Learn Data Engineering

The wrong database choice can kill your project! You start building a pipeline, and then BOOM: ? Queries crawl because the DB isn’t built for analytics ? Writes lag because of the wrong storage engine ? Scaling is a nightmare because trade-offs weren’t considered Pick the wrong store, and you’ll be stuck fixing performance issues instead of building cool stuff. Been there? Me for sure ?? That’s why I put together my Choosing Data Stores?Academy course! You’ll learn: ??OLTP vs. OLAP?– When to go transactional vs. analytical ??ETL vs. ELT?– Why storage decisions impact pipeline design ??Relational vs. NoSQL?– ACID compliance or schema flexibility? ??Data Warehouses vs. Data Lakes?– When to use what If you want to?avoid painful migrations and costly mistakes,?this training is for you! ?? ???Find the full course in my Academy, trusted by over 2,000 students ???https://lnkd.in/eQ2TiYqA +++ This and 30+ other courses and hands-on projects are available in my Data Engineering Academy. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now: https://bit.ly/3CSvWmc This way, we're celebrating our new Academy features, including step-by-step roadmaps for beginners, Scientists, and more! P.S.: For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts April 28th. Check it out here to join: https://bit.ly/4f67pss +++

Most people learn SQL for analysis. But Data Engineers? They need to master SQL for building and optimizing pipelines. Here's your roadmap to do it like a Data Engineering pro: Step 1: Database Foundations ? Understand?how relational databases store data ? Learn?SQL’s role in data engineering workflows Step 2: Writing Solid Queries ? Master?SELECT, WHERE, GROUP BY & JOIN?operations ? Use?subqueries & Common Table Expressions (CTEs)?for cleaner, more efficient queries (?? Struggling with JOIN statements? Check out the cheat sheet below ??) Step 3: Transactions & Data Integrity ? Learn?ACID properties?& how they impact reliability ? Use?Transaction Control Language (TCL)?to maintain data consistency Step 4: Performance Optimization ? Understand?indexing strategies?& how they speed up queries ? Identify & fix?slow queries?to optimize performance Step 5: Advanced SQL Mastery ? Use?window functions?for ranking & analytics ? Write?complex, high-performance queries?for large-scale datasets SQL isn’t just about writing queries. It’s about designing efficient, scalable data workflows. That’s exactly what I cover in?my hands-on course SQL for Data Engineers, where I walk you through this roadmap?step by step, so you can write?efficient, production-ready SQL?like a pro. ???Full course link in the comments! Where do you struggle most when it comes to SQL? Share your experiences and let's help each other. +++ This and 30+ other courses and hands-on projects are available in my Data Engineering Academy. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now: https://bit.ly/3CSvWmc This way, we're celebrating our new Academy features, including step-by-step roadmaps for beginners, Scientists, and more! P.S.: For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts April 28th. Check it out here to join: https://bit.ly/4f67pss +++

The feature of using Kibana dashboards connected with Elasticsearch is a big upside for log monitoring and also exploration. I used this with Coaching students before who implemented this in their workflow at work with great results.?That's why I built the course Log Analysis and Monitoring with Elasticsearch so you can?do this too! In this training, you get to know what Elasticsearch is, what makes it such a great tool and how you can use it efficiently. You learn in our hands-on part how to write events to Elasticsearch, and how to search and create dashboards with Kibana ?? ?? Want to get a head start? Dive into this Elasticsearch course in my Academy, trusted by over 2,000 students ???https://lnkd.in/e5WBkycx Here, you'll get into: Fundamentals ? Understand the Elasticsearch fundamentals vs relational databases ? What are ETL log analysis & debugging problems ? What are streaming log analysis & debugging problems ? Learn how to solve these problems with Elasticsearch Elasticsearch Hands on ? Get to know the ELK stack ? Setup Elasticsearch with docker & limit RAM ? Run Elasticsearch locally ? Understand Elasticsearch APIs & creating an index with Python ? Write logs (JSON) to Elasticsearch Analyzing logs with Kibana ? Create Kibana visualizations & dashboards ? Analyse logs by searching Elasticsearch index This and 30+ other courses and hands-on projects are available in my Data Engineering Academy. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now:?https://bit.ly/3CSvWmc This way, we're celebrating our new Academy features, including step-by-step roadmaps for beginners, Scientists, and more! P.S.: For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts March 17th. Check it out here and join:?https://bit.ly/4f67pss #dataengineering #learndataengineering #elasticsearch #kibana

