Claude Waddington’s Post

🚀 Metadata-Driven Lakehouse Implementation with Microsoft Fabric Modern enterprises deal with data from apps, ERP, CRM, HR, clickstreams, and campaigns. Managing such variety at scale requires more than pipelines — it needs a metadata-driven framework. Instead of hardcoding ingestion and transformations for each dataset, metadata tables, audit logs, and reusable modules make the framework configurable, automated, and scalable. 🔹 Why a Metadata-Driven Approach? Automation at scale – One framework can handle 10 or 1000+ sources. Reduced manual effort – Configurations replace custom code. Stronger governance – Every activity is logged and auditable. Compliance ready – Supports PII anonymization and regulatory standards like GDPR/CCPA. Business agility – New sources can be onboarded faster without redesigning pipelines. 🔹 Key Components in Microsoft Fabric Lakehouse 1️⃣ Data Ingestion Flexible ingestion via low-code Data Pipelines, Spark-based implementations, mirroring, or shortcuts. Metadata tables like ingest_control define which sources to pull, when, and how. Audit and notification modules provide complete transparency into success/failure. Result: A reliable Bronze layer containing raw but traceable data. 2️⃣ Data Validation Uses Spark-based validation rules driven by configuration. Checks for completeness (row counts, column checks) and reasonableness (aggregations, checksums). Results stored in validation_results and exposed in dashboards. Ensures confidence in both source and target data. 3️⃣ Data Quality Automated quality rules for consistency, accuracy, and compliance. Powered by metadata + tools like Microsoft Purview Data Quality. Health dashboards provide a proactive approach to data monitoring. 4️⃣ PII Anonymization Sensitive attributes (name, address, SSN, etc.) are masked/anonymized. Protects privacy while allowing analytics. Critical for compliance with GDPR, HIPAA, and CCPA. 5️⃣ Transformation & Enrichment Metadata-driven transformations from Bronze → Silver → Gold layers. transformation_config table defines rules like column renaming, datatype changes, conditional filters, and aggregations. Ensures business-level datasets are consistent, clean, and ready for consumption. 6️⃣ Governance & Monitoring – Config_mgmt, auditing, notifications, and reporting create a single pane of glass for operations. 🔹 Business Value -Faster onboarding of new datasets -Higher trust in data through validation & quality checks -Built-in compliance & privacy protection -Scalable foundation for BI, ML, and AI workloads 📌 Conclusion A metadata-driven Lakehouse on Microsoft Fabric goes beyond data storage — it creates a governed, automated, and business-ready ecosystem that delivers trusted insights at scale. ✨ If you’re building a data platform today, metadata-driven frameworks are not optional — they’re essential. . . . #DataEngineering #MicrosoftFabric #Lakehouse #MetadataDriven #DataGovernance #DataQuality #Azure #BigData #AI

Enterprise data platforms in 2025 are no longer just storage and analytics systems—they’ve become the foundation for AI, compliance, and real-time decision-making. Open formats like Apache Iceberg and Delta Lake are reducing lock-in and making cross-cloud architectures more flexible. AI is now embedded into platforms, with agents, RAG and vector search supporting governance, data quality and workflows in areas such as finance, supply chain and fraud detection. Hybrid and edge deployments are now standard in industries where speed, privacy and regulation matter, while FinOps has become essential as AI workloads drive up costs. Vendors are taking different approaches – Snowflake is embedding AI into SQL, Databricks is emphasizing interoperability and AI-first workflows, Informatica is extending metadata-driven governance, Cloudera is strengthening hybrid and edge, Teradata continues to focus on large-scale analytics, IBM is expanding watsonx for regulated AI workloads and Salesforce is reshaping its stack with acquisitions. The cloud providers also play a central role – Amazon Web Services (AWS) continues to provide a broad toolkit, Microsoft integrates Fabric across its ecosystem, Google Cloud builds around AI and open flexibility and Oracle ties data services tightly to its application stack. Read my latest Forbes article on how the role of data platforms has changed. They’re no longer just about managing information—they’ve become core infrastructure that drives compliance, shapes how AI is used and influences business decisions. The platforms that stay open, flexible, and cost-conscious will be the ones that help organizations move quickly and compete with confidence. Moor Insights & Strategy Microsoft 365 Oracle Cloud Google https://lnkd.in/emMff5Mp

