#Databricks Cloud Migration

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

🚀 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 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

Compartilha! #blockdag #airdrop

𝐂𝐥𝐨𝐮𝐝 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝 𝐋𝐚𝐜𝐤𝐢𝐧𝐠 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Starting without a roadmap is the #1 reason for project delays. You need to know which apps need to be retired, rehosted, or refactored. 𝐔𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐂𝐨𝐬𝐭𝐬: Many businesses focus on storage costs but forget about Data Egress (the cost of moving data out of the cloud) and bandwidth limitations. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 (𝐓𝐡𝐞 𝟐𝟎𝟐𝟔 𝐅𝐚𝐜𝐭𝐨𝐫): Laws like the DPDP Act (India) or GDPR (EU) have strict rules about where data lives. If you handle healthcare or banking data, moving it to an international server could lead to massive fines. 𝐒𝐤𝐢𝐥𝐥 𝐆𝐚𝐩𝐬: Your on-prem team might not be trained for a virtualized environment. Assuming your current IT can handle a massive 1,000-app migration without specific cloud training is a recipe for downtime. 𝐓𝐡𝐞 "𝐁𝐢𝐠 𝐁𝐚𝐧𝐠" 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Trying to migrate everything at once. Modern best practices suggest a Phased Migration, starting with non-critical systems to test the waters before moving the "heart" of your business. #CloudMigration #CloudComputing #DigitalTransformation #ITStrategy #CyberSecurity2026 #CloudCostOptimization #IBNTechnologies #TechTrends

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

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

Here's another data engineering project you can play around with: AWS S3 to Postgres (cloud to on premise data migration mini-project) #dataanalytics #dataengineering #datascience #techtok

Gotta load the data in chunks! #python #programming #datascience

How much data do you have on the cloud? • • #cloudstorage #dataprivacy #dataownership #tech #startup #internetfreedom

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

Comment “Dev” and i’ll send you my full AI infrastructure stack with every tool that I use. Pinecone= vector DB → Supabase (DB + backend) → Clerk (autu can literally ship a full → Stripe (payments)k app from your → Resend (emails)bedroom now. → Cloudflare + Namecheap (domain + DNS) → PostHog (analytics) → Sentry (errors) → Upstash/(Redis) an literally ship a full → Pinecone (vector séarch)p from your You can literally ship a fui stack app trom your bedroom in 2026. #Supabase #AlCoding #SaaS #Startup #Vercel