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#Databricks Cloud Data Platform Reel by @viktoria.semaan (verified account) - A quarter of Y Combinator W25 startups have 95% AI-generated codebases 🤯 But here's the problem most miss: External AI builders = data silos + secur
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@viktoria.semaan
A quarter of Y Combinator W25 startups have 95% AI-generated codebases 🤯 But here’s the problem most miss: External AI builders = data silos + security gaps The solution? Build AI apps INSIDE your data platform. Here’s your Databricks stack: 💾 Lakebase for storage ⚡ Databricks Apps for runtime 🛡️ Unity Catalog for governance 🤖 Model Serving to power your app with AI Result: Idea → working app in hours (not weeks) with enterprise security built-in. No ETL. No credential chaos. No security gaps. Have you tried vibe-coding? Share your experience and the tools you used in the comments! —— 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 #databricks #vibecoding #dataengineering #genai #sql
#Databricks Cloud Data Platform Reel by @arunansuspeaks (verified account) - Microsoft Purview is a comprehensive data governance platform to govern data across private, hybrid and multi cloud environments. Azure Databricks i
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@arunansuspeaks
Microsoft Purview is a comprehensive data governance platform to govern data across private, hybrid and multi cloud environments. Azure Databricks is an end-to-end analytics platform built on Spark. Unity Catalog in Azure Databricks provides centralized security, access control and governance for the entire data estate managed through Azure Databricks. Great news! Microsoft Purview now integrates with Azure Databricks enabling users to govern data in Azure Databricks Lakehouse and have Meta data from Unity Catalog be available through Microsoft Purview Data Catalog. #microsoft #azure #microsoftpurview #azuredatabricks #datagovernance #dataanalytics
#Databricks Cloud Data Platform 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 Cloud Data Platform 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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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 Cloud Data Platform 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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@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 Cloud Data Platform Reel by @deepakgoyalcloud - ❌ You'll get rejected in a Data Engineering interview if you can't answer this: ⚡ Spark vs PySpark vs Databricks - What's the difference? ✅ Apache S
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@deepakgoyalcloud
❌ You’ll get rejected in a Data Engineering interview if you can’t answer this: ⚡ Spark vs PySpark vs Databricks – What’s the difference? ✅ Apache Spark – The core engine for big data processing (written in Scala). ✅ PySpark – Spark’s Python API to write Spark code using Python. ✅ Databricks – A cloud platform built on Spark offering advanced tools like Delta Live Tables, Unity Catalog & more! 🎯 If you’re learning Data Engineering or prepping for interviews, this clarity is a must. Follow @deepakgoyalcloud to master these concepts easily. #viral #explore #fyp #instagood #ApacheSpark #PySpark #Databricks #DataEngineering #BigData #InterviewPrep #TechSimplified #DeepakGoyal #WhatsAppLearning #DataEngineerLife #CloudComputing #DeltaLiveTables #UnityCatalog #SparkExplained #LIVideoIndia
#Databricks Cloud Data Platform Reel by @itsallbout_data - Building a big data pipeline can feel like trying to assemble IKEA furniture without the manual-until you see it laid out like this. 🏗️💻 Whether you
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@itsallbout_data
Building a big data pipeline can feel like trying to assemble IKEA furniture without the manual—until you see it laid out like this. 🏗️💻 Whether you’re team AWS, Azure, or GCP, this is your ultimate roadmap from raw ingestion to final presentation. Save this so you never have to second-guess which service handles your ETL ever again! The Cheat Sheet Breakdown: * Ingestion: Getting the data in the door (Lambda, IoT Hub, Pub/Sub). * Data Lake: Storing it all in its raw glory (S3, Data Lake Store, Cloud Storage). * Computation: The heavy lifting & ML (SageMaker, Databricks, BigQuery). * Warehouse: Organizing for the win (RedShift, SQL, BigTable). * Presentation: Turning numbers into narratives (QuickSight, Power BI, DataLab). Which cloud provider are you currently building on? Let’s settle the debate in the comments! 👇 #BigData #DataEngineering #CloudComputing #AWS #Azure [GCP DataScience TechTips ByteByteGo CodingLife SoftwareArchitecture DataPipeline]
#Databricks Cloud Data Platform Reel by @altexsoftcom - What's the difference between database and data warehouse? Find out the full version in our video linked in bio. ______________ #AltexSoft_video #data
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@altexsoftcom
What's the difference between database and data warehouse? Find out the full version in our video linked in bio. ______________ #AltexSoft_video #data #dataengineering #database #datawarehouse #dataengineer #warehouse #datascience #AI #datamanagement
#Databricks Cloud Data Platform Reel by @dataanddreamscapes - Day 1 of Phase 1: Better Data Engineering in the Databricks 14 Days AI Challenge - 2 (Advanced). In this notebook: • CSV to Delta conversion • Manag
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@dataanddreamscapes
Day 1 of Phase 1: Better Data Engineering in the Databricks 14 Days AI Challenge – 2 (Advanced). In this notebook: • CSV to Delta conversion • Managed table creation • Incremental append simulation • Small file experiment • File math: 12.3 GB / 109 files • OPTIMIZE analysis Moving from executing commands to understanding architecture inside Databricks using Delta Lake. #Databricks #DeltaLake #DataEngineering #BigData #learninginpublic Access my Activity Log: https://pewter-porch-d86.notion.site/Databricks-14-Days-AI-Challenge-2-30947a4b88b880a5a363f2181e43601e?source=copy_link
#Databricks Cloud Data Platform Reel by @aiadventureryt - Comment "DATA" and I'll DM you the link to Google Dataset Search - the tool that lets you explore millions of free datasets from across the internet i
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@aiadventureryt
Comment “DATA” and I’ll DM you the link to Google Dataset Search — the tool that lets you explore millions of free datasets from across the internet in one place. It organizes datasets by topics like satellite imagery, traffic patterns, speech and language data, anime, sports, and more. If you’re training AI models, building recommendation systems, analyzing trends, or running experiments, this saves hours of searching. Instead of digging through random sites, you can discover real, structured datasets instantly. Each dataset clearly shows where the data comes from, how it’s structured, and how to access it, so you know exactly what you’re working with before downloading anything. Comment “DATA” and I’ll send you the link. #googledatasets #datasets #ai #machinelearning #datascience #aitools #research #aicommunity
#Databricks Cloud Data Platform Reel by @eduardofcostabh - #datavisualization #datascience #análisededados #dataanalytics #database
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@eduardofcostabh
#datavisualization #datascience #análisededados #dataanalytics #database
#Databricks Cloud Data Platform 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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