The Future of Analytics isn’t Dashboards. It’s Conversations with AI What if your BI stack could ask (and answer) its own questions? What if it reconciled messy, multi-source data on the fly, flagged anomalies before revenue felt the pinch, and explained *why* outliers happened—not just that they did? Imagine fraud signals surfaced in real time from transactions, emails, and logs; intelligent automation stitching those insights into approvals, alerts, and next-best actions; and AI-guided improvements that shave cycle time off core processes without a six-month re-engineering project. Can your data platform do that today? Now push it further: If AI can correlate context across ERP/CRM/HR, what business trends could you predict weeks earlier—pipeline risk, churn, supply shocks, cash-flow pressure? What if your dashboards became conversational copilots that justify recommendations with lineage and policy-aware guardrails? The leaders we're watching, are blending GenAI + analytics + automation to move from “reporting” to “decisioning.” A quick twist you might not expect: Oracle × Google is no longer hypothetical. **Oracle Database\@Google Cloud has been generally available since Sept 2024**, and **Oracle now offers Google’s Gemini models via OCI Generative AI—with plans to surface Gemini inside Oracle Fusion Cloud Applications**. That opens some very interesting doors for BI, anomaly/fraud detection, and process optimization on enterprise data. Read my article for the practical angles and examples: https://lnkd.in/dm34fYWx #oracle #ai, #generativeai, #agenticai #oraclefusion

Talentica Software achieves Snowflake AI Data Cloud Select Tier status, validating its secure, high-performance AI integrations for enterprise success - https://lnkd.in/dyHGu-Xd "Talentica's focus on AI-native product development and Snowflake's powerful Data Cloud create a strong foundation for customer success. Together, we aim to accelerate innovation, transform data into insights, and empower enterprises to stay ahead in a rapidly evolving market," said Manjusha Madabushi, CTO & Co-Founder at Talentica Software #TalenticaSoftware #SnowflakeAI #DataCloud #AIInnovation #EnterpriseTech #TechIntelPro

🚀 𝗧𝗵𝗿𝗶𝗹𝗹𝗲𝗱 𝘁𝗼 𝗦𝗵𝗮𝗿𝗲 𝗠𝘆 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗻 𝗚𝗖𝗣! Over the past few weeks, I’ve been working on an 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗗𝗼𝗺𝗮𝗶𝗻 🏥, where I designed and implemented a complete 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗖𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗥𝗖𝗠) 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 using 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 (𝗚𝗖𝗣). This project was both technically challenging and rewarding, as it combined 𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 best practices with the 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝗶𝗲𝘀 𝗼𝗳 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗱𝗮𝘁𝗮. 🔍 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗣𝗿𝗼𝗷𝗲𝗰𝘁? Healthcare organizations often deal with 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗱𝗮𝘁𝗮: ✅ EMRs from hospitals 🏥 ✅ Insurance claims 📄 ✅ CPT codes 💉 ✅ NPI data 🧾 The goal was to build a 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 that makes data accurate, analytics-ready, and easy to consume. 🛠️ 𝗞𝗲𝘆 𝗚𝗖𝗣 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: • 𝗚𝗖𝗦 📦 → Raw & processed storage • 𝗗𝗮𝘁𝗮𝗽𝗿𝗼𝗰 🔥 → Large-scale transformations • 𝗖𝗼𝗺𝗽𝗼𝘀𝗲𝗿 (𝗔𝗶𝗿𝗳𝗹𝗼𝘄) ⏱️ → Workflow orchestration • 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 🗄️ → EMR ingestion • 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 🗃️ → Analytics & gold tables • 𝗚𝗶𝘁𝗛𝘂𝗯 🐙 + Cloud Build ⚡ → CI/CD automation • 𝗖𝗹𝗼𝘂𝗱 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 📋 → Monitoring & error handling ✨ 𝗖𝗼𝗿𝗲 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: ✅ 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (Bronze → Silver → Gold) ✅ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 for schema flexibility ✅ 𝗦𝗖𝗗 𝗧𝘆𝗽𝗲 2 for historical tracking ✅ 𝗖𝗗𝗠 for standardized schema ✅ 𝗥𝗼𝗯𝘂𝘀𝘁 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 & 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 ✅ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗦𝗽𝗮𝗿𝗸 & 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 ✅ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗖𝗜/𝗖𝗗 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 📊 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀: ✔️ Automated 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 & 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 pipelines ✔️ Analytics-ready 𝗴𝗼𝗹𝗱 𝘁𝗮𝗯𝗹𝗲𝘀 in BigQuery ✔️ KPIs like: • 💰 Revenue trends • ⏳ Claim turnaround times • ✅ Acceptance vs ❌ rejection rates 🙌 𝗠𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: This project helped me strengthen: • 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 with GCP • 𝗘𝗧𝗟 𝗱𝗲𝘀𝗶𝗴𝗻 with Airflow + Spark • 𝗖𝗜/𝗖𝗗 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 in Data Engineering • Tackling 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗱𝗮𝘁𝗮 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 (schema drift, compliance, quality) • 𝗕𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗰𝗼𝘀𝘁 vs 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: 👉 https://lnkd.in/gQnixqEz 🚀 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? • Real-time ingestion with Pub/Sub + Dataflow • ML models for claim denial prediction & revenue forecasting • Advanced data governance & lineage tracking 💡 This reinforced my belief: 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 it’s about solving 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 with data.

