#Databricks Learning

شاهد 1K فيديو ريلز عن Databricks Learning من أشخاص حول العالم.

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#Databricks Learning Reel by @hustleuphoney - 🚀 Day 3 of Learning Databricks!✨️ Today, I explored what Databricks is and why we actually need it. It's not just another tool - it's a unified pla
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HU
@hustleuphoney
🚀 Day 3 of Learning Databricks!✨️ Today, I explored what Databricks is and why we actually need it. It’s not just another tool – it’s a unified platform that brings together data engineering, analytics, and AI in one place. • Key features I discovered today: • Manage scalable clusters with ease • Collaborate in powerful notebooks • Use SQL Warehouse for direct querying • Build automated ETL workflows • Set up alerts for monitoring jobs • Even run ML & AI workloads seamlessly Why Databricks? Because it replaces 4–5 different tools and gives you one ecosystem to handle everything – saving time, cost & effort! This was my Day 3 learning Tomorrow, I’ll dive deeper into its components – stay tuned for Day 4 💻 . . [Inspiration, motivation, corporate, job, morning, unskilled, employment, unemployment, corporate girlie, dataengineer, womenintech, science, ai, data scientist, hardwork, work, employment, study, switch, jio, reliance, study, learn, mumbai]
#Databricks Learning Reel by @viktoria.semaan (verified account) - Learning Databricks from scratch? Here's your roadmap. Follow these 4 steps: 1️⃣ Build foundations → Free courses on Databricks Academy (earn digital
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@viktoria.semaan
Learning Databricks from scratch? Here’s your roadmap. Follow these 4 steps: 1️⃣ Build foundations → Free courses on Databricks Academy (earn digital badges for LinkedIn) 2️⃣ Get hands-on → Databricks Free Edition gives you full platform access. Forever free. No credit card needed 3️⃣ Build a real project → Pick what excites you: AI agents, data pipelines, or streaming analytics. Use docs and sample notebooks to get started. 4️⃣ Join the community → 50K+ practitioners ready to help when you get stuck. 🔗 Comment ROADMAP and I’ll send you all links. Save this for later. Share with your team! —— Hi 👋 I’m Viktoria, Al Engineer and Principal Technologist at Databricks. I share practical Al tips and resources. Follow for more educational content! #Databricks #TechCareer #AI #Roadmap
#Databricks Learning Reel by @meet_kanth (verified account) - Databricks vs Microsoft Fabric for Data Engineering. 🚀🚀 Latest Syllabus on Azure Data Engineering Training Program with placements Our Placement-F
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@meet_kanth
Databricks vs Microsoft Fabric for Data Engineering. 🚀🚀 Latest Syllabus on Azure Data Engineering Training Program with placements Our Placement-Focused Curriculum with Portfolio Building courses helps you build a strong portfolio to bridge the gap between industry expectations and your skills. ✅ Data Science with Gen AI Training Program with Internship: https://bepec.in/courses/data-science-course-placements/ ✅ Data Engineer Training Program with Internship: https://bepec.in/courses/dataengineer-program/ ✅ AI , Gen AI Training Program with Internship: https://bepec.in/courses/artificial-intelligence-course-bangalore/ ✅ Generative AI Training Program with Internship: https://bepec.in/courses/generative-ai/ ✅ Data Analytics Training Program with Internship: https://bepec.in/courses/data-analyst-course-2026/ #dataengineer #sql #database #databricks
#Databricks Learning Reel by @dataengineeringtamil (verified account) - #Day1 Of SQL Learning in 60 Seconds Follow us @dataengineeringtamil #sql #database #DataEngineering #dataanalyst
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@dataengineeringtamil
#Day1 Of SQL Learning in 60 Seconds Follow us @dataengineeringtamil #sql #database #DataEngineering #dataanalyst
#Databricks Learning Reel by @data_master_consulting - 🚀 Databricks is now FREE! Yes, completely FREE. Databricks has launched Databricks Free Edition, and this is a huge opportunity for anyone who wants
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DA
@data_master_consulting
🚀 Databricks is now FREE! Yes, completely FREE. Databricks has launched Databricks Free Edition, and this is a huge opportunity for anyone who wants to learn Data Engineering, Analytics, Data Science, and AI — all in one platform. 💡 No credit card 💡 No cloud subscription 💡 Just sign up and start building You also get: ⚡ Serverless Compute for notebooks (ETL, ML, pipelines) 📊 Serverless SQL Warehouses for analytics and SQL workloads This means you can learn, practice, and build real projects without worrying about cloud costs. If you're serious about mastering Databricks, Data Engineering, and AI, this is the best place to start. 📌 Check the full video and start learning today! — Naval Yemul Databricks Consultant & Instructor 🔥 Follow for more Databricks & AI content #Databricks #DatabricksFreeEdition #DataEngineering #DataAnalytics #DataScience #AI #ArtificialIntelligence #Serverless #DataEngineer #BigData #Lakehouse #DataPlatform #LearnDatabricks #DataMasterConsulting #NavalYemul 🚀
#Databricks Learning Reel by @jessramosdata (verified account) - comment "AI" for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, bus
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@jessramosdata
comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle. Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering. Here’s what you should specify: ✨ size of dataset(s) (rows / columns) ✨ column names and data types ✨ primary keys and foreign keys ✨ distribution and allowed values ✨ variation of datapoints ✨ downloadable as CSVs ✨ anything else that may impact your project! Full example below: You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file. Have you tried this hack and said goodbye to Kaggle yet?
#Databricks Learning Reel by @data_pumpkin - In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and "works on my machine" chaos. T
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@data_pumpkin
In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos. These 4 tools fixed that: • dbt → modular, documented SQL transformations • Polars → faster, cleaner alternative to pandas • FastAPI → quick, reliable model deployment • Docker → consistent environments, no more deployment nightmares If you’re just starting out, learning these early will save you months of frustration.
#Databricks Learning Reel by @muskan.khannaa - Starting out in Data Engineering can feel overwhelming because there are so many tools and technologies out there. But before trying to learn everyth
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@muskan.khannaa
Starting out in Data Engineering can feel overwhelming because there are so many tools and technologies out there. But before trying to learn everything, focus on building strong fundamentals. Some free resources you can explore to get started: * SQL full courses on YouTube * Data engineering roadmap videos * Python for data engineering basics Once you’re comfortable with these, you can gradually move into data warehousing, pipelines, and cloud tools. Save this if you’re preparing for Data Engineering roles so you can come back to these resources later. What resource helped you the most while learning Data Engineering? . . . . . [Data Engineering Resources, Learn Data Engineering, Data Engineering Roadmap, SQL Full Course, Python for Data Engineering, Data Engineering for Beginners, How to Become a Data Engineer, Data Engineering Learning Path]
#Databricks Learning Reel by @priyal.py - 1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn
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@priyal.py
1. Netflix Show Clustering Group similar shows using K-Means based on genre, rating, and duration. Tech Stack: Python, Pandas, Scikit-learn, Seaborn 2. Spotify Audio Feature Analyzer Analyze songs by tempo, energy and danceability using Spotify API. Tech Stack: Python, Spotipy, Matplotlib, Plotly 3. YouTube Trending Video Analyzer Discover what makes a video go viral. Tech Stack: Python, Pandas, BeautifulSoup, Seaborn 4. Resume Scanner using NLP Parse and rank resumes based on job description matching. Tech Stack: Python, SpaCy, NLTK, Streamlit 5. Crypto Price Predictor Predict BTC/ETH prices using historical data. Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib 6. Instagram Hashtag Recommender Suggest hashtags based on image captions or niche. Tech Stack: Python, NLP, TF-IDF, Cosine Similarity 7. Reddit Sentiment Tracker Analyze community sentiment on hot topics using Reddit API. Tech Stack: Python, PRAW, VADER, Plotly 8. AI Job Postings Dashboard Scrape and visualize job trends by tech stack and location. Tech Stack: Python, Selenium/BeautifulSoup, Streamlit 9. Airbnb Price Estimator Predict listing prices based on location and amenities. Tech Stack: Python, Scikit-learn, Pandas, XGBoost 10. Food Calorie Image Classifier Estimate calories from food images using CNNs. Tech Stack: Python, TensorFlow/Keras, OpenCV Each project can be completed in 1-2 weekends. #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects
#Databricks Learning Reel by @pirknn (verified account) - Comment "LINK" to get links! 🚀 Want to learn database design in a way that actually sticks? This mini roadmap takes you from beginner fundamentals t
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@pirknn
Comment “LINK” to get links! 🚀 Want to learn database design in a way that actually sticks? This mini roadmap takes you from beginner fundamentals to designing production ready schemas you can confidently use in real apps. 🎓 Idea to Prod DB Perfect starting point if you are new to database design. You will understand how to go from a product idea to a clean data model, how to identify entities and relationships, and how to avoid common beginner mistakes. Great for learning the basics of schema thinking, constraints and tradeoffs. 📘 DBs in Depth Now deepen your understanding. This resource helps you build a strong mental model for how databases actually work under the hood. You will learn core concepts like indexing, query planning, transactions, isolation levels and normalization vs denormalization so you stop guessing and start designing with confidence. 💻 DB Design Course Time to go end to end. You will apply what you learned by designing schemas for real world features like users, payments, orders and analytics. You will learn how to model one to many and many to many relationships, choose data types, set keys and constraints, and prepare your database for real production workflows. 💡 With these database resources you will: Design clean schemas that scale with your product Understand normalization, indexes and transaction safety Build portfolio ready backend projects with production style database design If you are serious about backend engineering, system design interviews or building real products, database design is a must have skill. 📌 Save this post so you do not lose the roadmap. 💬 Comment “LINK” and I will send you all the links. 👉 Follow for more content on databases, backend engineering and system design.

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