Databricks Lab & Excercise – Notebook

Here’s my Top 15 commands to try first — grouped into environment checks, Spark basics, and data handling so you learn in a logical order. 1–5: Environment & Python Basics in Databricks 6–10: Spark Session & Cluster Basics 11–15: Data Creation, Querying, and Display Bonus Tips for First Run

Databricks Data Engineer Professional – Recommended Study Order

Got it — I’ll arrange these topics into a logical learning order so you build knowledge step-by-step, starting from fundamentals and moving toward advanced Databricks optimization topics. Databricks Data Engineer Professional – Recommended Study Order 1. Core Foundations 2. Spark & Databricks Fundamentals 3. Data Storage & Processing 4. Data Pipelines & Streaming 5. Data … Read more

Compare Databricks Paid and Free Edition

Here’s a clear, updated comparison of Databricks Paid Edition vs. Free Edition (2025): Databricks Paid Edition vs. Free Edition Feature / Aspect Free Edition Paid Edition Cost Free Billed (pay-as-you-go or subscription) Cluster Size Small, limited resources Scalable clusters, large instance types, autoscaling Session Limits Limited (e.g., timeouts, max sessions) Unlimited/longer session time Users/Collaboration Single … Read more

Lakehouse vs. Data Lake vs. Data Warehouse

Here’s a concise comparison of Lakehouse vs. Data Lake vs. Data Warehouse in a table, with a slide-ready bullet summary below: Comparison Table Feature/Aspect Data Lake Data Warehouse Lakehouse Purpose Store all raw/semi-structured data Store clean, structured data for fast analytics Combine the best of both: unified, flexible analytics platform Data Types Structured, semi-structured, unstructured … Read more

Databricks Feature Coverage Table (2025, Paid License)

Here’s a comprehensive, up-to-date table showing which of the features/technologies in your list are supported natively by Databricks with a paid license (as of 2025), and which require integrations or are only partially supported. Databricks Feature Coverage Table (2025, Paid License) Feature / Term Databricks Paid Support? Details / Notes ETL ✅ Full Support PySpark, … Read more

What is Data?

1. What is Data? Data is any collection of facts, values, or measurements that can be recorded, stored, and processed by computers or humans. 2. Types of Data Data can be classified in several ways. The most common are: A. By Structure Type Description Example Structured Organized in fixed fields/columns, like a table Databases, Excel … Read more

Complete Data Glossary & Terminology

Here’s a comprehensive glossary of all the key data platform, engineering, and analytics terms we discussed—including everything from your earlier questions and the expanded list. Each keyword includes a simple explanation. This will give you a full “cheat sheet” of modern data terminology. Complete Data Glossary & Terminology Keyword Meaning / Description Raw Data Sources … Read more

Step-by-Step Databricks Data Engineer Study Plan

Here’s a step-by-step learning plan that smoothly takes you from Associate-level foundations to Professional-level mastery for the Databricks Data Engineer certifications. This path combines theory, hands-on labs, and where to “go deeper” as you progress. 🛤️ Step-by-Step Databricks Data Engineer Study Plan (Associate ➔ Professional: Fully Linked) Step 1: Databricks Platform Foundations Step 2: Data … Read more

Data Engineer Professional Certification

Data Engineer Professional Certification Domains & weightings from official documentation (updated 2025) (Databricks, Whizlabs). Domain 1: Databricks Tooling (≈20%) Domain 2: Data Processing (≈30%) Domain 3: Data Modeling (≈20%) Domain 4: Security & Governance (≈10%) Domain 5: Monitoring & Logging (≈10%) Domain 6: Testing & Deployment (≈10%)

Data Engineer Associate Certification (July 25, 2025 version)

🧰 1. Data Engineer Associate Certification (July 25, 2025 version) Exam domains & weights are based on the updated guide published for exams taken on or after July 25, 2025 (). Domain 1: Databricks Intelligence Platform (≈10%) Domain 2: Development & Ingestion (≈30%) Domain 3: Data Processing & Transformations (≈31%) Domain 4: Productionizing Data Pipelines (≈18%) Domain 5: Data … Read more