222.6K
ECComment roadmap to get sent my free and complete data engineering roadmap!
@eczachly
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168.5K
HU🚀 Day 1: Noob to Pro Data Engineer 🚀
Started my journey today! 🔥 Learned about Apache Spark and how it helps solve the 3V problem (Volume, Velocity, Variety). Also compared Hadoop vs. Spark—turns out Spark is way faster! ⚡
💡 Key Takeaways:
✅ Spark processes data in-memory, making it much faster than Hadoop.
✅ Hadoop is great for batch processing, but Spark shines in real-time analytics.
✅ Practiced SQL on LeetCode & started working on my Azure Data Engineering project.
[Azure, cloud, learn, study, hardwork, consistency, hustle, motivation, job, employment, Microsoft azure, hadoop, dpark, daily vlog, daily study, unemployment, mnc, jio, corporate]
@hustleuphoney
34.7K
MRData Engineers work tirelessly behind the scenes to build the infrastructure for data projects. However, their efforts often remain invisible to business users, who focus on the end product and reward Data Scientists and Analysts with more recognition!
#dataengineering #azure #pyspark #dataengineer #azuredataengineer #data #aws #gcp #azuredatabricks #dataanalyst #datascientist #datascience
@mrk_talkstech
1.2M
MUSwitching from Testing to Data Engineering felt scary when I had no real experience. My experience was growing but not in the direction I wanted.
The longer I stayed, the more testing experience I was accumulating! And that scared me!
The biggest question I had was:
HOW do you justify a switch into Data Engineering when you haven’t worked on it professionally?
What worked for me was bridging the gap by showing the work.
- I didn’t wait for my job to give me hands-on data work (I might haven’t gotten any tbh)
- I built projects that reflected what Data Engineers actually do or atleast come close to it in someway.
Those projects did two things:
* helped my resume get shortlisted with the right key words and skills added to it
* showed interviewers that I genuinely wanted to work in data. And interviewers DO APPRECIATE IT!
If you haven’t gotten hands-on exposure at work, personal projects absolutely count.
That’s how I made the switch and it worked!
If you’re in testing or a service-based role and feeling stuck, switching roles is possible.
I’ll break down what kind of projects to pick and how to position them in the next post!
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[switching from testing to data engineering, testing to data engineer, career transition, service based company to product based company, accenture to data engineering, data engineer projects, working professional, learning data engineering while working, data engineer roadmap, microsoft data engineer]
#dataengineering #dataengineer #testing
@muskan.khannaa
61.9K
THData Engineer vs AI Engineer.
Here’s what each role does, what they earn, and how to choose.
What You Actually Do:
Data Engineer: Pipelines and reliability. Ingest, transform, model, validate. If data breaks, everything downstream breaks. Building data foundations that analytics, ML, and product teams rely on.
AI Engineer: Models in production. RAG systems, agent evaluations. If the model is slow, wrong, or unsafe, you fix it. Building AI features like chat, search, copilot, automations that users actually touch.
Languages You Use:
Data Engineer: SQL all day, Python for pipelines, Scala or Java for Spark.
AI Engineer: Python for model workflows, TypeScript or JavaScript for APIs, some SQL.
Tech Stack:
Data Engineer: Snowflake, BigQuery, Redshift, dbt, Airflow, Kafka, Databricks, Spark, Monte Carlo.
AI Engineer: OpenAI, Anthropic, Gemini, LangChain, LangGraph, Pinecone, Weaviate, Fireworks AI, Ragas, LangSmith, Weights & Biases.
Salary Ranges (NYC/SF):
Data Engineer: $140K-$200K base, $170K-$240K total comp
AI Engineer: $160K-$230K base, $200K-$300K total comp (higher at AI-first companies with equity)
Interested in data and building scalable systems? Data engineering.
Like AI and want to work with models in production? AI engineering.
@the.datascience.gal
1.1M
THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
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Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
@the.datascience.gal
306.0K
VEDATA ENG - 90 day prep resources
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{data engineering , resource , tech ,projects, internships, job search }
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#technology #trending #jobsearch #parttime #techconsulting #tech #hacks #behavioral #nodaysoff #veeconsistent #linkedin #emails #dataengineering
@vee_daily19
7.1K
SDComment 'Link' below if you want a free guide on how I got my first data analyst role ✨
--------------------------------------------------------------
YouTube channels for data engineers ✨
- Seattle Data Guy
- Data with Zach
- Andreas Kretz
- Gowtham (Data Engineering)
Who else belongs on this list?
@sdw.online
251.3K
ARData Engineering and Software Engineering Roadmap in under 30 seconds ft @shailjamishra__.
Data Engineer needs SQL, programming languages, data warehousing and cloud computing whereas Software Engineering needs DS Algos and System Design for interviews.
The average salary of both freshers be it 8-10 LPA.
The repositories for both projects were shown on the screen and both fields have a great scope.
Let me know in the comments - Data Engineering or Software Engineering?
* Stratascratch
Follow @arshgoyalyt and @shailjamishra__ for more.
#hiring #data #ai #coding #engineering #google
@arshgoyalyt
167.5K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude!
Here are the 3 types of projects you need:
↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables
↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one.
↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it
Save this video for later + send to a data friend!
@jessramosdata
548.6K
FIEp44- Stop learning everything!!
Are you learning everything in data analytics??
that’sthe biggest mistake and the reason people stay stuck with out getting a job.
Interviews don’t test random topics. They test specific skills. Right tools and project scenario based knowledge.
As an experienced data analyst with over 8 years of experience i have created a detailed pdf from my data analyst journey on which topics needs to be covered. Which needs to be ignored. How to prepare your own project based portfolio. Answer questions with right tools and skill. Below are the details included in pdf.
✔️ What to learn (and what to skip)
✔️ Skills interviewers actually ask
✔️ Role-wise roadmap (Fresher → Job ready)
✔️ Project clarity + interview direction
This is only for serious learners. Hence i made it as a paid one which costs a minimal fee.
Follow and comment EP-44. I’ll send you the link directly.
[data analytics, journey, road map, data analyst, jobs]
#dataanalyst #journey #roadmap #skills #growth
@fitwit_krish
1.6M
TEWant my full toolkit? ➡️💬 Comment “LINK” and I will share all the other websites that I use when I am doing DSA!
Kindly FOLLOW the account and then comment
LINK as instagram does not allow sending DMs to non-followers.
DSA doesn’t have to be boring text on a screen. 🙅♂️💻
If you are struggling to understand trees, graphs, or sorting algorithms, you need to see them in action. Visual learning > Rote memorization.
Follow @techninjaah to level up your dev journey! ✅
@techninjaah
✨ Guide de Découverte #Data Engineering
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