Cube

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

The secret to a better data stack? A semantic layer ??. ICYMI Cube’s Tony Kau and Coalesce's Douglas Barrett and Josh Hall shared how adding a semantic layer transforms your analytics experience, driving consistency and governance across your organization. Catch the replay to discover how Coalesce and Cube help you: ? Pre-aggregate data for faster queries. ?? Empower teams to access analytics-ready data. ?? Build seamless pipelines that support data mesh strategies. Watch here: https://lnkd.in/gm5mBDr6

Dive into the latest Pulse 2.0 interview with Artyom Keydunov, Co-Founder and CEO of Cube, as he shares his perspectives on the future of the universal semantic layer. Discover how Cube is transforming data analytics and empowering businesses worldwide. ?? https://hubs.la/Q02Y747n0

Exciting insights from Cube's CEO, Artyom Keydunov, in his latest Forbes article! ?? As modern cloud OLAP solutions revolutionize data analytics, our spreadsheets are often left in the dust. Discover how businesses can bridge the gap and empower spreadsheet users to thrive in the modern data stack. Don't let your team get left behind! Dive into the full article to learn more about unlocking the potential of your data. ?? https://hubs.la/Q02Y4N1h0 #DataAnalytics #CloudOLAP #BusinessIntelligence #Cube #Forbes #DataDriven #SpreadsheetRevolution

Join us tomorrow for an in-depth session with Cube's Tony Kau and Coalesce's Douglas Barrett and Josh Hall to explore how adding a semantic layer can elevate your data analytics experience ??. Discover how integrating Coalesce and Cube helps you: ?? Use organization-wide data without deep technical knowledge ?? Boost query performance and cut costs with Cube’s pre-aggregated data ?? Enable self-service analytics with Coalesce’s data mesh support Register below ?

Don’t miss our upcoming webinar: "Unlock More Business Value with Unified Data," featuring industry experts like Andrew Brust from GigaOm and Cube’s executive team. ?? Thursday, November 14 at 9 am PT | 12 pm ET Why should you attend? - Gain insights into Cube Cloud’s success, as highlighted in the 2024 GigaOm Sonar Report - Explore the advancements in semantic layers and metrics stores - Learn how to overcome common barriers in data management and governance Join us to unlock the potential of a unified semantic layer that's more relevant than ever. ??https://hubs.la/Q02WGNCH0

Next-Gen Data Modeling Engine (Tesseract) was one of the newest updates from Cube Rollup. ?? Check out the other features and announcements by watching the webinar on-demand: https://hubs.la/Q02WWNzZ0