I recently had the opportunity to attend the #Snowflake World Tour in Hyderabad, and it was truly inspiring to dive deep into the future of enterprise data. The sessions covered exciting advancements in AI, as well as innovative demos on Cortex agent and agentic applications, showcasing how data is evolving to do so much more. Here are the highlights I found most impactful: 1) Unified Data Platform: Snowflake is doubling down on integrating data, compute, and AI into a single, seamless platform. (Enterprise Data + Compute + AI). 2) Conversational AI for Enterprise Data: Interact with enterprise AI data using natural language—no more typing questions. 3) Cortex AI SQL Innovations: Brand-new Cortex AI SQL functions like AI_AGG, Aggregate Summary, AI_TRANSCRIBE, and many more. Kamesh Sampath - Smart Croud Counter Live Demo Using Cortex AI SQL+ LLM (Claude Sonnet 4.0) was awesome. 4) Snowflake Application Framework: Accelerates building AI-powered apps (e.g., PowerSchool), bringing intelligence directly to where enterprise data lives. 5) Cortex Analyst & Search: Advanced AI analytics and semantic search capabilities, with ongoing improvements on current limitations. 6) Cortex Agents & Agentic Applications: Introduction of Cortex Agents as the foundation for the next generation of autonomous, intelligent enterprise apps. 7) Evolution in Agentic Architectures: Progression from Monolithic to Service-Oriented to Microservices to Agentic systems featuring dynamic logic and reasoning, natural language user interfaces, and adaptive, context-aware responses. 8) Application Compatibility & New Use Cases: Agents unlock seamless integration for brand & product planning, claims processing, supplier management, retail order management, and insurance underwriting. 9) The Multi-Agentic Future: Enterprise applications are evolving to support multi-agentic architecture. 10) Openness & Interoperability: Support for open table formats and standards like ICEBERG and APACHE POLARIS enhances flexibility. 11) Snowpark Connect for Apache Spark: Saw a real-world example of 41% cost reduction by streamlining Spark workloads on Snowflake. 12) Horizon Connected Catalog: A new unified catalog that organizes the entire data estate for improved governance and discovery. 13) Enhanced Performance Optimizer: Now offers better visibility, control, and spend anomaly detection—enabling more efficient resource management. #Snowflake’s vision, “Where Data Does More,” truly came alive throughout the day as I connected with experts and explored groundbreaking AI solutions that are shaping the next era of data-driven decision making. Thank you, Snowflake Team, for the insightful and engaging deep dive session! Thank you Genpact COE-OneData. #snowflakesquad, #Snowflake, #Cortex, #CortexAgent, #SnowflakeIntelligence #MultiAgnet #CortexAnalyst&Search, #Genpact, #CoretexAI, #snowAI, #Cloud, #Claudesonnet, #CISCO, #

