SqlDBM

Excited to share that SqlDBM just launched iFrame- our newest feature making it easier than ever to embed live, interactive data models directly into your tools, portals, or docs. It’s seamless. It’s secure. And it’s going to change how teams collaborate on data modeling. #SqlDBM #DataModeling #ProductUpdate #Collaboration

📢 Share database models... anywhere! Our new iFrame embedding feature lets you share your SqlDBM models—giving teams across the company instant and secure access. From governance tools to CRM systems to internal knowledge-sharing repositories, SqlDBM projects can now be shared to any site that supports embedded HTML content. Please reach out to your account manager or contact us using the link below to find out more! https://lnkd.in/dkSC2iAj

🧱 Databricks + SqlDBM: 6 Core Components for Data Modeling Success Building a state-of-the-art Lakehouse without a clear blueprint is challenging. At last year's Data & AI Summit, we outlined six essential components every successful data architecture needs: 1. Model Management 2. Roles & Responsibilities 3. Governance 4. Supporting Technology 5. Communication & Collaboration 6. Data Quality SqlDBM integrates seamlessly with Databricks, making data modeling agile and efficient. You can watch the full session below. Speaker: Hazal Sener, Enterprise Data Modeling Specialist #datamodeling #dataengineering #databricks

As enterprises accelerate toward governed, scalable, and cloud-native architectures, we’re proud to be at the forefront—helping organizations design, standardize, and optimize their data strategies. A huge thank you to CDO Magazine for sharing our story! #datagovernance #datamodeling #dataquality

Practical Guide for Incorporating GenAI into Your Data Modeling Practice (from SqlDBM’s Product Manager Iván López Ribas) Feel free to post questions in comments and we will make sure to answer them. #datamodeling #dataengineering #genai

Here’s One Simple Step to Evaluate If Your Organization Is Ready for Cloud Data Modeling The truth is: not every organization is. SqlDBM is built for teams serious about data strategy—those looking to scale, govern, and optimize their cloud data initiatives. If you're a C-level data leader in Retail, Manufacturing, Financial Services, Insurance, or Pharma, you’re likely re-evaluating your data strategy for the cloud era. Outdated modeling slows teams down. It creates governance issues, inefficiencies, and roadblocks to scale. But with so many competing priorities, every decision needs a clear return. 🔹Does this investment make sense for your team’s size? 🔹Is now the right time to make the change? 🔹How do you calculate the ROI and make the right call? Download our ROI Report to take the next step with confidence. #datamodeling #roi #datastrategy

Although many CDOs and CDAOs recognize the importance of data modeling, their organizations still face challenges due to outdated, on-premises data modeling methods, which can lead to inefficiencies, compliance risks, and scalability issues — contributing to the very problem they were expected to solve. Read more: https://lnkd.in/gBmEVUUE #datamodeling #dataculture #CDOs

Last week our team had the privilege of joining the data and AI community in San Francisco for CDO Magazine’s Leadership Dinner. It was an incredible opportunity to exchange insights on harnessing the power of GenAI in the enterprise data journey. #datamanagement #dataengineering #dataandai

Data modeling and data governance are deeply interconnected because governance ensures that data definitions, standards, and policies are consistently applied, while modeling structures the data accordingly. SqlDBM allows users to import documented data definitions from governance platforms (like Collibra, Alation, or Informatica) into SqlDBM’s data modeling environment. This enables teams to: 1. Maintain consistency between data governance policies and actual database designs 2. Reduce manual effort in aligning business definitions with technical implementations 3. Improve collaboration between governance, engineering, and analytics teams 4. Ensure compliance by structuring models around approved data definitions #datamodeling #datacataloging #datagovernance