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#Databricks Google Cloud Integration Reel by @hustleuphoney (verified account) - 🚀 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 Google Cloud Integration 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 Google Cloud Integration Reel by @saqlaindigu - Comment FIRE, I will send you the link. #ai #artificialintelligence #tech #google #firebase #aitools #aitool #softwaredevelopers #appdevelopmentserv
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@saqlaindigu
Comment FIRE, I will send you the link. #ai #artificialintelligence #tech #google #firebase #aitools #aitool #softwaredevelopers #appdevelopmentservices #aideveloper
#Databricks Google Cloud Integration Reel by @iron.coding - Now you can build Full-Stack applications Without Writing Any Code! 💻 In this video, I show youI a complete e-commerce website that is fast, clean,
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@iron.coding
Now you can build Full-Stack applications Without Writing Any Code! 💻 In this video, I show youI a complete e-commerce website that is fast, clean, and fully functional with multiple product categories. I didn’t write a single line of code. I used Reflex, an AI-powered full-stack app builder that creates frontend, backend, and database — all in pure Python. Just describe your idea, and Reflex automatically generates everything — from UI to APIs. You can integrate tools like Databricks, Stripe, Supabase, and more directly — and deploy your app live in minutes. Plus, you get complete access to the source code, so you can customize anything anytime. Bonus: You can even use Reflex for free today — just comment on the LinkedIn post in my pinned comment to claim unlimited credits. #webdevelopment #coding #python #nocode #nocodetools #aitools #programming
#Databricks Google Cloud Integration Reel by @edhonour (verified account) - Google Collab and Jupyter Notebooks makes a lot more sense if you envision everything you do to eventually be a micro service.
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@edhonour
Google Collab and Jupyter Notebooks makes a lot more sense if you envision everything you do to eventually be a micro service.
#Databricks Google Cloud Integration Reel by @data_with_anurag (verified account) - Comment Down "DATA" Day 143 of Learning About Data Domain from Scratch for Free✅ Certified Associate Data Engineer Guide🔥💯 💯Long Form Video on -:
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@data_with_anurag
Comment Down “DATA” Day 143 of Learning About Data Domain from Scratch for Free✅ Certified Associate Data Engineer Guide🔥💯 💯Long Form Video on -: yt channel -: Be a Programmer . . . ❤️Follow❤️ @data_with_anurag for more👈🏻 #datawithanurag #dataanalytics #dataanalyst #databricks #dataengineering #certifieddataengineer (Data engineer job, Data Engineer Certificate, Databricks certificate, free guide, free resource, Data Engineering Skill)
#Databricks Google Cloud Integration Reel by @1stepgrow_academy - 🏗️ Become the Blueprint Builder of Data - Your Complete Data Architect Roadmap Data Architects are the strategic minds behind every data-driven orga
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@1stepgrow_academy
🏗️ Become the Blueprint Builder of Data — Your Complete Data Architect Roadmap Data Architects are the strategic minds behind every data-driven organization. They design how information flows, scales, and stays secure — from databases to the cloud. Ready to build your path? Here’s your roadmap 👇 🧱 Master Databases Learn the backbone of every data system: 🔹 Modeling & Normalization 🔹 Transactions, Concurrency & Integrity 🔹 Query Optimization & Indexing 🔹 NoSQL & Distributed Systems (CAP) 🔹 Security, Governance & Warehousing 🗺️ Data Modeling Craft the architecture of insights: 🔹 Conceptual, Logical & Physical Models 🔹 Relational & Dimensional (BI) Models 🔹 Data Vault & NoSQL Designs ⚙️ System Architecture Think big-picture: 🔹 Build structured conceptual → physical layers 🔹 Design distributed, modular, and secure systems 🔹 Prioritize observability, scalability & cost efficiency ☁️ Cloud Platforms Go cloud-native with: 🔹 AWS | Azure | GCP 🔹 Snowflake & Databricks for scalable analytics 🔥 Big Data Processing Frameworks Handle massive data streams with: 🔹 Spark | Kafka | Flink | MapReduce | Storm 🔄 Data Integration Master how data moves and transforms: 🔹 ETL & ELT 🔹 CDC & Virtualization 🔹 API-based & Streaming Pipelines 🛡️ Data Security & Governance Protect and manage data like a pro: 🔹 Encryption, Access Control, Masking 🔹 Lineage, Quality, Compliance & Metadata 📊 Advanced Analytics Turn architecture into intelligence: 🔹 Predictive, Prescriptive & Real-Time Analytics 🚀 Final Takeaway The best Data Architects don’t just build systems — they design ecosystems of intelligence, performance, and trust. 