Identifying risks and analyzing their impact helps you focus on what really matters! Because let’s be honest: Not every issue is a disaster. Think about it: A user gets an?"access denied"?error. Annoying, but not the end of the world. Outdated data? Frustrating. Might lead to bad reports, but usually fixable. Now compare that to this: Your database goes offline. Suddenly, nothing works. A schema change breaks your pipeline, and you’re firefighting for hours. ?? That’s why we?prioritize risks?based on?likelihood?and?severity: ???Likelihood (1-5)?– How often does this happen? ???Severity (1-5)?– How bad is it if it does? ???Try it out yourself.?In my course?"Becoming a Better Data Engineer", I walk you through risk assessments step by step—so you can spot risks early, rank them properly, and keep your projects running smoothly. And this is just a small part of the course! After that, I talk about mitigation strategies that can help you mitigate the risks you found. There's much more to learn about designing and optimizing data pipelines projects. I’ll guide you through proven strategies to handle all the Data Engineering challenges out there effectively. Check out the full course here:?https://bit.ly/3CJN7qd What’s a risk in your data pipeline that you underestimated until it became a real problem? Share your experience in the comments ?? ++++ P.S.: This course is part of my Data Engineering Academy, where you have access to 30+ individual courses and hands-on projects to work through. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now: https://bit.ly/3CSvWmc For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts next Monday, March 17th! Check it out here and join: https://bit.ly/4f67pss ++++

Handling real-time data? Make sure your pipeline is built for it. Unlike traditional databases, time series data plays by different rules—it’s all about constant updates, fast queries, and time-based analysis. If your pipeline isn’t built for it, you’ll hit performance bottlenecks FAST. Here’s how to set it up the right way: ? Pick the right DB –?InfluxDB > relational DBs for time series ? Design for time-based queries – Use?tags & fields wisely ? Ingest data from all sources –?API + historical CSVs?for full context ? Optimize queries – Flux makes slicing & analyzing time windows easy ? Visualize it –?Grafana dashboards?make insights interactive I built a hands-on "Storing & Visualizing Time Series Data" project that walks you through all this step by step. If you’re tired of static reports and want real-time, high-frequency insights, check it out! ?? ?? Link to the full course in the comments!

Bad queries? Slow reports? Data all over the place??If your data isn’t structured properly, you’re setting yourself up for frustration. That’s why?Dimensional Data Modeling?is so important. It helps you design your data warehouse the right way so you get?fast queries, clean reporting, and analytics that actually make sense. In my Academy course "Dimensional Data Modeling", we show you?how to model data properly?with fact and dimension tables, slowly changing dimensions, and different types of fact tables. And we’re not just talking theory! You also get hands-on experience with?DuckDB and DBeaver?to set up your own data warehouse. By the end, you?understand how to structure data for high-performance reporting?and be confident working with modern data warehouse tools ?? ?? Want to get a head start? Find the link to this complete training in the comments below! Work through these topics: Introduction ? Course Goals ? Intro to Data Warehousing Dimensional Modeling Basics ? Approaches to building a data warehouse ? Dimension tables explained ? Fact tables explained ? Identifying dimensions Data Warehouse Setup ? What is DuckDB ? First DuckDB hands-on ? Creating tables in DuckDB ? Installing DBeaver Working With The Data Warehouse ? Exploring scd0 and scd1 ? Exploring scd2 ? Exploring transaction fact table ? Exploring accumulating fact table This and 30+ other courses and hands-on projects are available in my Data Engineering Academy. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now:?https://bit.ly/3CSvWmc This way, we're celebrating our new Academy features, including step-by-step roadmaps for beginners, Scientists, and more! P.S.: For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts March 17th. Check it out here and join:?https://bit.ly/4f67pss #dataengineering #datamodeling #duckdb #dbeaver #datawarehouse?

