"DBMS Innovations: AI, Data Mesh, and More"

Here is the list of GCP (Google Cloud Platform) data components 👇 🔹 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 – Serverless, fully managed data warehouse for analytics at scale. 🔹 𝐂𝐥𝐨𝐮𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞– Durable, scalable object storage for structured & unstructured data. 🔹 𝐂𝐥𝐨𝐮𝐝 𝐒𝐐𝐋– Managed relational database service for MySQL, PostgreSQL, and SQL Server. 🔹 𝐁𝐢𝐠𝐭𝐚𝐛𝐥𝐞– NoSQL wide-column database for large-scale, low-latency workloads. 🔹 𝐃𝐚𝐭𝐚𝐩𝐫𝐨𝐜 – Managed Spark & Hadoop for batch and streaming data processing. 🔹 𝐃𝐚𝐭𝐚𝐟𝐥𝐨𝐰– Serverless service for ETL and real-time data streaming pipelines. 🔹 𝐏𝐮𝐛/𝐒𝐮𝐛– Messaging service for event-driven systems and real-time ingestion. 🔹 𝐋𝐨𝐨𝐤𝐞𝐫 (𝐟𝐨𝐫𝐦𝐞𝐫𝐥𝐲 𝐃𝐚𝐭𝐚 𝐒𝐭𝐮𝐝𝐢𝐨)– Business intelligence and visualization platform. 🔹 𝐃𝐚𝐭𝐚𝐩𝐥𝐞𝐱– Unified data governance, catalog, and security across GCP data lakes/warehouses. 🔹 𝐅𝐢𝐫𝐞𝐬𝐭𝐨𝐫𝐞– Serverless NoSQL document database for app data and analytics. 🔹 𝐃𝐚𝐭𝐚 𝐅𝐮𝐬𝐢𝐨𝐧– Managed ETL/ELT tool for building and orchestrating pipelines. 🔹 𝐀𝐈 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 / 𝐕𝐞𝐫𝐭𝐞𝐱 𝐀𝐈– End-to-end ML/AI platform integrated with data pipelines. #DSA #DE #LearnGCPwithDSA #AI

TL;DR: Azure Databases Embrace First-Class JSON & Schema Agnosticity Microsoft's data platforms have achieved a major milestone in database evolution, fundamentally blurring the lines between relational and semi-structured models by integrating JSON as a first-class citizen across their ecosystem. What started as an experiment in schema-agnostic indexing (indexing every JSON path without a predefined schema) has grown into powerful, unified systems: • Cosmos DB: Built on the foundation of schema-agnostic indexing, it focuses on global scale, schema flexibility, and AI-readiness for massive, distributed workloads. It now includes vector indexing (DiskANN) and multi-model capabilities. • DocumentDB (PostgreSQL): Extends PostgreSQL by optimizing the query path (BSON direct to query tree) and enhancing GIN/RUM indexes to create a truly document-aware optimizer. It successfully brings document query semantics into the relational world. • SQL Server 2025 & Azure SQL Database: This release completes the vision by introducing a native JSON data type optimized for performance, supporting documents up to 2 GB in size. SQL Server 2025 integrates native JSON indexing, ANSI SQL-compatible JSON functions, vector data types, and AI integration hooks. The Key Takeaway: The functional distinction between semi-structured (JSON) and fully structured/relational data is disappearing. Developers can now mix and query both data types freely, using standard SQL semantics, scaling from local applications to globally distributed AI systems within a single engine. MADE WITH NOTEBOOKLM #SQLServer2025 #AzureSQL #AzureDocumentDB #AzureCosmosDB #AzureDatabaseforPostgreSQL