Migration Support via AI Agents (2026–2027) While not part of the upcoming BDB 10.5 release, this initiative represents a critical strategic direction for the BDB Platform. 🌐 Migration Focus Enabling seamless transitions from leading platforms to BDL (BDB Data Lake powered by Hudi/Iceberg): *Databricks Lakehouse → BDL *Snowflake → BDL *Google BigQuery → BDL *AWS Athena → BDL *Azure Synapse → BDL 💡 Why This Matters Today, the top five platforms may not yet recognize the depth and potential of BDB. However, with a carefully crafted go-to-market strategy, brand visibility, and ecosystem partnerships, BDB is positioned to compete head-to-head. For customers and investors alike, this initiative is both transformational and high-return. 🛠️ Our Approach Over the past two years, we have laid the groundwork by: Delivering complex projects on Databricks, AWS, and Snowflake for major enterprises. Implementing multiple solutions natively on BDB and creating an Implementation Playbook for accelerated adoption. Partnering with leading Indian institutions to teach AI on BDB, extending soon into an Apps Framework where students and developers can build integrated apps. Strengthening our data lake foundations with clear migration paths and best practices. 📊 The Business Case The economics of migration are compelling: Enterprises currently spending $3Y over three years on legacy platforms can migrate to BDB, with migration + 3 years of enhancement/maintenance offered at $1.2Y–$1.5Y. Thousands of dashboards from Tableau and Power BI—now converging with Salesforce and Fabric—will need future-proof migration into BDB Dashboards. Our AI Agent-driven automation will reduce migration costs and timelines dramatically. 🔮 Vision Being underestimated provides us with a unique competitive advantage. With Agentic AI, deep Data Lake capabilities, and customer-first pricing, BDB will emerge as the most affordable, scalable, and intelligent migration alternative in the industry. #BDB #DataLake #AI #AgenticAI #Migration #AnalyticsTransformation

Qlik Announces Canada Cloud Region to Empower Data Sovereignty and AI Innovation: TORONTO, Sept. 9, 2025 -- Qlik, a global leader in data integration, data quality, analytics, and artificial intelligence (AI), today announced ...

Please allow me to introduce Noranalytos. Elevate your data journey with Noranalytos! Our solutions integrate security, metadata illumination, domain-driven lake architecture, and prompt engineering to redefine data analytics and Generative AI excellence. We are proud to be an AWS APN Partner/TDSYNNEX, recognized for our AWS Qualified Software. At Noranalytos, we specialize in modernization with a strong data foundation, delivering comprehensive, integrated, and governed solutions for faster, smarter, and better business outcomes. Our expertise includes Generative AI, Business Intelligence (BI), and Machine Learning (ML), all powered by multi-cloud capabilities. Visit us here: Noranalytos AWS Partner Page (https://lnkd.in/gi8U_qFH) 1 NA-Gen-AI: Discover more (https://lnkd.in/gJaZ_bwh) 2 NA Domain-Driven Data Lake: Discover more (https://lnkd.in/gCVsGTGg) 3 NA Metadata: Discover more (https://lnkd.in/gczPzD2z) 4 NA Data Security: Discover more (https://lnkd.in/giqHv54y) 5 AWS Marketplace Listing: NA BI Migration Agent (https://lnkd.in/gN8KVdbd) 6 AWS Marketplace Listing: NA SAS Migrator Agent (https://lnkd.in/gDE7icx8) 7 AWS Marketplace Listing: Nor Campaign AI Agent (https://lnkd.in/gYuWMD_n) AWS Partner Solutions Finder (https://lnkd.in/g8iA4uAW) Problem We Solve • Legacy BI & SAS tools causing cost and inefficiencies
- Fragmented metadata and outdated analytics
- Lack of real-time, AI-powered insights
- High SAS licensing & infra costs • Siloed data lakes • Limited data democratization across teams • Manual campaign creation • Limited Gen AI integration in enterprise data flows https://lnkd.in/g8iA4uAW USP - Automated legacy BI & SAS migration tools
- Integrated Generative BI
- Custom ingestion by domain
- Domain driven data architecture
- Pay-as-you-go cloud model - Automate end-to-end marketing with AI-driven market campaign generation.

