Theo

Today I'm launching my latest course "Modern Data Workflows with Databricks" on AnalystBuilder.com! Databricks is one of the most in-demand platforms in data right now, but getting started can feel overwhelming. In this course, I'm going to walk you through everything you need to know to actually work in Databricks. Things like: - Navigating the Databricks UI and understanding how everything connects - Building dashboards and sharing them with stakeholders - Creating ELT pipelines from raw data to clean, ready-to-use tables - Scheduling and automating your workflows with Jobs - Using AI features like Genie Spaces and AI Agents Whether you're trying to break into data engineering or you're an analyst looking to level up, this course will get you there! Check it out here: analystbuilder.com/courses/modern…

4 Hour AWS Masterclass for Data Engineering with Project - Ready to ship tomorrow ✅

When I was starting in data it only ETL - I never knew anything different. Now ELT is on the rise and I've been using it more and more myself - and it just makes more sense these days.

Three beginner-friendly analytics projects you can finish in an afternoon with Databricks Free Edition • Analyze a simulated environment using AI/BI Dashboards and AI_Query() • Explore sample trends and generate predictions with AI_Forecast() • Load open data with Python and explore it using SQL Each project uses public or sample datasets to build real dashboards and code you can showcase in your portfolio. Start building: databricks.com/blog/tutorial-…

I'm excited to share that today I'm officially changing my YouTube channel to Alex Does AI! As AI continues to take over every area of our lives I want to do more content on it exclusively and step away from Analytics focused content as that is mostly going to be in the past. I will do content on things like: - AI workflows for marketers - How to make AI agents attend meetings for you - Ranking the top 10 AIs most likely to replace you first - Automation tools that do all your work for you - Synthetic podcast co-host experiences - Training a custom GPT to think exactly like me (basically be me full-time) I’m also starting to automate my responses here on LinkedIn and over email, because helping people one by one is time consuming and not profitable. The automations will be trained on my videos, so in many ways, it will still be me. Alex The Analyst is out. Alex does AI is in.

> Amazon Data Engineering Interview Questions - SQL / Data Manipulation 1. Find the 2nd highest salary from a table 2. Identify duplicate records in a dataset. 3. Remove duplicates without using DISTINCT 4. Get top N records per group (e.g., top 3 users per country) 5. Calculate running totals (cumulative sum) 6. Find gaps in dates or missing records 7. Write a query to pivot/unpivot data 8. Find users who logged in consecutively for N days 9. Join multiple tables and handle NULL cases 10. Optimize a slow SQL query - Python / Scripting 1. Read multiple CSV files and merge them 2. Handle missing or corrupted data in a dataset 3. Transform nested JSON into tabular format 4. Implement data deduplication logic 5. Process large files efficiently (memory optimization) 6. Generators vs lists (difference and use case) 7. Write ETL logic in Python 3. ETL / Pipeline Design 1. Design a pipeline to ingest data from multiple sources 2. Handle late-arriving data in pipelines 3. Implement SCD Type 1 and Type 2 4. Ensure idempotency in ETL jobs 5. Design incremental vs full load strategy 6. Handle schema changes in pipelines 7. Orchestrate pipelines (Airflow concepts) 4. Data Warehousing 1. Difference between OLTP and OLAP 2. Star schema vs snowflake schema 3. Fact table vs dimension table 4. Design a data warehouse for an e-commerce platform 5. Partitioning and bucketing strategies 6. Data modeling for analytics queries 5. Big Data / Distributed Systems 1. Explain how Hadoop works (HDFS basics) 2. Spark vs Hadoop MapReduce 3. How Spark processes large-scale data 4. Partitioning in Spark and its impact 5. Handling data skew 6. Fault tolerance in distributed systems 6. System Design (Data-Focused) 1. Design a real-time data pipeline (clickstream processing) 2. Design a log ingestion system at scale 3. Build a recommendation data pipeline 4. Design a metrics/analytics backend 5. Streaming vs batch processing trade-offs 6. Data consistency vs latency trade-offs 7. AWS / Cloud 1. How S3 works internally 2. Redshift vs RDS vs DynamoDB 3. Design pipeline using S3 + Glue + Redshift 4. What is EMR and when to use it 5. IAM roles in data pipelines 6. Cost optimization strategies in AWS 8. Data Quality & Reliability 1. Validate data in pipelines 2. Detect anomalies in incoming data 3. Ensure data consistency across systems 4. Monitoring and alerting strategies 5. Handling pipeline failures and retries 9. Behavioral (Amazon Leadership Principles) 1. Handling large data under pressure 2. Optimizing a data pipeline 3. Working with ambiguous requirements 4. Ownership of a failed system 5. Trade-offs in system design 10. Advanced / Edge Cases 1. Schema evolution in streaming systems 2. Exactly-once vs at-least-once processing 3. Change Data Capture (CDC) implementation 4. Handling backfills in production pipelines 5. GDPR-compliant data deletion

