What Makes Data "AI Ready"?

Module 2-Data Preparation and Feature Engineering- Data imputation

Module 2-Data Preparation and Feature Engineering- Fundementals

Module 1-Data Preparation for Machine Learning: Load and Explore Data- DEMO

Module 1-Data Preparation for Machine Learning: Managing and Exploring Data

Databricks Certified Machine Learning- Associate: #1 Introduction

Bullet proof your data with 6 Data Quality dimensions

Databricks Delta Live Tables(DLT) with Demo

Databricks Architecture Center | Best Practices & Reference Architectures

Databricks Jobs- Create Jobs- DEMO

Databricks AI/BI Genie-Introduction

Microservices and Data Engineering: How They Work Together

Batch vs Stream Processing Explained | Real-Time vs Scheduled Data Processing

Big Data Battle: Lambda vs Kappa ⚔️ | Real-Time Architectures Explained with Use Cases

Databricks Free Edition is HERE! Build AI, SQL & Data Pipelines – ABSOLUTELY FREE!”

Databricks Lakebase | The Future of Databases for AI and Real-Time Apps

Spark 4.0 Features Demystified

5 levels of building data pipelines | Noob to Ninja

Databricks vs Snowflake | The Ultimate Showdown

Data Mesh - The Modern Data Platform

Data As a Product- DaaP - Modern Data Platform

Data As a Service - DaaS - Modern Data Platform

Idempotent Data Load

Data Loads Strategies- Full vs Incremental vs Differential vs CDC Load

DP 700: Section 1: Introduction-Topic #4- About the Exam and Skills Measured

Data Archival Tools and Technologies

Day #3 of Microsoft FABRIC DATA ENGINERING (DP-700): Microsoft Fabric Free Account for Practice

Data Deletion vs Data Purging

Data Backup vs Archival

Day #2 of Microsoft FABRIC DATA ENGINERING (DP-700): Introduction

Day #1 of Microsoft FABRIC DATA ENGINERING (DP-700): Why LEARN Microsoft FABRIC DATA ENGINERING???

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Meet Luo Fuli: The Genius AI Prodigy who did Biggest AI Disruption

Deep Seek- First Impression of using it

SQL Functions for Data Cleaning | SQL for Data Professionals |Topic #23

COALESCE function in SQL | SQL for Data Professionals |Topic #22

TEMP Tables vs Common Table Expressions(CTEs) in SQL | SQL for Data Professionals |Topic #21

TEMP Tables in SQL with Practical Use Cases | SQL for Data Professionals |Topic #20

EXISTS Operator in SQL | SQL for Data Professionals |Topic #19

WHERE 1=1, Dummy condition, Placeholder in SQL | SQL for Data Professionals |Topic #18

Recursive Common Table Expressions(CTEs) in SQL | SQL for Data Professionals |Topic #17

Performance optimization using Sharding in SQL | SQL for Data Professionals |Topic #16

Clustered vs Non Clustered Indexed in SQL | SQL for Data Professionals |Topic #15

JSON Data Parsing using SQL | SQL for Data Professionals |Topic #14

CSV Data Parsing using SQL | SQL for Data Professionals |Topic #13

XML Data Parsing using SQL | SQL for Data Professionals |Topic #12

WHAT are SQL Indexes? Pros and Cons of using them | SQL for Data Professionals |Topic #11

Disassemble Python bytecode into a human-readable form | Topic #24

SQL-SUB-QUERIES vs JOINS | SQL for Data Professionals |Topic #10

SQL window function in detail with examples| ROW_NUM(),RANK(), DENSE_RANK(),ROLLING SUM()|Topic #9

SQL Complex Aggregate Function with examples | SQL for Data Professionals | Topic #8

SQL Keys and their Purpose with Examples | SQL for Data Professionals | Topic #7

Python List Comprehension In great detail with examples | Topic #23

Lists vs Sets vs Dictionary vs Tuple: Performance Comparison and Analysis | Topic #22

COUNT(*) vs COUNT(1) in SQL | SQL for Data Professionals | Topic #6

SQL Group By and Having| SQL for Data Professionals | Topic #5

SQL Joins- Simplified|INNER JOIN, LEFT JOIN, RIGHT JOIN, OUTER JOIN, CROSS JOIN,SELF JOIN | Topic #4

DCL(Data Control Language) | SQL for Data Professionals | Topic #3

DML(Data Manipulation Language) | SQL for Data Professionals | Topic #2

Lists vs Sets vs Dictionary vs Tuple- with Real time Use Cases | Topic #21