Azure Cosmos DB introduces Binary Encoding for better performance and storage

Azure vs AWS for data engineering. Here's how I choose - Storage (ADLS Gen2 vs S3) “Both are rock-solid. S3 wins on ecosystem and Iceberg/Athena maturity. ADLS shines with granular ACLs and tight Azure AD integration.” - Compute/ETL (Synapse/Fabric/ADF vs Glue/EMR/Athena) “Azure gives you a friendlier on-ramp (ADF UI, Synapse, and Fabric’s ‘one pane’ vision). AWS leans serverless and modular (Glue, EMR Serverless, Athena), with more knobs and flexibility.” - Streaming (Event Hubs vs Kinesis/MSK) “Parity for most use cases. AWS has breadth with Kinesis + MSK. Azure plays well if you’re already in the Microsoft lane.” - Warehousing & BI (Fabric/Power BI vs Redshift/QuickSight) “Fabric + Power BI is tough to beat for end-to-end analytics. Redshift has matured (serverless, RA3) but QuickSight serves a different crowd.” - Orchestration (ADF vs MWAA/Step Functions) “ADF is approachable and visual. On AWS, Airflow via MWAA + Step Functions is powerful if your team prefers code-first.” - Governance (Purview vs Lake Formation) “Purview’s discovery/lineage is strong. Lake Formation is great for lake permissions but can be prickly to learn.” - DevEx & Ops “Azure often feels smoother in Microsoft shops (SSO, networking, procurement). AWS has the deepest marketplace, docs, and community patterns.” My rule of thumb: - Pick Azure if you’re all-in on M365/Power BI/Azure AD and want a unified, governed experience. - Pick AWS if you want serverless-first, open table formats (Iceberg/Delta), and polyglot flexibility. - Either way: Databricks is first-class on both. Favor open formats, IaC, and avoid multi-cloud unless you truly need it. What tipped the scales for your team?

Musili Adebayo, thank you for this illustration as it puts the 🐘 in the room righ under the spotlight. Batch processing was a genius idea back in the day—a smart way to bundle transactions and process them at a specific time, saving resources. But let's be real, in today's instant-gratification world, who has 24 hours to wait for a critical outcome if they can get it now? Instant processing isn't just a nicety; it’s becoming the baseline. The move from batch to real-time, event-driven architecture is all about speed and user experience. However, have we really considered the elephant in the room? The sheer, unholy scale of data growth this speed-driven, instant-everything world is creating? Every single, individually processed transaction generates its own heap of metadata, logs, and audit trails. Moving from one big, bundled process to millions of micro-processes doesn't just increase speed; it multiplies the data footprint. Now, throw AI into the mix—which thrives on this granular, real-time data—and you’ve got an explosion happening in your storage environment. We can't just keep throwing more storage at the problem and hope for the best. That's a highway to unnecessary cost and complexity! Instead of waiting to see what impact this speed has on our balance sheets, we should be proactively designing intelligent storage policies right now. This means shifting our focus from buying more capacity to optimizing the capacity we have. Luckily, the same cloud platforms that enable this instant world are giving us AI-powered tools to fight back and align with #FinOps principles: For AWS users, services like Amazon S3 Intelligent-Tiering use machine learning to automatically move data between frequent, infrequent, and archive access tiers. You simply turn it on, and the AI handles the cost optimization for you. You can also leverage AWS Cost Explorer (enhanced with ML) and AWS Compute Optimizer for rightsizing recommendations. For Azure users, Azure Blob Storage Lifecycle Management (which can be augmented by AI/ML FinOps tools) automatically transitions data based on rules like "last access time" to move it from Hot to Cool or Archive tiers. Additionally, Azure Storage Discovery provides advanced storage insights, sometimes powered by Azure Copilot, to help you find and optimize under-utilized data across your estate. The future of data management isn't just about faster processing; it's about smarter storage. 1. Leveraging hot, warm, and cold storage tiers. 2. Designing with retention and deletion rules built-in. 3. Using these AI-driven tools to automatically classify and move data, saving on cost and freeing up engineering time. What are you seeing in your organization? Are you feeling the pressure from this data growth, and what are your most effective strategies for intelligent storage tiering? Let's discuss! 👇 #DataManagement #StorageSolutions #AIEffect #RealTimeProcessing #FinOps...

