Greyskull Analytics pivots to SAP BusinessObjects

Federation: Stop moving data, start accessing it. Why are we still spending 60% of our time building pipelines just to move data from Point A to Point B? In the old world of BI, we lived by the rule of ETL (Extract, Transform, Load). In 2026, the elite SAP Data Engineers are shifting to Data Federation. What’s the difference? Instead of physically copying millions of rows into a central database—creating latency and storage costs—SAP Datasphere allows you to leave the data where it lives (S/4HANA, Snowflake, Google BigQuery) and query it in real-time. The benefits of a "Zero-Move" strategy: • Real-Time Accuracy: Your SAC dashboards reflect what’s happening in the source system right now, not what happened during last night's batch run. • Reduced Complexity: No more monitoring a dozen fragile pipelines that break the moment a source schema changes. • Cost Efficiency: You only pay for the computation you use, rather than the massive storage of redundant data copies. Is Federation always the answer? Not necessarily. For high-volume historical trend analysis, a physical "Replication Flow" is still your best friend. But for operational agility? Federation is the king. Are you still "Loading" everything into a central hub, or have you started leveraging "Live" connections for your analytics? Share your strategy in the comments! 👇 #SAPDatasphere #DataEngineering #DataFederation #SAPAnalytics #ModernDataStack #BusinessDataFabric #SAPHANA #CloudArchitecture

Why "Data Fabric" is the 2026 buzzword you actually need to care about. The era of "building bigger silos" is officially dead. In 2026, we’ve finally realized that moving data isn’t the solution—accessing it is. This is where the Business Data Fabric comes in, and if you're in the SAP ecosystem, it’s the most important architectural shift you'll face this year. But what does it actually mean for your day-to-day? Traditionally, data engineering was about rigid ETL pipelines. We spent 80% of our time moving data from point A to point B, only for it to be outdated by the time it reached the dashboard. The Data Fabric (powered by SAP Datasphere) changes the game by: • Federation over Migration: Accessing data where it lives (S/4HANA, Snowflake, or AWS) without the cost and latency of physical movement. • Semantic Consistency: Keeping the "Business Context" intact. When you pull a "Gross Margin" field, it actually means "Gross Margin" across the entire enterprise. • Self-Service Empowerment: Letting business units build their own "Spaces" while IT maintains the governance "Guardrails." In short: Data Fabric is the move from being a "Data Mover" to a "Data Orchestrator." It’s about creating a unified layer that weaves together disparate sources into a single, intelligent fabric of information. Are you still spending the majority of your week on ETL maintenance, or have you started moving toward a Data Fabric approach? Let’s discuss in the comments. 👇 #SAPDatasphere #DataFabric #SAPBTP #DataEngineering #BusinessIntelligence #Analytics2026 #SAPHANA

𝐁𝐞𝐟𝐨𝐫𝐞 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐫𝐚𝐧, 𝐈 𝐡𝐚𝐝 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐨𝐧𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: 𝐖𝐡𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 — 𝐚𝐧𝐝 𝐜𝐚𝐧 𝐰𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐫𝐮𝐬𝐭 𝐢𝐭? The answer was complicated. Data was scattered across SAP tables and hundreds of Excel files — different formats per region, merged cells, inconsistent headers, mixed data types. No schema. No governance. No single source of truth. Before the ML model could predict anything, I had to build the foundation it would stand on. Here's exactly how I moved a global supply chain's data from SAP + Excel into Snowflake — end to end. 𝑻𝒉𝒆 𝑫𝒂𝒕𝒂 𝑴𝒊𝒈𝒓𝒂𝒕𝒊𝒐𝒏 𝑨𝒓𝒄𝒉𝒊𝒕𝒆𝒄𝒕𝒖𝒓𝒆: -> 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐒𝐨𝐮𝐫𝐜𝐞 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐓𝐨𝐮𝐜𝐡 𝐀𝐧𝐲𝐭𝐡𝐢𝐧𝐠 𝑺𝑨𝑷: Sales orders, materials, and demand data lived across relational tables with complex foreign key relationships. Understanding which tables were relevant and how they joined was the first real challenge. 𝑬𝒙𝒄𝒆𝒍: Region-wise files, month-wise sheets, no enforced schema — every file was its own surprise. I audited and documented source schemas, data types, volumes, and refresh frequencies before extraction began. -> 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 — 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐎𝐮𝐭 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐋𝐨𝐬𝐢𝐧𝐠 𝐈𝐭 𝑺𝑨𝑷 → Azure Data Factory using native SAP connectors to extract structured transactional data reliably at scale. 𝑬𝒙𝒄𝒆𝒍 → Python + pandas to read, parse, and standardize files, then dropped into Azure Data Lake Storage as the raw landing zone. 𝑻𝒉𝒆 𝑹𝒖𝒍𝒆: Raw data lands untouched. Always preserve the original — so you can reprocess if something breaks downstream. -> 𝐓𝐡𝐞 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐌𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Following an ELT approach — load first, transform inside Snowflake using SQL. 🥉 𝑩𝒓𝒐𝒏𝒛𝒆 𝑳𝒂𝒚𝒆𝒓: Exact raw replica of source data. Loaded via Snowflake's COPY INTO command. Nothing transformed. Nothing lost. 🥈 𝑺𝒊𝒍𝒗𝒆𝒓 𝑳𝒂𝒚𝒆𝒓: Cleaning happens here — standardized date formats, renamed columns, null handling, deduplication, data type corrections. SAP stores numeric fields as strings — a classic trap that breaks downstream aggregations if missed. SAP and Excel data were also joined at this stage via ADF pipelines and SQL. 🥇 𝑮𝒐𝒍𝒅 𝑳𝒂𝒚𝒆𝒓: My layer. Layered complex CTEs across hundreds of underlying tables to produce a single unified "final view". 𝐓𝐡𝐞 𝐑𝐞𝐬𝐮𝐥𝐭 ✦ Hundreds of fragmented files → One trusted Gold layer ