45 Metric Views in Databricks Unity Catalog | Design Semantic Model | Measures and Attributes
Description
Databricks Metric Views | Semantic Model in Databricks | Unity Catalog Metric Views Video explains - What is Metric View in Databricks Unity Catalog? How to define measures in Metric Views in Databricks? How to use Window function logic Metric Views? How to join tables in Metric Views in Databricks? Chapters 00:00 - Introduction 00:14 - What is Unity Catalog Metric View in Databricks? 00:46 - Benefits of Metric Views 02:15 - How to create Metric Views? 04:34 - How to create dimensions Metric Views? 07:44 - How to create measures Metric Views? 09:57 - How to use Metric Views in SQL? 12:49 - How to use Window function logic Metric Views? 23:13 - How to use Joins Metric Views? Databricks Website: www.databricks.com Databricks Metric Views - https://learn.microsoft.com/en-us/azure/databricks/metric-views/create Data Warehousing Playlist - https://youtube.com/playlist?list=PL2IsFZBGM_IE-EvpN9gaZZukj-ysFudag&si=V3RiyxZ_fNBKj8dS The series provides a step-by-step guide to learning Databricks, a popular unified Data Intelligence Platform. New video in every 3 days ❤️ Follow Subham Khandelwal on LinkedIn and Don't forget to Share - https:// www.linkedin.com/in/subhamkharwal/ Discla…
Description from YouTube. Full content on the video page.
More from Ease With Data
Tutorials✅ How Transformers Work - Attention Explained Step by Step | Chapter 06
The video explains the Transformer architecture, detailing how it processes text input through tokenization, embedding, and a stack of Transformer blocks to generate the next token. It breaks down the attention mechanism, multi-head attention, and feed-forward layers within a Transformer block, highlighting the differences between encoders and decoders.
NewsHow LLMs Understand your Prompts: Tokenization & Embeddings | Chapter 05
The video explains how Large Language Models (LLMs) understand text by converting it into numerical representations through tokenization and embeddings. It demonstrates how text is broken into tokens, assigned unique IDs, and then transformed into dense vectors (embeddings) that capture semantic meaning and positional information for LLM processing.
TutorialsHow Large Language Models (LLMs) Work - Full Explanation | Chapter 04
Large Language Models (LLMs) are text-based neural networks trained on massive data to predict the next word (token), operating through tokenization, vector embeddings, and a transformer architecture. LLMs undergo pre-training, supervised fine-tuning, and reinforcement learning from human feedback to become helpful, safe, and aligned, with concepts like context length, knowledge cut-off, and hallucination defining their capabilities and limitations.
NewsHow Neural Network works | Weights and Bias #dataengineering #neuralnetworks #genai
A neural network's neuron processes input signals by assigning weights to each, reflecting its importance (e.g., monthly income has a high positive weight, outstanding debts a negative weight). These weighted inputs are summed with a bias, and the result is passed through an activation function to produce an output decision.
TutorialsNeural Networks Explained - How They Work & Are Trained | Chapter 03
This video explains how artificial neural networks (ANNs) work, detailing the components of a neuron (inputs, weights, bias, activation function) and how they form layers in a network. It also covers the training process, including forward propagation, loss calculation, and backpropagation using gradient descent to adjust weights and biases.