Oracle AI Database 26ai Powers the AI for Data Revolution At Oracle AI World in Las Vegas (October 14, 2025), Oracle unveiled Oracle AI Database 26ai—a major release that architects AI directly into the core of the database to realize an “AI for Data” vision across operational and analytic workloads. It enables dynamic, agentic AI that combines private enterprise data with public information, and follows an open approach with support for Apache Iceberg, Model Context Protocol (MCP), leading LLMs, and ONNX—deployable across multicloud and on-premises environments. Oracle On the tech front, the new Autonomous AI Lakehouse supports the Iceberg format and interoperates with Databricks and Snowflake across OCI, AWS, Azure, and Google Cloud. Key capabilities include Unified Hybrid Vector Search (blending vector, relational, text, JSON, graph, and spatial), MCP server support for iterative agent reasoning, a Private AI Services Container for running private models, and Exadata acceleration with vector offload—plus integrations with NVIDIA NeMo Retriever, cuVS, and CAGRA for high-performance RAG pipelines. Oracle For mission-critical security and resiliency, 26ai implements NIST-approved post-quantum (ML-KEM) encryption for data-in-flight alongside encryption at rest, and adds Zero Data Loss Cloud Protect, a Globally Distributed Database (active-active with RAFT, <3s failover), True Cache with automatic transactional consistency, and SQL Firewall. 26ai is an LTS release replacing 23ai; customers can apply the October 2025 release update to gain currently available 26ai features without a full upgrade or re-certification—AI Vector Search included at no extra cost. Oracle #AI #Oracle #OracleDatabase #OracleAI #GenAI #AgenticAI #VectorSearch #DataLakehouse #ApacheIceberg #MCP #RAG #Exadata #NVIDIA #DataSecurity #PostQuantum #Multicloud #DataEngineering #DataAnalytics #EnterpriseAI

Check out this new analysis of Oracle Autonomous AI Lakehouse by Steve McDowell on Forbes "The solution’s multi-cloud deployment model offers infrastructure flexibility that competitors struggle to match...Oracle also enables Iceberg access from operational databases, not just analytics platforms. This operational integration eliminates switching costs and lock-in that analytics-only platforms cannot establish....the company’s mission-critical database heritage enables Oracle to deliver mature capabilities in areas where lakehouse-native vendors often struggle, including enterprise-grade security and availability." #Oracle #OracleAIDatabase #OracleCloudInfrastructure #Exadata #AI #GenAI #OCI #AWS #Azure #GoogleCloud #Vectors #VectorSearch #AIVectorSearch #Lakehouse #Iceberg #Open #DataLake #Multicloud #26ai #Autonomous #AutonomousAIDatabase https://lnkd.in/gVw3JDJD

SQL vs. NoSQL vs. Blob Storage Not all data storage is created equal. As data engineers, our first and most critical design decision is where to store data. Choosing the wrong tool leads to high costs, poor performance, and technical debt. 1. SQL (Relational) Databases -What it is: The world of structured tables, rows, and columns. -Best for: Data where consistency and integrity are non-negotiable (e.g., financial transactions, user accounts, e-commerce orders). -Think: PostgreSQL, Azure SQL, AWS RDS. 2. NoSQL (Non-Relational) Databases -What it is: A flexible world of JSON documents, key-value pairs, or graphs. -Best for: Data where scalability and flexibility are paramount (e.g., IoT sensor data, shopping carts, social media feeds, real-time analytics). -Think: MongoDB, Azure Cosmos DB, AWS DynamoDB. 3. Blob (Object) Storage -What it is: A massive, cost-effective "digital garage" that holds anything. -Best for: Unstructured data (images, videos, logs), backup/archiving, and, most importantly, the foundation of a modern Data Lake. It's our primary "landing zone." -Think: Azure Blob Storage, AWS S3, Google Cloud Storage. Understanding the "why" behind each tool is the foundation of building a robust, efficient, and scalable data platform. #DataEngineer #DataArchitecture #SQL #NoSQL #BlobStorage #AWS #Azure

Modern Data Engineering Workflow – End to End Pipeline Data Engineering involves more than just moving data. It’s about designing scalable pipelines, enabling real-time insights, and powering business decisions. Data Sources – Ingest structured & unstructured data (APIs, CSV, Web, Relational, XML). Data Extraction – Load raw/unprocessed data into Data Lakes. Data Processing – Perform cleansing, validation, transformation, and aggregation (batch or real-time). Data Storage – Store processed data in Data Warehouses for fast analytics. Data Visualization – Enable insights through intuitive dashboards and advanced analytics. This is the foundation behind any modern cloud data platform whether on AWS, Azure, or GCP. #DataEngineering #ETL #DataPipelines #AWS #Azure #GCP #Databricks #Snowflake #BigData #DataWarehouse #DataLake #Analytics #CloudComputing #Python #SQL #Spark #DataTransformation #DataVisualization #DataOps #C2C #SeniorDataEngineer

