#Databricks Data Intelligence Platform Features

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#Databricks Data Intelligence Platform Features Reel by @dataelevate_engineer (verified account) - Some real-time interview questions on Databricks that cover architecture, optimisation, integration, and troubleshooting: Save and share before it di
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@dataelevate_engineer
Some real-time interview questions on Databricks that cover architecture, optimisation, integration, and troubleshooting: Save and share before it disappear 🫠 Follow and check out other career content!! 1. You notice one of your Spark jobs on Databricks is running significantly slower than usual after a recent data volume increase. How do you diagnose and fix the problem? 2/ A Delta Lake table update fails with a concurrent write conflict error. How do you handle such concurrency issues in production? 3/ You have a join between a large table and a very small table, but the job is still running slow. What steps do you take to optimise the join in Databricks? 4/ Your Spark job is failing intermittently with OutOfMemoryError on the executors. How do you troubleshoot and mitigate this issue? 5/ Explain how you would handle a scenario where a large partition in a Delta table causes data skew and slows down the job execution. 6/ You are tasked with designing a streaming pipeline in Databricks to ingest IoT sensor data with low latency. What architecture and optimisations would you apply? 7/ Your Databricks cluster frequently auto-terminates during heavy workloads, causing job failures. How do you adjust cluster settings to handle this? 8/ A scheduled Databricks job fails with permission denied errors accessing S3 or ADLS storage. How would you troubleshoot and fix access issues? 9/ You deployed a Databricks notebook that queries data from multiple external sources (e.g., JDBC databases, APIs), but the performance is poor. What approaches would you take to improve this? 10/ Delta Lake’s OPTIMIZE command is taking too long on a large table. How do you approach optimising this command without impacting production workloads? Credit : Abhinav singh . . #AWS #dataengineering #SQL #DataScience #InterviewPreparation #DataAnalytics #dataengineering #career #careers #Powerbi #analytics #dataanalyst #freshers #dataanalysis #tech #technology #amazon #google #coding #reels #engineering #corporate #SQLChallenges #questions #dataanalyst #TechSkills #softwaredeveloper #software #viralvideos #computerscience #dsa Data Analytics Data Engineering Data bricks
#Databricks Data Intelligence Platform Features 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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@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 Data Intelligence Platform Features 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 Data Intelligence Platform Features Reel by @viktoria.semaan (verified account) - Free. Forever. For Everyone. Link in captions ↓ Databricks removed all the gatekeeping from Data & AI! ❌ No "business email" required ❌ No credit ca
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@viktoria.semaan
Free. Forever. For Everyone. Link in captions ↓ Databricks removed all the gatekeeping from Data & AI! ❌ No “business email” required ❌ No credit card ❌ No expiring credits Now anyone can get hands-on with the same cutting-edge platform used by the world’s top data teams — free, forever. Perfect for learning, prototyping, and building real skills. What you get: ✅ Unified Data & AI workspace — SQL, Python, dashboards & ML in one place 📓 Collaborative notebooks — built-in versioning & comments 📊 Sample datasets + Genie — query with SQL or natural language 🧱 Lakehouse + LakeFlow — ingest, transform, orchestrate visually 🤖 AI apps & agents — experiment with open models instantly 🧠 Databricks Assistant — live code suggestions & explanations 🎓 Self-paced training — Data Engineering, ML & GenAI 🔗 Sign up now: bit.ly/dbx-free-signup Comment DATA and I’ll send you links to self-paced courses! This is your chance to build skills in the technology that’s defining the future. 📌 Save & Share with others! —— Hello 👋 I’m Viktoria, AI Engineer and Principal Technologist at Databricks. I share practical AI tips and resources. Follow for more educational content @viktoria.semaan #ai #databricks #freetutorials #genai
#Databricks Data Intelligence Platform Features Reel by @sundaskhalidd (verified account) - Repost to share with friends ♻️ Here's how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it d
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@sundaskhalidd
Repost to share with friends ♻️ Here’s how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it down to 3 minutes because instagram. One part that got cut off was the job market. Should I post a part 2? what are other skills that would you add to the list?? #dataanalysis #dataanalyst #sql #python
