DataOps School

🔹 1. Introduction In Databricks, you often need to interact with: 👉 For these tasks, Databricks Utilities (dbutils) provide built-in helpers. Key points: 🔹 2. What is dbutils? dbutils is…

🔹 1. Introduction In Databricks, we usually store tabular data in Delta tables (structured data).But what about: 👉 For these, Databricks introduces Volumes, which provide a governed, secure storage layer…

Delta Lake keeps improving with features that optimize performance and storage. Two of the most important recent features are: Let’s explore both in detail with examples you can run inside…

This tutorial covers how to perform upserts (MERGE) in Delta tables on Databricks, with both hard deletes and soft deletes (using SCD1 style). 1. 🔹 Introduction In Delta Lake, the…

This tutorial will walk you through core Delta Lake functionality in Databricks, including catalogs, schemas, tables, views, CTAS, deep clone, and shallow clone. Each section is backed with SQL and…

this is exactly the core of Unity Catalog’s object model. The way Databricks resolves storage paths for managed tables depends on where you attach the external/managed location. Let’s break it…

Databricks Unity Catalog Tutorial Managed vs External Tables + UNDROP (with External Location setup) Introduction (what we’ll build) You’ll learn to: What’s new in Databricks? (Updates & Releases) In the…

We will: Unity Catalog has a 4-level hierarchy: Metastore → Catalog → Schema → Table 👉 Today we’ll create three schemas to see how Unity Catalog stores managed table data…

Good Read – https://dataopsschool.com/blog/databricks-catalog-schemas-tables-with-external-location/ 1. Create Catalog without External Location 2. Create Catalog with SQL 3. Drop Catalog and Drop Catalog Recursively 4. Create External Location in Databricks 5. Create…

🔑 Unity Catalog vs Catalogs vs Workspace vs Metastore 1. Unity Catalog (UC) ✅ 👉 Analogy: National Library System – it governs all libraries in a country. 2. Catalogs 📚…

Databricks Components Hierarchy 1. Account Level (Top Layer) 2. Governance & Data Management 3. Computation & Execution 4. Developer Interfaces 5. Data & AI Layers ✅ In one line:

1. Introduction & Overview What is Data Democratization? Data Democratization is the process of making data accessible, understandable, and usable to everyone in an organization—without requiring deep technical expertise. It…

Introduction & Overview What is a Semantic Layer? A semantic layer is a data abstraction layer that sits between raw data sources and business users, providing a consistent, unified, and…

1. Introduction & Overview What is a Metrics Store? A Metrics Store is a centralized repository designed to store, organize, and serve business metrics in a consistent, governed, and reusable…

1. Introduction & Overview What is a KPI Dashboard? A KPI Dashboard (Key Performance Indicator Dashboard) is a data visualization tool that consolidates and displays real-time business or technical metrics…

1. Introduction & Overview What is Self-Service Analytics? Self-Service Analytics (SSA) is an approach that empowers business users, analysts, and even non-technical stakeholders to access, explore, and analyze organizational data…

1. Introduction & Overview What is Embedded Analytics? Embedded Analytics is the integration of analytical capabilities (like dashboards, reporting, and visualization) directly into applications, workflows, or business platforms. Instead of…

1. Introduction & Overview What is Looker? Looker is a modern Business Intelligence (BI) and Data Analytics platform (acquired by Google in 2019, now part of Google Cloud Looker Studio)…

1. Introduction & Overview What is Power BI? Power BI is a business intelligence (BI) and data visualization tool by Microsoft that allows organizations to connect to various data sources,…