Databricks releases General Availability of Delta Sharing for MVs and STs

From Raw to Golden: Delivering High-Quality Data at Scale using Modern Tools In modern data architectures, the golden layer isn’t just a storage target—it’s the single source of truth where trustworthy, actionable data comes to life. Delivering it at scale comes with challenges: messy raw data, costly ETL pipelines, and high risk of downstream errors. Detecting and handling anomalies and inconsistencies early is critical to maintaining trusted, enterprise-ready datasets. Here’s a structured approach using modern tools in Microsoft Fabric: 1.     DataWrangler – Precision & Exploration Notebook-based wrangling: Transform, profile, and clean datasets interactively. Statistics-driven insights: Detect anomalies, missing values, and inconsistent or invalid records before production. Data Anomaly & Inconsistency Detection: Apply data validation rules (type checks, range checks, uniqueness). Identify outliers or null-heavy records for review. Generate summary statistics to highlight suspicious patterns early. Guiding Principle: “Validate early to prevent downstream errors and unnecessary ETL costs.” 2.     Dataflow Gen2 – Scale & Automation Visual ETL pipelines: Orchestrate large-scale workflows efficiently. AI-assisted transformations: Standardize, enrich, and automate data cleaning. Guiding Principle: “Automate repetitive tasks and scale what must scale.” Hybrid Approach – Efficiency Meets Trust Pre-curate and filter out anomalies and inconsistencies in DataWrangler → feed cleaned datasets into Dataflow Gen2 pipelines. Optimize compute and storage by reusing curated datasets and dynamically scaling clusters. Catch errors early, reduce pipeline failures, and deliver trusted, golden-layer-ready data. 💡 Key Takeaway: High-quality data isn’t just technical—it’s a strategic capability. Treat data as a critical business asset, implement early anomaly and inconsistency detection, and design workflows that balance precision, scalability, and cost efficiency to deliver enterprise-grade golden insights—the single source of truth for your organization. #DataEngineering #DataArchitecture #MicrosoftFabric #ETL #DataOps #GoldenLayer #DataQuality #DataValidation #CostEfficiency #GuidingPrinciples #ModernDataTools

Friday – Wisdom to apply + Sneak peek next week 💡 You’ve got options—choose based on maturity and goals. If your organization still struggles with data silos and slow central teams, a Data Mesh (even partial) can supercharge agility. If you're focused on big data analytics with fewer domain needs, a Data Lake may offer simpler scale. Many of today’s architects mix both—using lakes for raw consolidation and meshes for domain empowerment. Cutting-edge approach? Autonomous data products—trusted, governed, domain-owned—and the future of scalable data ecosystems. 👉 What shall we explore next week? Potential topics: "Scalable MLOps Patterns" or "Responsible AI System Design"? Pro Tips: * Always align your architecture with org structure and culture. * Use pilots to validate before full transformation. * Build governance into your design, not as an afterthought. 📖 Read more: 🔗 https://lnkd.in/gqPBS2sG 🔗 https://lnkd.in/gfpPFGQj 🔗 https://lnkd.in/g6Q6V2Jc 🔗 https://lnkd.in/gSyUQCSf #DataMesh #DataLake #DataArchitecture #NextWeekPreview

🔗 Data Pipeline Overview – The Heart of Data Engineering 🚀 A strong data pipeline is what powers modern businesses. From collection to consumption, it ensures data flows smoothly and is transformed into real value. Here’s a simple breakdown 👇 📥 Collect – Data comes from sources like databases, streams, and applications. 🔄 Ingest – Data is loaded into queues and pipelines for further processing. 🗄️ Store – Data is stored in Data Lakes, Warehouses, or Lakehouses depending on the use case. ⚙️ Compute – Data is processed through batch or streaming to make it analytics-ready. 📊 Consume – Finally, data powers BI dashboards, self-service analytics, ML models, and data science. 💡 Why is this important? Without a well-structured pipeline, data stays siloed and underutilized. With it, organizations gain real-time insights, smarter decisions, and scalable analytics. 👉 Every Data Engineer should master pipeline design — it’s the foundation of data-driven organizations. Which stage do you think is the most challenging — Ingest, Store, or Compute? #DataEngineering #DataPipeline #BigData #MachineLearning #DataScience #CloudComputing #Analytics

