222.6K
ECComment roadmap to get sent my free and complete data engineering roadmap!
@eczachly
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#Data Engineering
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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
7.1K
SDComment 'Link' below if you want a free guide on how I got my first data analyst role ✨
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YouTube channels for data engineers ✨
- Seattle Data Guy
- Data with Zach
- Andreas Kretz
- Gowtham (Data Engineering)
Who else belongs on this list?
@sdw.online
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
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
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
248.8K
MUYou DO NOT need to learn everything to become a Data Engineer.
People often prepare for mid-level roles while applying for entry-level roles.
Here’s what actually mattered for me in the beginning when switching from testing to a data engineer role.
1. SQL(non-negotiable): You’ll need to know the basics and complexities of sql along including subqueries and window functions.
If you’re not strong in SQL, you won’t be able to move forward in interviews.
2. Python concepts basics like lists, dictionaries, sets and basic problem solving.
You can solve questions in other languages too but I’d suggest Python as it’s easy to learn.
You don’t need hardcore DSA for most entry-level Data Engineering roles, but DSA is definitely important.
3. Data warehousing concepts like facts vs dimension, star vs snowflake schema, SCD Type 1,2 etc.
Understanding concepts and what data warehousing is and why it’s there mattered more than tools.
4. ETL and data pipeline understanding. How data is extracted, transformed, loaded is the CORE of Data Engineering.
You don’t need spark understanding in the beginning, just the understanding of how data flows in and out.
5. System design basics, not like design twitter/uber. Simple understanding of how data moves end to end and overall understanding of data eco-systems.
No deep design is expected at entry-level.
6. Pick any one cloud. Don’t chase all clouds, just any one cloud and cover its basics because you’d most likely be working on some cloud in your work.
I moved from Testing to Data Engineering by focusing on these basics, instead of trying to learn every other tool out there, and it is still the very core of Data Engineering which one must know to crack interviews.
Save this if you’re planning to make a switch into Data Engineering.
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[data engineering roadmap, entry level data engineer preparation, switching to data engineering, testing to data engineering, data engineer interview preparation, sql for data engineering, python basics for data engineer, data engineers for beginners, microsoft data engineer]
#dataengineer #dataengineering
@muskan.khannaa
6.6K
LELife of a Data Engineer😂
#learnomatetechnologies #learnomate #explorepage #reels #reelkarofeelkaro #trending #officereels #explore #foryou #corporatelife #dataengine #dataengineering #techmeme #funnytech #engineerhumor #relatable
@learnomate
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
166.9K
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
384.3K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
214.9K
ITHow I’d become a Data Analyst in 2026 ⬇️
1️⃣ Get in the door (any role)
Data Analyst titles are hard to land, degree or not.
So get into any role at a tech forward company with an analytics team/department .
Sales. Ops. Data entry.
Work up! Prove your value.
That’s exactly what I did.
2️⃣ Improve what’s in front of you
Look for small things you can control:
• Excel
• MS Access
• Power Query
Invoices research (ms access), trends, reports doesn’t matter, anything YOU can do.
3️⃣ Learn only what you need
Target the tools you’re already working with/access too.
(DataCamp and Codecademy worked for me)
4️⃣ Build something real
Not tutorials. Build a tool people (and you) actually use even if it’s simple. Examples could be: Using forms and VBA/SQL in ms access to build a form for people to researching invoices!
5️⃣ Show your work
Demo it. Explain the impact. Who uses it. Why it matters. And how it helps!
6️⃣ Say yes to opportunities
Take on EVERYTHING, prove you can do the work, even if it adds more stress.
That’s how you stack proof for the next role.
No degree required.
👉 Follow if you’re breaking into data.
#dataanalyst #howto #breakintotech #nodegree #2026goals
@itssimplyjordan
✨ #Data Engineering発見ガイド
Instagramには#Data Engineeringの下に621K件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
ログインせずに最新の#Data Engineeringコンテンツを発見しましょう。このタグの下で最も印象的なリール、特に@the.datascience.gal, @chrisoh.zip and @vee_daily19からのものは、大きな注目を集めています。
#Data Engineeringで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @the.datascience.gal, @chrisoh.zip, @vee_daily19などがコミュニティをリード
#Data Engineeringについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Data Engineeringのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均518.7K回の再生（平均の2.1倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
🔥 #Data Engineeringは高いエンゲージメント可能性を示す - ピーク時に戦略的に投稿
📹 #Data Engineeringには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長814文字
✨ 多くの認証済みクリエイターが活動中（58%） - コンテンツスタイルを研究
#Data Engineering に関連する人気検索
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Data Engineering ReelsData Engineering動画を見る
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Data Engineeringを探索#data engineering certification programs#data engineering tamil#data engineer road map#data scientist vs data engineer#data engineer skills in demand#engine#data engineering career paths#data engineering trends 2026