#Databricks Data Intelligence Platform Features Reel by @data_with_anurag (verified account) - 🚨 Want to become a Data Analyst but don't know where to start? 👀 I've got you covered - Microsoft has launched a dedicated learning path with free
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@data_with_anurag
🚨 Want to become a Data Analyst but don’t know where to start? 👀 I’ve got you covered — Microsoft has launched a dedicated learning path with free resources to help you master Data Analytics step by step! 📊 💬 Comment “DATA” and I’ll DM you the complete roadmap + official Microsoft resources. ✅ Beginner to advanced topics covered ✅ 100% FREE learning materials ✅ Certificate-ready path to build your career 🔥 This is your sign to start learning data analytics the right way — straight from Microsoft! 🚀
#Databricks Data Intelligence Platform Features Reel by @datacraftly - Day 1/365 - What is Databricks? 🚀 If data had a kingdom, Databricks would be the throne 👑 So what exactly is Databricks? 👉 Databricks is a unifi
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@datacraftly
Day 1/365 – What is Databricks? 🚀 If data had a kingdom, Databricks would be the throne 👑 So what exactly is Databricks? 👉 Databricks is a unified data + AI platform built on Apache Spark. It helps companies: • Process massive data (Big Data) • Build data pipelines (ETL) • Run analytics • Train Machine Learning models • Build AI solutions In simple words? It’s where Data Engineering + Data Science + AI come together in one place. Think of it like this 👇 SQL handles structured data Spark handles big data processing And Databricks brings everything together in the cloud ☁️ It supports Azure, AWS & GCP And yes — most modern data teams use it today. If you want to become a Data Engineer in 2026, you cannot ignore Databricks. This is Day 1 of our 365-day journey. Let’s build data careers together 💙 Follow us for more such content. ⸻ [databricks | big data | data engineering | apache spark | cloud analytics] #DataEngineering #Databricks #BigData #TechCareers #learning
#Databricks Data Intelligence Platform Features 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 Data Intelligence Platform Features Reel by @dataanddreamscapes - Day 3 of Better Data Engineering 🚀 Parameterized Notebooks, Modularized Feature Logic, and Scheduled Automated Jobs in Databricks. Moving from Manual
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@dataanddreamscapes
Day 3 of Better Data Engineering 🚀 Parameterized Notebooks, Modularized Feature Logic, and Scheduled Automated Jobs in Databricks. Moving from Manual Runs to Pipeline Orchestration. My Activity Log: https://pewter-porch-d86.notion.site/Databricks-14-Days-AI-Challenge-2-30947a4b88b880a5a363f2181e43601e?source=copy_link #DataEngineering #DatabrickswithIDC #Databricks #Automation #ai
#Databricks Data Intelligence Platform Features Reel by @sundaskhalidd (verified account) - Comment 'Projects' to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist i
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@sundaskhalidd
Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai
#Databricks Data Intelligence Platform Features Reel by @onseventhsky (verified account) - Data Analytics Road map (6-9 months) https://drive.google.com/drive/folders/17KOCp6F1JGqOCwIdryzcDykNCSu93Ltc?usp=sharing Built from my personal int
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@onseventhsky
Data Analytics Road map (6-9 months) https://drive.google.com/drive/folders/17KOCp6F1JGqOCwIdryzcDykNCSu93Ltc?usp=sharing Built from my personal interview experiences(Interviews given - 5+) Duration - 1-2 Months - Basics Learn basic - intermediate SQL(joins) from youtube/udemy Basic Python from youtube/udemy/Leetcode Basic Excel Duration 2-3 Months - Intermediate Practice intermediate to advanced SQL on Data Lemur/Leetcode/WiseOwl Practice easy-intermediate python questions on Leetcode/Hackerrank Start BI - Power BI tutorial from youtube/udemy Duration 3-4 Months - Advanced Learn Pandas/pyspark, practice EDA on csv files from Kaggle datasets on jupyter notebook/colab Practice advanced SQL questions(window functions) Build BI projects from kaggle datasets/Datacamp Github profile to showcase your projects + LinkedIn Theoretical knowledge on ETL pipelines/ Data warehousing concepts(Chat GPT) Resources SQL - Theory - W3Schools(free)/Udemy(paid), Practice - Leetcode/Data Lemur Python - Theory - Youtube/Udemy, Practice - Leetcode(easy to medium) Data Warehousing+ETL - Tutorials Point/Udemy, Datacamp/Chat GPT Power BI/Tableau - Datacamp, wiseowl Pandas/Pyspark - Datacamp, Leetcode, Kaggle Basic Excel . . . . . . #big4 #fyp #data #analytics #ootd #grwm
#Databricks Data Intelligence Platform Features 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?

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