MariaDB just eliminated the need for separate vector databases. RAG functionality now lives directly inside the database. The architecture diagram got simpler overnight. This changes everything for AI application development. MariaDB's Enterprise Platform 2026 just launched with something remarkable: "RAG-in-a-Box." No more complex data pipelines. No separate vector stores. No external retrieval systems. Everything happens inside one database. The platform includes: 🤖 Built-in AI copilots for developers and DBAs 📊 MariaDB Exa analytical engine (1,000x faster processing) ☁️ Serverless database with elastic scaling 🔍 Native vector search and embedding capabilities The performance gains are stunning. Enterprise Server 11.8 shows 250% better performance than previous versions. This matters because AI workloads are unpredictable. Traditional databases struggle with sudden activity spikes. MariaDB solved this with pay-as-you-go serverless options that automatically adjust resources. The result? Organizations can build intelligent applications faster. Less complexity. Fewer moving parts. Better performance. Natural language queries get converted to database actions instantly. Real-time insights happen without moving data around. This unified approach - transactional, analytical, and AI workloads in one place - feels like the future of database architecture. What's your take on databases integrating AI natively versus keeping them separate? #MariaDB #DatabaseInnovation #ArtificialIntelligence 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/eseCaUdB

Wolfram vs SQL/NoSQL for Smart Data Handling Ever wondered how to make your data work smarter, not harder? Companies often juggle SQL for structured data and NoSQL for unstructured logs. The problem historically is that They’re separate worlds potentially leading to - slow pipelines, complex integration, and missed insights. Enter Wolfram Mathematica & Wolfram Cloud: one environment where you can Query structured data, Integrate unstructured logs, Clean, enrich, and analyse it, Run predictive models, dashboards, and APIs. An all in one unique computational eco system. Wolfram doesn’t treat data as “rows” or “documents” it treats it as computable knowledge. Think of it as combining the reliability of SQL with the flexibility of NoSQL, powered by computation and automation. No more stitching together multiple tools, just one platform to turn data into actionable intelligence. "Wolfram doesn’t just store your data, it understands it!" If your team is still moving data around between systems, it might be time to see what a unified computational approach can do. Learn More: https://lnkd.in/e-mtzmtZ https://lnkd.in/eNXG4B6p https://lnkd.in/e24gh6H6 https://lnkd.in/ePGRQizG https://lnkd.in/e7b58AgC #DataScience #SalesAnalytics #Wolfram #SQL #NoSQL #BusinessIntelligence #Automation #PredictiveAnalytics #Business #Computation

Excited to share the launch of Oracle AI Database 26ai — a groundbreaking evolution of Oracle’s flagship database, now fully architected with AI at its core. Key Highlights: 1. AI seamlessly integrated across all major data types and workloads for unmatched insights and innovation. 2. Open and flexible: supports Apache Iceberg open table format, Model Context Protocol (MCP), leading LLMs, agentic AI frameworks, and ONNX embedding models. 3. Enables building, deploying, and managing custom AI agents with a no-code visual platform and declarative PL/SQL & Python support. 4. Quantum-resistant encryption securing data-in-flight and at-rest—safeguarding against future quantum threats. This database runs across Oracle Cloud, hyperscale clouds, private cloud, and on-premises—delivering AI where your data lives. It offers a smooth upgrade from Oracle Database 23ai with no application changes or recertification needed. Oracle AI Database 26ai empowers enterprises to unify all data types and leverage AI natively, eliminating data silos. It is redefining the AI-for-data revolution by bringing AI directly to enterprise data, unlocking new levels of productivity and intelligence securely and efficiently. #Oracle #AIDatabase #AIforData #Cloud #DataInnovation #QuantumSecurity #DigitalTransformation

Modern Data Engineering Workflow – End to End Pipeline Data Engineering involves more than just moving data. It’s about designing scalable pipelines, enabling real-time insights, and powering business decisions. Data Sources – Ingest structured & unstructured data (APIs, CSV, Web, Relational, XML). Data Extraction – Load raw/unprocessed data into Data Lakes. Data Processing – Perform cleansing, validation, transformation, and aggregation (batch or real-time). Data Storage – Store processed data in Data Warehouses for fast analytics. Data Visualization – Enable insights through intuitive dashboards and advanced analytics. This is the foundation behind any modern cloud data platform whether on AWS, Azure, or GCP. hashtag #DataEngineering #ETL #DataPipelines #AWS #Azure #GCP #Databricks #Snowflake #BigData #DataWarehouse #DataLake #Analytics #CloudComputin #Python #SQL #Spark #DataTransformation #DataVisualization #DataOps #C2C #SeniorDataEngineer