How banks in MENA can leverage the Databricks Lakehouse Platform for data management and AI.

🚀 Excited to share insights on the evolving world of data management this August 2025! As organizations increasingly rely on data to drive decisions, staying ahead of the curve is critical. This month, the spotlight is on AI-driven data governance and real-time data integration. Key trends shaping the landscape: 1️⃣ AI-Powered Data Governance: With regulations tightening and data volumes exploding, AI is transforming how we ensure compliance, security, and quality. Automated tools are now smarter, catching anomalies and ensuring trust in data like never before. 2️⃣ Real-Time Data Integration: Businesses are moving beyond batch processing to real-time pipelines, enabling faster insights and agile decision-making. Solutions like Apache Kafka and cloud-native platforms are leading the charge. 3️⃣ Data Fabric Adoption: The rise of data fabric architectures is simplifying complex ecosystems, unifying disparate data sources, and empowering seamless access across hybrid environments. As we navigate this dynamic space, the focus is clear: leverage automation, prioritize security, and embrace scalability. What's your take on these trends? How is your organization tackling modern data management challenges? Let's connect and discuss! 💬 #DataManagement #AI #DataGovernance #RealTimeData #DataFabric #TechTrends #PriyankSompura #Facilloc

💡 Data Governance Delivers: $12M Saved, 315% ROI, and AI That Finally Works In 2025, the benefits of strong data governance are undeniable 👇 📈 ROI Gains Enterprises see an average ROI of 315% over 3 years. Many recover their initial investment in just 6–12 months. 💸 The Cost of Poor Data $12.9M lost annually due to bad data. $14.8M in compliance fines vs. $5.5M prevention cost. ⚠️ The Governance Gap Nearly 40% of companies lack governance frameworks. 44% of financial firms wrestle with fragmented silos. 💻 AI + IT Pressure With AI everywhere, up to 50% of IT budgets go to fixing disconnected data sources. 🚀 How Flex83 Helps ✅ Edge-to-Cloud Architecture → Unified data across silos ✅ Real-Time Monitoring & Alerts → Issues caught before impact ✅ Metadata Validation → Clean, trusted data at scale ✅ AI/ML Pipelines → Dynamic, automated governance Flex83 transforms governance from a roadblock into a strategic growth engine — cutting costs, accelerating ROI, and enabling innovation with confidence and control. 🔎 Question for Leaders: 👉 Are you still paying the price of inaccurate data, or are you ready to make governance your growth advantage? 📩 Let’s talk: ankit.sharma@iot83.com 👉 #DataGovernance #DataQuality #AIandData #DigitalTransformation #DataPrivacy #IIoT #Innovation #BusinessGrowth

Delta Sharing ✨ is an innovative protocol designed to securely exchange cloud-based data, allowing effortless collaboration across a wide range of platforms and eliminating obstacles that can arise from incompatibility between various systems. 💖 Key Features: 🌟 Databricks Integration: Delta Sharing enables secure data exchange both within the Databricks ecosystem and between Databricks and external systems, including custom data-sharing setups for non-Databricks sources. ❇️ Governance: The platform is closely interwoven with Unity Catalog, supporting comprehensive governance and catalog management. 🩷 Multi-Type Sharing: Delta Sharing goes beyond traditional data by accommodating the transfer of different models, volumes, and AI-related documents, with thorough backing for both data-centric and AI asset sharing. 🧡 Advantages: 💥 Cross-Platform Compatibility: Facilitates data sharing utilizing open-source formats like Delta and Parquet, ensuring smooth interoperability between public cloud systems, on-prem setups, and various platforms. 💛 Streamlined Data Transfer: Removes the need for redundant copying or repeated movement of information, fostering efficient data workflows. 💚 Unified Governance: Presents one centralized solution for monitoring access, managing permissions, and maintaining compliance with regulations. 💙 Flexible Sharing Options: Capable of exchanging not just datasets but also streams, ML models, and custom files, supporting diverse analytics and operational needs. 🩵 Reduced Costs: Lowers infrastructure and operational costs by bypassing the requirement for multiple isolated environments, benefiting both data providers and consumers. 🤎 Application Examples: 💫 Enterprise Collaboration: Deploy Delta Sharing to build a federated data system, improving communication between different teams or subsidiaries while reducing unnecessary data duplication or transfers. 🩶 Business-to-Business Data Exchange: Safeguard the sharing of critical information with external collaborators, eliminating demands for all users to be on the Databricks platform. 🤍 Monetizing Data Assets: Extend valuable datasets, models, and dashboards across organizations, offering accessible data products without enforcing platform dependency. ❣️ #databricks #dataengineering #dataanalyst #etl #deltasharing #ai #governance #unitycatalog