🌊 𝐃𝐚𝐲 61: 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐯𝐬 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 🏛️ ❓𝐐1: 𝐀𝐫𝐞 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐚𝐧𝐝 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐛𝐚𝐬𝐢𝐜𝐚𝐥𝐥𝐲 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞? 💡𝐀: Not really — their foundations are different. • A 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 (𝐃𝐖) is schema-on-write. That means data must be cleaned, transformed, and structured 𝘣𝘦𝘧𝘰𝘳𝘦 it enters the system. It’s optimized for fast SQL queries and BI reporting. • A 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞, on the other hand, is schema-on-read. Data can be stored in its raw form (structured, semi-structured, unstructured), and only when you query it do you apply schema and transformations. 👉 Bottom line: 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐞𝐱𝐜𝐞𝐥 𝐚𝐭 𝐜𝐥𝐞𝐚𝐧, 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬, while 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞𝐬 𝐠𝐢𝐯𝐞 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐫𝐚𝐰 + 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬 like ML and AI. ❓𝐐2: 𝐖𝐡𝐲 𝐰𝐨𝐮𝐥𝐝 𝐚 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐦𝐨𝐯𝐞 𝐟𝐫𝐨𝐦 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐭𝐨 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞? 💡𝐀: Flexibility, scalability, and cost efficiency are the main reasons.  In a DW-only setup, companies often maintain 𝘵𝘸𝘰 𝘴𝘺𝘴𝘵𝘦𝘮𝘴:  1. A 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 for raw data ingestion.  2. A 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 for curated analytics. • A Lakehouse merges these into one, cutting redundancy and data movement. • Storage costs are lower since you’re keeping data in cheaper formats (like Parquet/Delta). • Plus, data scientists get direct access to historical + unstructured data for AI/ML, which a warehouse alone can’t easily provide. 👉 In short: Lakehouse = 𝐨𝐧𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐟𝐨𝐫 𝐁𝐈 + 𝐀𝐈. ❓𝐐3: 𝐈𝐬 𝐚 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐭𝐡𝐞 𝐛𝐞𝐭𝐭𝐞𝐫 𝐜𝐡𝐨𝐢𝐜𝐞? 💡 𝐀: Not always — it depends on your workload maturity. • If your organization is heavily BI-focused with highly structured data (finance, sales, reporting dashboards), a 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 will likely perform better with lower complexity. • If your use cases involve IoT, logs, images, machine learning, and diverse data formats, a 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 offers flexibility and future readiness. • Many modern enterprises actually use a 𝐡𝐲𝐛𝐫𝐢𝐝 𝐦𝐨𝐝𝐞𝐥: Warehouses for fast reporting + Lakehouses for innovation and advanced analytics. 👉 The smartest path? Evaluate 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘸𝘰𝘳𝘬𝘭𝘰𝘢𝘥𝘴 𝘷𝘴 𝘧𝘶𝘵𝘶𝘳𝘦 𝘯𝘦𝘦𝘥𝘴 before deciding. 🔜 𝐍𝐞𝐱𝐭 𝐮𝐩: Introduction to Data Vault Modeling in Azure🏰 #AzureDataFactory #Azure #DataEngineering #DaysOfAzure #DataPipelines #Automation #CloudETL #AzureLearning #AzureEngineering #Learning #Learn #ADF #Datasets #ETL #Cloud #DataFlow #Transformations #DataFlows #ETLTools #CloudIntegration #LearnAzure #QnA #Learner #ELT #CloudData #LinkedInLearning #LearningJourney #MicrosoftAzure #TechCommunity #BigData #Lakehouse #DataWarehouse #DataArchitecture #Day61