirements to our databases. Additionally, we get even more: 🎨 #DataModeling: Unified, consistent metric definition in a declarative manner. 📈 #APIs (GraphQL, REST, SQL, MDX, AI): Integrate with any endpoint and deliver trusted data. 👮🏻 #AccessControl: Centralize and fine-grain governance and security policies. 🏃🏻♂️ #Caching: Query optimization, pre-aggregations; deliver faster, more cost-efficient results. --- We can use the DRY principle, create complex business KPIs once, and provide convenient access through out-of-the-box APIs. We can reuse and build on top of each measure and business metric. Ultimately, it enables better data governance and simplifies data modeling in large organizations. Read more in the article; I hope you enjoy it. I'm excited to hear what you think about this comparison.

Meet with Cube at AWS re:Invent-- come by our booth, schedule time to meet the team, or stop by the happy hour. Check it out 👉https://hubs.la/Q02YLtD80

🎳 Get ready to roll at Data Rock and Roll! Join us at the Brooklyn Bowl in Las Vegas for an epic happy hour during AWS re:Invent! Network with industry leaders, enjoy great music, and show off your bowling skills. Don’t miss out—RSVP today! 🔗 https://hubs.la/Q02YCVdY0 #AWSreInvent #DataRockandRoll