Financial firms are tackling an explosion of complex data - from structured trading records to unstructured research reports. Scaling data lakes that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 is now the critical next step. Is your organization facing these challenges? ⏳ Legacy systems slowing down under data growth 🐢 Lengthy analytics delaying insights 🔒 Managing compliance risks across complex data environments 🤯 Difficulty delivering trusted, unified data access at scale Discover how financial institutions can overcome these barriers with 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. Our latest guide, “𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 𝗶𝗻 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗙𝗶𝗿𝗺𝘀,” offers: ✅ Real-world lessons and case examples ✅ A clear framework covering ingestion, storage, analytics, governance, and consumption ✅ Key trade-offs and how to navigate them ✅ A practical, step-by-step roadmap to build and mature your data lake ✅ A maturity model to benchmark your AI readiness progress 📥 Download the attached PDF to unlock actionable insights and accelerate your AI transformation journey. 💬 What’s the biggest challenge your data teams face at scale? Or which breakthrough moved your analytics forward? Let’s discuss in the comments! #DataLakes #AIinFinance #Fintech #CloudData #DataGovernance #MachineLearning #FinancialServices #BigData

#DataModernization - 𝐖𝐡𝐲 𝐢𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬   : Data Modernization Is No Longer Optional -  It’s a Business Need Over the past decade, I’ve seen many enterprises invest heavily in data platforms, but still struggle with 𝐬𝐢𝐥𝐨𝐬, 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐠𝐚𝐩𝐬. The reality is - business expectations from data have changed. It’s no longer about just storing information; it’s about making it 𝐭𝐫𝐮𝐬𝐭𝐞𝐝, 𝐚𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐟𝐚𝐬𝐭 𝐞𝐧𝐨𝐮𝐠𝐡 to drive real-time decisions. This is where 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 comes in. To me, it’s not a buzzword -  it’s the foundation for: >>Moving away from legacy ETL-heavy, rigid systems >>Simplifying data pipelines and democratizing access >>Building stronger governance, security, and compliance >>Enabling AI/ML and advanced analytics at scale Key Focus Areas to Accelerate Modernization: - Scalable Data Engineering : Unify batch and streaming data pipelines for greater efficiency - Collaborative Analytics : Enable data engineers, scientists, and analysts to work seamlessly on a single platform - Governance with Unity Catalog : Ensure consistent access controls, data lineage, and audit capabilities -  AI/ML Readiness :Leverage MLflow, model serving, and deep integration with enterprise AI strategies In my experience, successful modernization isn’t just about choosing the right tools - it’s about having a strong adoption strategy, solid governance, and a well-defined operating model. #DataModernization #DataStrategy #BigDataPlatforms #Cloud #DataInfrastructure

𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐌𝐞𝐬𝐡: 𝐇𝐨𝐰 𝐌𝐂𝐏 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞   Traditional data mesh architectures promise decentralized ownership with federated access, but in practice still require custom integration work for each data source.   MCP introduces a standardized protocol layer that abstracts away the underlying API differences between heterogeneous data products. Our implementation sits between MCP clients and our existing data products (TCGA, clinical trials, biomarkers, etc.), handling discovery, authentication, and data transformation through a common interface.   The technical shift is significant: instead of building point-to-point integrations, we're seeing emergence of what might be called an "agentic mesh" - where AI agents can computationally discover and query federated data sources without knowing their underlying schemas or APIs. This reduces the integration complexity from O(n²) to O(n) when connecting multiple data products.   Key architectural lessons (We are still learning) Here are some of the challenges we faced: - Connection pooling and caching become critical when serving multiple concurrent agent requests. - Data product adapters need to handle version differences gracefully. Streaming support is essential for large datasets that agents typically request.   The broader implication is that MCP may become the missing application layer for federated data architectures. Teams building data products should consider MCP compatibility as a first-class requirement, not an afterthought, as it determines whether their data can participate in automated cross-domain analysis workflows. Let us know your thoughts or if you want to try out our server (https://lnkd.in/ei8s4-tT)

🚀 Modern Data Integration in Data Engineering In today’s data-driven world, organizations need real-time, reliable, and scalable pipelines to transform raw data into actionable insights. This architecture highlights the critical flow: 🔹 Data Sources → APIs, Databases, Applications 🔹 Ingestion Layer → Streaming (real-time), CDC (change data capture), Batch loads 🔹 Raw Zone → Object stores & landing areas for unprocessed data 🔹 ETL/ELT Transformation → Standardization, cleansing, enrichment 🔹 Curated & Conformed Zones → ✅ Data Lakes & Spark platforms for unstructured & semi-structured analytics ✅ Data Warehouses for structured, business-ready insights 🔹 Data Consumers → BI dashboards, Analytics, AI/ML models, and Data Science teams 💡 Key Takeaways: Streaming + Batch = Hybrid data strategy for real-time + historical insights Data Lakes + Warehouses complement each other → flexibility & governance AI/ML thrives only when upstream data engineering is robust Manage & Monitor with Control Hub ensures governance, observability & reliability Modern enterprises that invest in scalable pipelines not only enable faster decision-making but also unlock new opportunities in predictive analytics and AI innovation. #DataEngineering #ModernDataIntegration #BigData #DataPipelines #StreamingData #ETL #DataLake #DataWarehouse #AI #MachineLearning #BusinessIntelligence #Analytics #CloudData #DataOps