Data silos aren’t just an inconvenience - they’re silent profit killers. Every isolated database. Every locked department. Every “we’ll sync later” moment… They all cost you: speed, clarity, and ultimately - growth. The signs are everywhere: – Reports that never align – Teams working in the dark – Missed opportunities hidden in plain sight Now imagine this instead: Your data flows seamlessly - from one team to another, one decision to the next - with zero friction. Here’s what makes it possible: > Centralized Data Platforms – A single source of truth instead of fragmented chaos > ETL & ELT Pipelines – Structured + unstructured data, connected in real time > Data Governance & Accessibility – Secure yet collaborative access for every stakeholder > AI & Automation – Metadata driven categorization for instant discoverability At Brilliqs, we help businesses unlock seamless, interconnected data ecosystems that drive faster, smarter decisions. Because when data flows freely, so does innovation. Because when data flows, innovation follows. Want to break down the silos slowing your progress? Comment below or message us directly - let’s start unlocking your next wave of growth. #DataSilos #DigitalTransformation #BusinessIntelligence #DataStrategy #EnterpriseData #Brilliqs

This is a fantastic and well-articulated post. I completely agree that the most critical shift isn't just in the technology, but in the enterprise, strategy moving from a monolithic pipeline to a truly federated, domain-driven data ecosystem. It's the only way to build a sustainable and agile data foundation at scale.

How #DataEngineering Transforms Raw Data into #Business Intelligence The Data Engineering Process Data engineering is #indispensable in #enhancing unrefined data to an usable format. Data engineering is defined by building sophisticated pipelines of data, automating data collection, and integrating disparate systems into one. This allows #businesses to receive clean, organized, and #accurate datasets in real time. https://lnkd.in/e7g_DwZg #AIforBusiness #ArtificialIntelligence #AIstrategy #BusinessGrowth #StartupAI

How to get started with Data Strategy!!! Building a data-driven application? A solid data strategy turns raw data into real business value. Here’s a simple technical framework to get started: 1. Set Clear Business Goals What problem are you solving? Improving customer experience, enabling predictive analytics, or automating decisions? Clear goals drive your data strategy. 2. Map Your Data Sources Think beyond databases—include APIs, user events, logs, and external data. Ensure data is clean, structured, and easily accessible. 3. Leverage Modern Data Architecture We can leverage platform like Databricks Lakehouse combines the best of Data Lakes and Data Warehouses—unified storage, strong governance, and fast analytics in one platform. Use processing engines like Spark or Databricks for batch and real-time workloads. 4. Implement Data Modeling Early Design dimensional models (facts & dimensions) or use data vault techniques to make data easy to query and maintain over time. Well-modeled data helps deliver faster, reliable insights. 5. Plan Data Governance Define data ownership, security rules, and compliance from day one to avoid future technical debt. Start small, iterate fast, and always focus on delivering actionable insights. #DataStrategy #Databricks #Lakehouse #DataEngineering #DataModeling #BigData #CloudComputing #Analytics #AI #TechTips

🗄️ Understanding Data Storage: Databases vs. Data Lakes In today’s data-driven world, one of the most important decisions organizations make is how to store their data. Two core approaches dominate the landscape: Databases and Data Lakes. 🔹 Databases Databases are structured storage systems designed for fast queries and transactions. Store data in organized tables (rows & columns) Best for operational systems like banking apps, e-commerce platforms, and CRMs Optimized for accuracy, integrity, and quick lookups Think of a database as a neatly organized library, where every book is catalogued and easy to find. 🔹 Data Lakes Data lakes are massive repositories that store raw, unstructured, and structured data at scale. Can handle text, video, images, logs, sensor data Store first, define structure later ("schema-on-read") Power advanced analytics, machine learning, and real-time insights Think of a data lake as a huge reservoir—it holds everything, and you decide later how to use it. ✅ Key Difference: Databases → Precision, structure, speed (OLTP, OLAP) Data Lakes → Scale, flexibility, raw storage for analytics & AI 💡 In modern data architecture, organizations often use both together: databases for daily operations, and data lakes for analytics, research, and innovation. #DataEngineering #DataStorage #Databases #DataLakes #BigData #day3of73 #dataengineering

🏗️ Where does your data live — a warehouse or a lake? A Data Warehouse is structured, optimized for analytics, and built for answering business questions with speed and reliability. Think of it as a well-organized library — curated, indexed, and ready to give you answers fast. A Data Lake, on the other hand, stores raw, unstructured, and semi-structured data at scale. It’s flexible and powerful for data scientists and engineers who want to explore, experiment, train ML models, or process massive datasets. The challenge? Warehouses can be expensive and rigid. Lakes can become “data swamps” if not managed well. That’s why modern architectures often blend both in a Lakehouse — combining the governance and performance of a warehouse with the scalability and flexibility of a lake. At its core, Data Engineering isn’t just about pipelines. It’s about designing the right home for your data to deliver value at every stage. #DataEngineering #DataWarehouse #DataLake #Lakehouse #BigData #Analytics #MachineLearning