Integration Challenges in Cloud Storage

Living in a multi-cloud world is very common these days. Data is distributed across multiple cloud providers. But the biggest challenge remains securely sharing governed , high-quality datasets across these environments. Most importantly, without duplicating data, breaking governance, or relying on complex ETL pipelines. Imagine your enterprise has workloads split across Azure and AWS (or GCP). The analytics team in Azure needs to access curated datasets stored in AWS, and vice versa. Traditional approaches involve copying datasets between storage, building ingestion pipelines (needs a lot of maintenance 😒). This whole process loses end-to-end governance as data moves. Also, it increases cost, latency, and compliance risks. 🚀 How do you solve this problem? Databricks Unity Catalog + Delta Sharing With UC, your data objects (tables, views and volumes) live in a governed metastore with consistent permissions and lineage tracking. Delta Sharing extends this by enabling open, secure data sharing across clouds without physically moving the data. 💡 How to make it work? The illustrative architecture shown consists of two-cloud environments: ☑️ Azure: Hosting a Unity Catalog metastore with managed tables pointing to ADLS containers. ☑️ AWS: Hosting another Unity Catalog metastore with managed tables pointing to S3 buckets. 🌊 The data flow: 1️⃣ Table Registration: ☘️ Tables are created Catalog A -> Schema B -> Table C in each cloud's Unity Catalog. ☘️ These can be managed tables or external tables. 2️⃣ Delta Sharing Setup: ☘️ In the source cloud, you need to define a share in UC containing the desired tables. ☘️ UC enforces fine-grained access control down to table and column level. 3️⃣ Cross-Cloud Sharing: ☘️ Using Delta Sharing, these tables are made available to consumer in AWS. ☘️ The consumer sees the data as a read-only shared table in their Unity Catalog, under a shared schema. 4️⃣ Secure Access Control: ☘️ Governance policies set in the source Unity Catalog are enforced end-to-end, even across clouds. ☘️ This includes row/column-level security and audit logging. 5️⃣ Consumption: ☘️ The consumer in AWS can query the shared Azure data (and vice versa) directly from their own workspace as if it were a native table. 🍁 There's a key consideration that I'd like to share: The staging catalogs (shared/landing) at both sides are required because of fine grained access controls acting on Catalog A tables on both cloud environments. Probably, in the future, it might not be needed as Attribute Based Access Controls comes into effect. #Databricks #UnityCatalog #DeltaSharing #Azure #AWS

💭 Ever faced the challenge of keeping your data consistent across regions, clouds, and systems — in real time? A few years ago, I worked on a global rollout where CRM operations spanned three continents, each with its own latency, compliance, and data residency needs. The biggest question: 👉 How do we keep Dataverse and Azure SQL perfectly in sync, without breaking scalability or data integrity? That challenge led us to design a real-time bi-directional synchronization framework between Microsoft Dataverse and Azure SQL — powered by Azure’s event-driven backbone. 🔹 Key ideas that made it work: Event-driven architecture using Event Grid + Service Bus for reliable data delivery. Azure Functions for lightweight transformation and conflict handling. Dataverse Change Tracking to detect incremental updates. Geo-replication in Azure SQL to ensure low latency and disaster recovery. What made this special wasn’t just the technology — it was the mindset: ✨ Think globally, sync intelligently, and architect for resilience, not just performance. This pattern now helps enterprises achieve near real-time visibility across regions — no more stale data, no more integration chaos. 🔧 If you’re designing large-scale systems on the Power Platform + Azure, remember: Integration is not about moving data. It’s about orchestrating trust between systems. #MicrosoftDynamics365 #Dataverse #AzureIntegration #CloudArchitecture #PowerPlatform #AzureSQL #EventDrivenArchitecture #DigitalTransformation #CommonManTips

