DataVidhya

practice data infra on cloud here - datavidhya.com/labs

You need only one lab to master Microsoft Fabric Nothing else! datavidhya.com/labs/

Master Data Infra here - datavidhya.com/labs

One place to master everything about Azure Data Engineering @thedatavidhya

10 Data Engineering Projects You Should Build • Data Lake → S3 + Glue Crawler + Athena • Lakehouse → Databricks + Delta Lake + Unity Catalog • ELT Pipeline → Airbyte + Snowflake + dbt • Real-Time Streaming → Kafka + Spark Structured Streaming + Delta • Medallion Architecture → Bronze / Silver / Gold on Databricks • Workflow Orchestration → Airflow + dbt + Snowflake • CDC Pipeline → Debezium + Kafka + Iceberg • Big Data Processing → EMR + Spark + S3 • Data Quality & Observability → Great Expectations + dbt tests + Slack alerts • End-to-End Analytics → Fivetran + BigQuery + dbt + Looker Don't just learn tools. Connect them. Move data through them. Prove you can build pipelines.

If You’re Learning Spark in 2026, Read This First... Most people learn Spark like this: -> Here’s read.parquet() -> Here’s groupBy() -> Here’s join() -> Here’s write.format("delta") And then the first time their job runs for 3 hours, they stare at the Spark UI like it’s a cockpit. Here’s the thing: Spark isn’t hard because the API is hard. Spark is hard because it’s a distributed system that pretends to be a Python library. So instead of starting with APIs, let’s build the mental model that makes Spark feel obvious. Today we’ll nail 5 ideas: 👉🏻 DAG 👉🏻 Shuffle 👉🏻 Partitioning 👉🏻 Catalyst 👉🏻 Memory I have published a newsletter that will teach spark more than any $1000 course ever will Read the entire newsletter here: blog.datavidhya.com/p/if-you…

If you want to learn cloud data engineering and have a fear of a $1000 bill, read this. The thing that stops people is not the concepts. It is the credit card. So we built @thedatavidhya CloudLabs. Real AWS. Sandbox account, data already loaded, disappears when you are done. No card, no bill. 10 labs live tomorrow: S3 data lake, Athena, Glue crawler, Glue PySpark, Glue Studio, Lambda, S3 events, SQS, lifecycle rules, Iceberg lakehouse. That is not a cloud course. That is the job. ------ Start here: datavidhya.com We are running a limited-time 50% offer for Independence Day 🇮🇳 USE CODE - FREEDOM50 at checkout (this is a limited-time offer valid till tomorrow, 15th Aug 2026, midnight)

I spent 6 hours reading Netflix's data engineering blog, so you don't have to 2 petabytes of new data every week. The storage bill is going up 50% every single year. The generic answer is: get more storage and compute But Netflix handles it differently. Now here is what confused me the first time I read it. Netflix runs hundreds of petabytes on S3. Their queries still come back. Their bill did not go to the moon. So what did they actually do? I read six of their engineering posts this week. Not summaries. The actual posts. Netflix did not find a cheaper cloud. They stopped paying for work nobody asked for. That is the whole thing. Four leaks, four fixes. Read the complete blog here: blog.datavidhya.com/p/how-do…

I spent 8 hours learning dbt, so you don't have to I spent a workday reading the dbt docs, the Snowflake performance guides, and the BigQuery partitioning reference to find out where the minutes actually go. The answer was uncomfortable, because it isn’t a dbt problem you can fix with a dbt flag. dbt is a compiler and a scheduler. It renders your Jinja into plain SQL text, works out an order, opens a few connections, and sends the text down the wire. Then it sits there. That reframe matters because it changes what you go looking for. You stop tuning dbt and start reading query plans So a slow project is one of four things, and all four are warehouse-side: ✅ You picked the wrong materialisation for the model’s size. ✅ Your incremental strategy is scanning far more than it needs to. ✅ Your DAG shape forces the same work to happen more than once. ✅ Your tests are quietly running hundreds of full-table queries. Read the complete newsletter here -blog.datavidhya.com/p/i-spen…

Become an AI Data Engineer with just one video ⬇️ Here's what you'll build: 1. RAG-based chat application to talk with the dataset 2. Sentiment analysis of user feedback 3. Text-to-SQL to chat with a data warehouse Not only this, but you will also learn how to build a complete data pipeline Zomato Dataset→ S3 → Snowflake. Bronze, Silver, Gold, cleaned and modelled with dbt. Orchestrated with Airflow. Served with Streamlit. This 5 Hour video will teach you more than a $ 1,000 paid course; go check it out, IT'S FREE! Find the video - youtube.com/watch?v=kYwaNMQ3…