🚀 𝗔𝗜_𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗶𝘀 𝗻𝗼𝘄 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗹𝘆 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲! Big moment for unstructured data in the Snowflake AI Data Cloud. AI_Extract, powered by our in-house Arctic-Extract model, is now GA across all deployments and platforms. 🧊 What it is An AISQL function that lets you extract structure from documents, text blobs, and images — directly in Snowflake. No pipelines. No orchestration. Just SQL. ⚙️ Why I love it ✅ One API for text, images, and documents ✅ Works in-place on your cloud storage (S3, Azure, or GCS) — no data movement ✅ Supports 20+ common file types — PDFs, DOCX, PNGs, JPEGs, TXTs, and more ✅ Flexible JSON outputs — define exactly what you need ✅ 29 languages supported 💡 What’s new in GA • Support for full-table JSON extractions • 4K token context window • Prompt context injection for guided extractions • Brand-new upgraded model — higher quality on DOCVQA + table extraction tasks AI_Extract turns unstructured data into queryable insights — instantly, with the same simplicity as SELECT *. #Snowflake #AIExtract #Arctic #AISQL #DataCloud #Developers #UnstructuredData #GenAI

AI is only as smart as your data foundation… and that’s where the real work of AI adoption lies. 💡 At Foundations 2025, AWS’s Harshit Kohli challenged the idea that models lead — he says we need to fast-track data readiness first. 👇 Explore how CData Software + Amazon Web Services (AWS) are enabling live data access, frictionless pipelines, and AI systems that stay aligned with real-time data. #CData #CDataFoundations #DataStrategy #AIstrategy #AWS #DataOps

AI is only as smart as your data foundation… and that’s where the real work of AI adoption lies. 💡 At Foundations 2025, AWS’s Harshit Kohli challenged the idea that models lead — he says we need to fast-track data readiness first. 👇 Explore how CData Software + Amazon Web Services (AWS) are enabling live data access, frictionless pipelines, and AI systems that stay aligned with real-time data. #CData #CDataFoundations #DataStrategy #AIstrategy #AWS #DataOps

Cosmos DB in Microsoft Fabric is now in preview! An AI-optimised, autonomous database built on Azure Cosmos DB, now deeply integrated into Fabric bringing NoSQL + SQL, real-time intelligence, and Copilot-powered BI together in one unified platform. Get started in seconds. Scale automatically. Stay secure by default. And build agentic AI apps without the database management overhead. The future of data + AI starts here. Read our latest blog to explore more : https://lnkd.in/d5W6k-Af #MicrosoftFabric #CosmosDB #AI #DataPlatform #Azure #Innovation

Oracle delivers Database 26ai and autonomous lakehouse to support AI training and inferencing across cloud and on-premises environments. Move to a data platform that’s more secure, flexible and scalable. Talk to Oracle to understand how these capabilities deliver in a cost effective and efficient manner. SiliconANGLE details the latest capabilities available in Oracle's AI Database and Autonomous AI. Lakehouse:https://lnkd.in/gMVHMfrn #AIWorld

🚀 Day 8: Deep Dive into AWS AI/ML, Generative AI, and Data Analytics My Cloud Engineer journey took a massive leap today with an intensive session on AWS AI/ML and the complete Data Pipeline lifecycle. The sheer power and breadth of these services are inspiring! Here are the four core lessons from the day that are crucial for any modern cloud professional: 1. Mastering the AWS AI/ML Stack & Generative AI 🤖 I learned that AWS structures its intelligence services into a tiered system: AI Services (Tier 1): Pre-built models for specific tasks, like Amazon Comprehend for text analysis and Amazon Rekognition for computer vision. Amazon SageMaker (Tier 2): The fully managed platform for building, training, and deploying custom ML models at scale. Generative AI: Explored the next frontier with Amazon Bedrock—the managed service for working with Foundation Models (FMs)—and the powerful enterprise assistant, Amazon Q. 2. Building Robust Data Pipelines 🌊 A reliable data pipeline is the backbone of analytics. I mapped out the end-to-end flow using AWS services: Ingestion: Using Amazon Kinesis (real-time) and Amazon Firehose (near real-time). Storage: Understanding the difference between Amazon S3 (Data Lake for raw, flexible data) and Amazon Redshift (Data Warehouse for structured, BI-optimized data). Processing: Leveraging AWS Glue for managed ETL and Amazon EMR for large-scale processing with frameworks like Spark. The AWS Glue Data Catalog keeps everything organized! 3. Turning Data into Insights with Analysis & BI 📈 The final stage is analysis—making the data useful! Amazon Athena: Run serverless SQL queries directly on data stored in S3 (my Data Lake). Amazon Redshift: Use the power of its MPP architecture for massive data analytics. Amazon QuickSight: Create interactive, serverless business intelligence dashboards for technical and non-technical users alike. Feeling energized by this deep dive into data intelligence! On to Day 9! #CloudEngineer #AWS #AIML #GenerativeAI #DataAnalytics #DataEngineering #100DaysOfCloud