# Star Schema
Suosituimmat podcast-jaksot
Päivitetty:
In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
Creating a Better Data Warehouse with the Unified Star Schema, Featuring Francesco Puppini
In a recent conversation with data warehousing legend Bill Inmon, I learned about a new way to structure your data warehouse and self-service BI environment called the Unified Star Schema.
The Unified Star Schema is potentially a small revolution for data analysts and business users as it allows th…
Dear Analyst #131: Key insights and best practices from writing SQL for 15+ years with Ergest Xheblati
If you could only learn one programming language for the rest of your career, what would be it be? You could Google the most popular programming languages and just pick the one of the top 3 and off you go (FYI they are Python, C++, and C). Or, you could pick measly #10 and build a thriving career o…
Reduce Friction In Your Business Analytics Through Entity Centric Data Modeling
Summary
For business analytics the way that you model the data in your warehouse has a lasting impact on what types of questions can be answered quickly and easily. The major strategies in use today were created decades ago when the software and hardware for warehouse databases were far more cons…
How Column-Aware Development Tooling Yields Better Data Models
Summary
Architectural decisions are all based on certain constraints and a desire to optimize for different outcomes. In data systems one of the core architectural exercises is data modeling, which can have significant impacts on what is and is not possible for downstream use cases. By incorporat…
Building ETL Pipelines With Generative AI
Summary
Artificial intelligence applications require substantial high quality data, which is provided through ETL pipelines. Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his…
259: How Important Is Star-Schema in Microsoft Fabric?
Since Analytical Models the star-schema design has been the cornerstone of reporting solutions, which was just enhanced and even more in Power BI.
However, with Direct Lake, Lakehouses, and Data Science in Fabric, do star-schemas still have the same impact?
Mike, Seth, & Tommy discuss this in det…
Accelerate Development Of Enterprise Analytics With The Coalesce Visual Workflow Builder
Summary
The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. Coalesce is a platform designed to reduce repetitive work for common workflows by adopting a visual pipeline builder …
How To Bring Agile Practices To Your Data Projects
Summary
Agile methodologies have been adopted by a majority of teams for building software applications. Applying those same practices to data can prove challenging due to the number of systems that need to be included to implement a complete feature. In this episode Shane Gibson shares practical…
Exploring The Insights And Impact Of Dan Delorey's Distinguished Career In Data
Summary
Dan Delorey helped to build the core technologies of Google’s cloud data services for many years before embarking on his latest adventure as the VP of Data at SoFi. From being an early engineer on the Dremel project, to helping launch and manage BigQuery, on to helping enterprises adopt G…
Be Careful with that Umfahren, w/ Lars Schreiber
Lars Schreiber is another one of the great examples of the human element of data that we love! He's an MVP, a long-standing member of the Power BI Community, and his passion for the Power Platform is quite catchy. Catch up with Lars at his website, Self Service BI Blog!
References in this episode:…
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