FREE Zomato AI Data Pipeline | End-To-End AI Data Engineering Project (5 Hours) How does AI work in Data Engineering? This project will teach you the major things you need to know. 1/ The foundation Zomato Dataset→ S3 → Snowflake. Bronze, Silver, Gold — cleaned and modelled with dbt. Orchestrated with Airflow. Served with Streamlit. 2/ LLM as a transform step Enriching raw reviews into structured insights, landing in a mart like any other model. 3/ RAG on your own data Reviews → embeddings → vector store. Ask a question, get a grounded answer with sources. Your pipeline can now explain itself. 4/ Text-to-SQL with guardrails Chat with your warehouse in plain English. This is the difference in 2026: ❌ Everyone can move data from A to B. ✅ Very few can build a pipeline where AI is integrated with security and governance to match. Tag someone who might be interested and comment your thoughts below ⬇️ Watch here - youtu.be/kYwaNMQ3XT8?si=aA0y…

AI tools I use as a Data Engineer 🤖 Here's my stack: 🔹 GitHub Copilot → SQL, Python, PySpark, dbt - writes 40% of my code. 🔹 Claude → Data architecture reviews, pipeline design, debugging messy logic. 🔹 ChatGPT → Documentation, ETL logic, runbooks, and pipeline explanations. 🔹 Cursor → Refactoring data pipelines, scripts, and full project codebases. 🔹 Amazon Q → AWS Glue, Redshift, Athena, S3 troubleshooting. 🔹 Snowflake Cortex → AI-powered SQL, data analysis, and building LLM apps inside Snowflake. 🔹 Databricks Genie → Asking questions on business data using natural language. 🔹 dbt AI Assistants → Model documentation, lineage, tests, and SQL cleanup. 🔹 k8sgpt → Debugging Airflow, Spark, and Kubernetes-based data workloads. I use these tools as per the need; a lot of the time, I use Claude Code for most of my coding and jump between Codex and Cursor What do you use on a day-to-day basis? Let me know ⬇️

6 datasets that will actually teach you Data Engineering (real production-scale problems, not clean 500-row CSVs) 👇 Every beginner practices on the Titanic dataset. Then they hit a real job and see 2 billion rows, messy formats, and access controls. These 6 datasets close that gap: ☝️ 𝗡𝗬𝗖 𝗧𝗟𝗖 𝗧𝗿𝗶𝗽 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 🚕 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘣𝘪𝘨 𝘥𝘢𝘵𝘢 𝘣𝘢𝘴𝘪𝘤𝘴 → Billions of taxi trips, already in Parquet → Practice partitioning, incremental loads, and Spark jobs → The best first "my laptop can't handle this" moment → nyc.gov/site/tlc/about/tlc-t… (also on the AWS Open Data registry) ✌️ 𝗜𝗻𝘀𝘁𝗮𝗰𝗮𝗿𝘁 𝗠𝗮𝗿𝗸𝗲𝘁 𝗕𝗮𝘀𝗸𝗲𝘁 🛒 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘥𝘢𝘵𝘢 𝘮𝘰𝘥𝘦𝘭𝘪𝘯𝘨 → Real e-commerce tables: orders, products, users → Perfect for joins, star schemas, and dbt practice → Search "Instacart" on kaggle.com/datasets 🤟 𝗡𝗢𝗔𝗔 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮 (𝗚𝗛𝗖𝗡 / 𝗜𝗦𝗗) 🌦️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘤𝘭𝘦𝘢𝘯𝘪𝘯𝘨 𝘮𝘦𝘴𝘴𝘺 𝘥𝘢𝘵𝘢 → Decades of weather readings from stations worldwide → Weird formats, missing values, quality flags — like real company data → Search GHCN or ISD on NOAA's data portals 🖖 𝗔𝗪𝗦 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 ☁️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘤𝘭𝘰𝘶𝘥-𝘴𝘤𝘢𝘭𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 → Common Crawl (the web), Landsat (satellites), OpenStreetMap → Read directly from S3 — no downloads, just like production → registry.opendata.aws ✋ 𝗠𝗜𝗠𝗜𝗖-𝗜𝗜𝗜 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 🏥 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝘸𝘰𝘳𝘬𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘴𝘦𝘯𝘴𝘪𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢 → Real (de-identified) hospital records → You sign an access agreement first — exactly how regulated industries work → Search "MIMIC-III PhysioNet" 🤙 𝗡𝗬𝗖 𝟯𝟭𝟭 / 𝗖𝗶𝘁𝘆 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏙️ 𝘛𝘦𝘢𝘤𝘩𝘦𝘴: 𝗔𝗣𝗜 𝘪𝘯𝘨𝘦𝘴𝘵𝘪𝘰𝘯 → Millions of service requests, updated daily → Build a pipeline that pulls fresh data on a schedule — a real portfolio project → Search "311" on data.cityofnewyork.us (DataSF and other cities work too) My advice: don't collect all 6. Pick ONE. Build ONE pipeline end to end. Put it on GitHub. One finished project beats six downloaded datasets. Which dataset did I miss?