Jefferson Matos De Oliveira

Optimizing Python UDFs in Snowflake just got easier. Parallel execution and resource-efficient design can supercharge Python workloads in Snowpark. From reducing execution time to improving performance at scale, this guide breaks down key optimizations to help you maximize efficiency. 🔗 Learn more: https://okt.to/EDTUB0

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In Notion ’s customer-facing analytics stack, user activity logs used to be: → Aggregated in Snowflake → Served via Redis + DynamoDB mashup It worked at first, but cracks appeared: • Complex multi-system orchestration • Fragile refresh logic and inconsistent latency • Predefined metric sets that limited exploration They rebuilt on StarTree (powered by Apache Pinot). Now: • Real-time ingestion and query serving in one system • Sub-second latency without cache layers • Ad-hoc dimensional flexibility for evolving metrics. #current2025 #kafka Adam Hudson

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Modern data teams are making the switch from #EMR’s fragmented ecosystem to Databricks’ unified Lakehouse architecture. If you're one of them, here's why it makes sense: ▶️ Unified architecture and built-in governance   ▶️ 2–3x faster queries with Photon engine  ▶️ Optimized for ML & GenAI workloads  ▶️ Faster time to value with simplified stack & reduced DevOps If you’re planning to scale and drive next-gen analytics, this EMR to Databricks migration guide breaks down the proven 6-step framework for you. Read the full data migration approach here: https://lnkd.in/gQ-HCMxX #DataModernization #DataMigration #CloudMigration #DevOps #Lakehouse #Databricks #DataStrategy 

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📢 Dataloop Integrates with Databricks for Multi-modal AI Orchestration. Managing unstructured and structured data for AI workflows just got a whole lot easier. Dataloop is now integrated with the Databricks Data Intelligence Platform, enabling scalable multi-modal AI orchestration for enterprises handling large-scale unstructured and structured data processing, model fine-tuning, and GenAI pipelines. What’s new? ➝ Automated Data Ingestion & Preparation – Easily import raw data from Databricks Unity Catalog into Dataloop for curation and annotation. ➝ Advanced Dataset Management – Filter, enrich, and structure unstructured data for AI-readiness. ➝ LLM Fine-Tuning & RLHF – Train foundation models with Reinforcement Learning from Human Feedback (RLHF) for higher accuracy. ➝ Seamless Storage & Scaling – Store processed datasets back in Databricks for further refinement or deployment. Why does this matter? ↳ AI teams often face fragmented workflows and tool overload. This integration eliminates inefficiencies, ensuring your data, models, and pipelines operate in sync - without extra steps or complexity. Read more: https://lnkd.in/ddUgGyDK #AI #Databricks #GenAI #DataPipelines #DataloopAI #LLM #MultiModal

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