For most of my career, the destination was a database. A schema was defined. A table was waiting. You transformed the data, loaded it, and moved on. Lakehouses changed the contract. The destination is no longer just a rigid table with types enforced at write time. It's often a flexible storage layer that accepts data in varied formats and defers more of the validation to later stages. That sounds like freedom. But for the engineer sending the data, it quietly shifts the responsibility. What typically changes at the integration boundary: → Schema enforcement often moves from the target to the pipeline - if you don't validate before landing, you might be the last line of defense → File format matters more than it used to - choosing between formats has real performance and cost implications, and picking wrong compounds over time → Partitioning becomes an integration decision, not just a storage one — how you write affects how efficiently others read → Small files become a real problem - high-frequency ingestion without compaction can quietly degrade query performance → Metadata management gets heavier - the lakehouse gives you flexibility, but tracking what landed, when, and in what shape is now your job Lakehouses don't eliminate complexity. They redistribute it. What used to be the database's responsibility is now the pipeline builder's responsibility. If the boundary is loosely defined, the lake becomes a landfill. If contracts are explicit, the lake becomes an asset. What's the hardest part of integrating into a lakehouse today - contracts, latency, cost, or something else? #DataEngineering #DataArchitecture #SystemDesign

Interesting conversation this morning with a #utility looking to drive new use cases with their AMI data. One of the biggest challenges they are facing right now is 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 #Utilities leverage an extensive ecosystem of legacy systems—SAP for enterprise resource planning, Itron, Inc./Oracle for AMI data management, Siemens for grid automation, and various #SCADA, #GIS, and #CIS platforms. These systems were never designed for cloud-native environments, leading to: 1. 𝗗𝗮𝘁𝗮 𝗦𝗶𝗹𝗼𝘀 & 𝗟𝗮𝘁𝗲𝗻𝗰𝘆: AMI data needs to seamlessly integrate with multiple platforms to drive DAP (Data Analytics Platform) use cases. Many utilities struggle to create a unified data fabric due to disparate architectures. 2. 𝗛𝘆𝗯𝗿𝗶𝗱 & 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗚𝗮𝗽𝘀: Many critical OT workloads must remain on-prem for reliability and latency reasons, necessitating a hybrid cloud strategy that legacy vendors often fail to support. 3. 𝗖𝘂𝘀𝘁��𝗺 𝘃𝘀. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗔𝗣𝗜𝘀: Vendors like Itron, Inc., Landis+Gyr, and Siemens often have proprietary interfaces that require heavy customization when integrating into cloud-based AI/ML models or analytics platforms. 💡 TechBlocks Insight: The future lies in #MACH architecture (#Microservices, API-first, #Cloud-native, Headless) and the adoption of data mesh strategies. Utilities embracing Kafka-based event streaming, GraphQL for flexible APIs, and AI-driven automation will gain a significant advantage in achieving seamless integration.

Kubernetes persistent storage is not difficult because Kubernetes lacks storage support. It is difficult because most environments assemble storage from multiple independent products that coordinate through APIs instead of operating as one system. A typical enterprise Kubernetes storage stack often includes: - External storage array or distributed storage platform - CSI driver - Snapshot framework - Backup software - Disaster recovery tooling - Separate monitoring and lifecycle management Each layer works. The operational overhead appears during upgrades, failures, restores, and recovery events. CSI standardized portability between Kubernetes and storage systems. That was a major architectural improvement. It did not eliminate fragmentation between operational domains. This is why many platform teams spend more time coordinating storage infrastructure than provisioning it. The next shift in Kubernetes infrastructure is not another abstraction layer. It is consolidation of the infrastructure substrate underneath Kubernetes itself. When storage, snapshots, replication, virtualization, and Kubernetes integration share the same platform services, the operational surface area shrinks dramatically. The discussion around Kubernetes storage should move beyond “Does it work?” and toward “How many independent systems are involved when it breaks?” For a deeper dive, search StorageSwiss for my latest article. #Kubernetes #CloudNative #Storage #PlatformEngineering #DevOps #Infrastructure #DataProtection #Virtualization