The truth about data migration in 2026: For too long, migration was treated as a checkbox. A “necessary step” in a project plan. But today, it’s a strategic battleground where agility, security, and innovation determine who leads and who lags behind. Truth #1: Hybrid-Cloud is essential- Vendor lock-in limits growth. Leading organisations are building flexible, multi-cloud ecosystems to scale and adapt with speed. Truth #2: AI is transforming governance- Manual processes cannot keep pace. AI-driven compliance and risk detection now set the standard for speed, safety, and accuracy. Truth #3: Downtime is unacceptable - Extended outages erode trust, revenue, and competitive edge. Continuous synchronization and streaming migration are the new benchmarks. Truth #4: Sustainability matters - Energy consumption is under scrutiny. Businesses are expected to migrate smarter, reducing waste and optimizing efficiency. Truth #5: Data quality is non-negotiable - Migration does not fix broken data. Inconsistent, duplicated, or low-quality information will only compound problems after the move. The ultimate truth: Data migration is no longer a backroom IT activity,it's a boardroom priority. Every decision impacts resilience, reputation, and revenue. This is where Boon Solutions delivers measurable advantage. We bring proven expertise across industries where precision is critical mining, finance, government, healthcare, education, and infrastructure. — End-to-end Data services: strategy, automation, analytics, AI and integration. — ISO/IEC 27001:2022 certified -security embedded at every step. — Technology-agnostic capability: AWS, Databricks, Microsoft Fabric, Snowflake, Qlik and Talend. With Boon Solutions, migration becomes more than a technical success it becomes a competitive edge. #DataMigration #DigitalTransformation #CloudStrategy #HybridCloud #AITech #ZeroDowntime #DataGovernance #EnterpriseIT #TechLeadership #SustainableTech #BoonSolutions #DataAnalytics #ISO27001 #Innovation #Boonsolutions #Data #AI #ML #Datamigration #Datamovement

🔍 From Warehouses to Lakehouses… and now “Data Mesh”? For years, we’ve debated Data Warehouses vs. Data Lakes vs. Lakehouses. But the next evolution is already here: Data Mesh. ⚡ What’s different? Instead of centralizing all data in one giant system, Data Mesh promotes: Domain-oriented ownership (teams own their data as products) Decentralized architecture (no single bottleneck) Self-serve data platforms (engineers + analysts can move faster) Federated governance (standardization without slowing innovation) 💡 Why it matters: Warehouses are great for BI Lakehouses are powerful for AI/ML But Data Mesh is about scaling people + processes, not just storage. 👉 In short: Warehouse = What happened? Lakehouse = What’s happening + What’s next? Mesh = Who owns it + How it scales across the org #DataEngineering #DataMesh #Lakehouse #Analytics #AI #FutureOfData

🚀 Data Pipelines: The unsung heroes of streamlined data flow! 💡 1. Data pipelines are the backbone of efficient data processing, making sure information flows seamlessly from source to destination. 🌐 2. By automating data movement and transformation, pipelines save time and reduce errors, empowering organizations to make decisions faster and more accurately. ⏱️ 3. To build a robust data pipeline, consider factors like scalability, reliability, and monitoring to ensure smooth operations even in the face of unexpected challenges. 🛠️ 4. Remember: Data pipelines thrive on data quality and consistency. Garbage in, garbage out – so always prioritize data integrity. 🧹 5. Embrace tools like Apache NiFi, Airflow, or AWS Glue to streamline your data pipeline setup and maintenance, boosting productivity and reliability. 🛁 6. Pro tip: Regularly monitor and optimize your data pipeline performance to identify bottlenecks and enhance efficiency, keeping your data operations running smoothly. 🚦 Takeaway: Data pipelines are the unsung heroes of data management, enabling organizations to harness the power of their data effectively. 🦸♂️ CTA: What's your experience with data pipelines? Share your insights in the comments below! 💬 #DataPipelines #BigData #DataManagement #DataEngineering #DataScience #TechTrends #Analytics #Automation #AI #MachineLearning #CloudComputing #DigitalTransformation Future Perspective: As we move into the next decade of technology, artificial intelligence will play a key role in optimizing and evolving data pipelines, maximizing efficiency and innovation. 🌟 Mandatory Footer: Connect with me on LinkedIn for more tech insights: https://lnkd.in/g8vg4iSy