👉 Follow @1stepgrow_academy for complete roadmaps, data career guides & expert insights to build your future in Data, Cloud & AI. #DataArchitect #DataEngineering #BigData #DataAnalytics #DataModeling #DataGovernance #AWS #Azure #GCP #Databricks #Snowflake #TechCareers #1stepGrow
#Databricks Google Cloud Integration Reel by @itversity - In this video, we'll break down the 7 key differences between PySpark and Apache Spark, helping you decide which is the right choice for your big data
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@itversity
In this video, we'll break down the 7 key differences between PySpark and Apache Spark, helping you decide which is the right choice for your big data processing needs. We'll cover: * Language Support: Why PySpark uses Python and Apache Spark supports multiple languages. * Performance: Is there a real-world speed difference between Spark and PySpark? * Library Integration: How PySpark leverages Python libraries like NumPy and Pandas for seamless data science workflows. * Developer Productivity: Choosing the right tool based on your team's skills (Python vs. Scala/Java). * Memory Management: Understanding the differences in how PySpark (Python garbage collection) and Spark (JVM) handle memory. * Community & Resources: Leveraging the power of the Python and Spark communities. * When to Choose PySpark vs. Apache Spark: Clear guidelines for making the right decision based on your project and goals. Whether you're a data scientist, data engineer, or big data developer, this video will give you a clear understanding of the strengths and weaknesses of each framework. Ready to start learning PySpark hands-on? Check out our Udemy course: https://www.udemy.com/course/apache-spark-and-databricks-for-beginners/learn/?couponCode=24T3MT270225 #PySpark #ApacheSpark #BigData #DataScience #Python #DataEngineering #SparkVsPySpark #Tutorial
#Databricks Google Cloud Integration Reel by @cloudwithsingh - Google just launched Google Skills - a new way to learn in-demand tools and boost your career for free! Try it out now and see what you can unlock. C
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@cloudwithsingh
Google just launched Google Skills — a new way to learn in-demand tools and boost your career for free! Try it out now and see what you can unlock. Comment “skills” and I’ll send you the link 🔗 #cloudwithsingh #google #googleai #googleskills #cloud #cloudcomputing #google
#Databricks Google Cloud Integration Reel by @techtokwithkriti - 💻 Want to drastically improve your chances of cracking Cloud Engineer interviews? 🌩️ Day 6 is all about coding and scripting-essential skills that y
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@techtokwithkriti
💻 Want to drastically improve your chances of cracking Cloud Engineer interviews? 🌩️ Day 6 is all about coding and scripting—essential skills that you need to sharpen, even if you’re applying for entry-level roles. Here’s how to get started: 1️⃣ Set a timer for 30 minutes a day and work on coding problems—even if you don’t solve them right away, the practice will pay off. 2️⃣ Leverage coding platforms like @hackerrank or @codecademy 3️⃣ Explore Python’s capabilities (or any language of your choice) for automation and scripting within cloud environments. 4️⃣ Check out @udemy or @coursera for beginner-friendly Python courses. @kodekloud has lab enviornments that you can leverage. 5️⃣ Practice integrating scripts with cloud APIs to automate tasks. Play with AWS Lambda or Google Cloud Functions. 📌 Remember, consistency is key! The more you practice, the better and faster you’ll become at solving real-world problems in cloud environments. #CloudEngineer #CodingSkills #PythonForCloud #GoLang #TechWithKriti #CloudComputing #AutomationSkills #TechCareers #WomenInTech #LeetCode #HackerRank #LearnPython #CloudProjects #AWS #GoogleCloud #CloudAPI #DevOps #TechTok #techtokwithkriti