If AI still feels a bit scattered, this helps bring it all together. This learning path focuses on Microsoft Foundry and covers: • Gen AI and agents • Text analysis, speech, and computer vision • Information extraction Start learning: msft.it/6015QxUDJ

In this week's lesson we are starting our Data Engineering in @databricks Series! We will be covering Ingesting Data, Transforming Data in ETL Pipelines, Automating and Scheduling, and we will end with a Full Project. youtu.be/hEv5y_s0L3c

Read this Google Cloud community article to learn about the Google Cloud Professional ML Engineer (PMLE) exam, how a Googler passed it in 30 days*, and how you can too → goo.gle/4bCOZQn Featuring her structured study plan covering all essential topics!

The Databricks Learning Festival is underway! Complete all modules in at least one self-paced learning pathway in Customer Academy by April 3 to earn: • 50% off any Databricks Certification • 20% off a yearly Academy Labs subscription Take advantage of this chance to build real skills in data engineering, analytics, machine learning, and GenAI. community.databricks.com/t5/learning-ev…

Data engineering is getting more complex, but it doesn't have to slow you down. The Big Book of Data Engineering is a practical guide packed with how-tos, code snippets, and real-world examples to help you build and scale pipelines faster and deliver high-quality data for AI, BI, and analytics workloads. Inside: - Patterns for scaling ETL pipelines effectively - Orchestrating data, analytics, and AI workloads - Implementing observability for your data pipelines - Using Lakeflow to manage pipelines databricks.com/resources/eboo…

I pray this for your family 🙏

STOP grinding random LeetCode problems for your Data Engineering interview. There's a better way. I built a structured coding interview roadmap on DataVidhya. 11 steps. 104 problems. SQL + Python + PySpark. And a big chunk of it? Completely FREE. Here's the full path from zero to interview-ready: 🗄️ SQL (52 problems) 1️⃣ SQL Basics (11 lessons) → SELECT, WHERE, ORDER BY, data types, how relational databases work 2️⃣ SQL Fundamentals (10 lessons) → JOINs, GROUP BY, subqueries, CTEs, CASE, NULL handling, DML 3️⃣ SQL Window Functions & Advanced (9 lessons) → Window functions, date/time, string functions, views, casting 4️⃣ SQL Interview Patterns (12 patterns) → The 12 patterns that show up in 90% of DE SQL interviews 5️⃣ SQL Practice Problems (10 problems) → Solve coding problems directly on the platform 🐍 Python (21 problems) 6️⃣ Python Fundamentals (11 lessons) → Data structures, functions, OOP, Pandas 7️⃣ Python for Data Engineers (10 lessons) → APIs, generators, decorators, testing, packaging ⚡ PySpark (26 problems) 8️⃣ PySpark Fundamentals (8 lessons) → DataFrames, transformations, aggregations, joins, window functions 9️⃣ PySpark Advanced & Optimization (10 lessons) → Partitioning, caching, UDFs, Spark UI, structured streaming, data skew 🔟 PySpark Interview Patterns (8 patterns) → The exact patterns interviewers test for Spark proficiency 🎯 Final Step Review & Mock Interview → Most commonly asked questions + full mock interview simulation No more random problem-solving with no direction. No more wondering, "Am I even studying the right things?" Just a clear path. Step by step. Basics to advanced. Learning to practice for a mock interview. Start for free. Upgrade when you're ready to go deeper.

while True: chase_dreams = True while chase_dreams: learn_something_new() take_calculated_risks() if obstacles: overcome_them() success = achieve_milestone() if success: celebrate()

Learning Strategies That Actually Work in 2026; 1. Project-Based Learning is Non-negotiable. Tutorials give you false confidence. You need to struggle through problems without a guide. Start building on day one, even if your code is messy. The gap between “I

Dear Data Analyst!! 5 FREE websites to practice SQL today: HackerRank — hackerrank.com LeetCode — leetcode.com Mode Analytics — mode.com SQLZoo — sqlzoo.net StrataScratch — stratascratch.com Bookmark all 5. Practice 30 minutes daily for 30 days. You will be a different analyst. Want the full roadmap? Get DATA ANALYSIS MADE EASY: selar.co/bpr5

@clcoding Data.

Most people collect data… Few turn it into insights. In just 10 lines of Python, you can: • Create datasets • Analyze trends • Add logic • Visualize results Stop guessing. Start understanding 📊 Reply “DATA” and I’ll share more like this.

SQL Data Warehouse from Scratch | Full Hands-On Data Engineering Project youtube.com/watch?v=9GVqKu…

Learning Linux for data engineering @LuxDevHQ is practical and engaging. Built real pipeline workflows hands-on. Highly rewarding experience. @HarunMbaabu

@parmardarshil07 When are you going live??

We are coming up with an ultimate interview platform for Data Engineers You are not ready for this!