Check Out This 14-Week Data Engineering Roadmap for Data Scientists! ?? With this perfect learning path, ?I put everything together that you need as a Data Scientist to either become a Data Engineer or use Data Engineering in your job. Have fun! #dataengineering #datascientist #dataengineeringroadmap

I'm really excited here to see how many people from the Data Science side are actually using Databricks because I feel like this is more of a platform for Engineers to make data available for Analytics.?And this is super easy by the way. You can put the data in the data lake and then make it available to Data Analysts through tables. I'm going to show you this and many other cool stuff in my course Databricks for Data Engineers. ?? Find the link to the course in the comments below! Work through these topics: Databricks Setup ? Create your Databricks account & workspace ? Understand the AWS resources created by Databricks ? Experience the Databricks UI & Compute Cluster Data & Code ? Take a simple dataset like an e-commerce one ? Set goals for an ETL & visualization pipeline ? Import data in Databricks UI ? Find the Databricks data in S3 ? Create code repos Processing & Visualization ? Run your ETL job ? Explore data tables in AWS folders ? Explore data with Databricks notebooks and much more! This and 30+ other courses and hands-on projects are available in my Data Engineering Academy. And now is the best time to join: We expanded our?20% discount on all Academy memberships for the whole of March! ???Join the Academy with 20% off now:?https://bit.ly/3CSvWmc This way, we're celebrating our new Academy features, including step-by-step roadmaps for beginners, Scientists, and more! P.S.: For those who are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts March 17th. Check it out here to join:?https://bit.ly/4f67pss #dataengineering #learndataengineering #databricks #ETL

So many of you found last week's discount super helpful - so we're extending it for the whole month of March!! ?? If you’ve been thinking about?getting into Data Engineering, now’s the perfect time! Join my Academy with 20% off the annual or unlimited access. Use code "ROADMAPS2025" at checkout! ???https://bit.ly/3CSvWmc We’ve built?step-by-step roadmaps?for you so you always know?what to learn next.?No more confusion, just?follow your path and level up! Pick your roadmap & get started: ???11-Week Beginner Roadmap?– Go from zero to Data Engineering? ? Learn Python & SQL from scratch ? Understand databases & data storage ? Work with real data pipelines ? Get hands-on with cloud platforms (AWS, GCP, Azure) ? Build your first real-world Data Engineering project ???10-Week Analyst Roadmap?– Move from dashboards to real data pipelines? ? Learn SQL & database optimization ? Understand data ingestion & transformation ? Work with Apache Airflow & message queues ? Automate reporting & ETL processes ? Build scalable data solutions ???12-Week Software Dev Roadmap?– Transition into Data Engineering? ? Master data processing frameworks (Spark, Kafka) ? Learn to work with batch & streaming data ? Understand data modeling & warehouse design ? Deploy pipelines with Docker & Kubernetes ? Optimize data for analytics & ML ???14-Week Data Scientist Roadmap?– Build reliable ML pipelines? ? Learn MLOps & data pipeline automation ? Work with feature stores & model deployment ? Optimize data for ML & AI ? Scale your workflows with cloud & distributed computing ? Make your ML models production-ready For further details, check out all individual roadmaps here:?https://bit.ly/3Dg4xLu Now tell me –?how long do you think it takes to go from beginner to Data Engineer???? Vote in the poll??? Let’s see what you guys think! P.S.: If you are looking for?more personalized 1:1 guidance, the next cohort of my?Data Engineer Coaching program?starts March 17th. Check it out here and join:?https://bit.ly/4f67pss #dataengineering #learndataengineering #dataengineeringroadmap #dataengineercoaching

How do you build an?IoT-ready data pipeline?that?automates data collection, processing, and reporting - all without managing servers? I came across this AWS setup that uses?Lambda, Redshift, S3, EventBridge, and CloudFront?to create a?fully serverless, event-driven data lake?that scales effortlessly! I took a deep dive into this next-level architecture ?? Here’s my?reaction & breakdown?of how it all works! ???Watch here????https://bit.ly/43G8fZo #AWS #CloudComputing #Serverless #DataEngineering #IoT #EventDriven