#Databricks Google Cloud Integration Reel by @blocklantis - Cloud data company Databricks recently announced the completion of a Series I fundraising round where it raked in $500 million from primary investor a
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@blocklantis
Cloud data company Databricks recently announced the completion of a Series I fundraising round where it raked in $500 million from primary investor accounts led by T. Rowe and new investors Capital One Ventures, Ghisallo Capital Management, Ontario Teachers’ Pension Plan and Nvidia. The latest investment round was achieved at a valuation of $43 billion dollars and a share price of $73.50, according to Databricks. In 2021, the company raised $1.6 billion in Series H funding at a valuation of $38 billion. Databricks’ financial partnership with Nvidia indicates an increased synergy between the two AI corporations, with generative AI for enterprise data being the primary driver for the raise. Nvidia CEO Jensen Huang, per a Databricks press release, called enterprise data “a goldmine for generative AI,” adding, “Databricks is doing incredible work with Nvidia technology to accelerate data processing and generative AI models.” Databricks’ most prominent product is its “Lakehouse” platform. Lakehouse essentially combines a data warehouse with a data lake for seamless developer workflow access. Per the company’s press release: “The Databricks Lakehouse unifies data, analytics and AI on a single platform so that customers can govern, manage and derive insights from enterprise data and build their own generative AI solutions faster.” Lakehouse empowers enterprise clients to build out their own generative AI models — sort of like a ChatGPT-style AI system that’s trained only on a client’s internal data. This architecture puts privacy controls in the purview of the business client and allows enterprises to fine-tune models in order to inhibit unwanted or erroneous outputs. At a valuation of $43 billion, Databricks is one of the highest-valued artificial intelligence companies existing in the US — trillion-dollar competitors such as Amazon Web Services, Microsoft Cloud and Google Cloud notwithstanding. #blocklantis #jpegs #nftbrasil #nftsanimation #tokensale #nftturkey #raribleart #nftmarket #nftgames #nftgaming #nfty #nftcrypto #nftartcollector #nftgram #tradingcrypto #nftmagazine #solana #nftcollection #openseanft #coolcats #nftcommunity #nftart #vr #animation
#Databricks Google Cloud Integration Reel by @justsayabhi - Moving from a Jupyter notebook to production requires a real architecture. Here is the Google Cloud stack that scales. 👇 1️⃣ Containerize Everything
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@justsayabhi
Moving from a Jupyter notebook to production requires a real architecture. Here is the Google Cloud stack that scales. 👇 1️⃣ Containerize Everything Wrap your RAG application (Python logic, LangChain code) in a Docker container. Push it to Google Artifact Registry. No more dependency conflicts. 2️⃣ Cloud Run with Autoscaling Deploy that container to Cloud Run. It scales from 0 to 1,000 instances automatically. If 500 users ask about shipment tracking at 9 AM, Cloud Run spins up replicas in seconds and you only pay per request. 3️⃣ FastAPI + Async Don’t use Flask for LLM apps. Use FastAPI with async def. LLM calls are I/O bound (waiting for Vertex AI). Async lets your server handle other requests while waiting for the model to reply. 4️⃣ Database Choice Use Cloud SQL for PostgreSQL with the pgvector extension for your knowledge base. It handles relational data (user IDs, shipment records) and vector data (embeddings) in one place. Managed backups + high availability included. — Hi! My name is Abhishek and I work as an AI Engineer for a Google Cloud startup in Toronto.  If you want to certified as a Google Cloud Machine Learning Engineer, comment “AI” and I’ll send you a link in your DM. —- Keywords: #gcp #cloud #rag #llm #google #foryou #explore #home

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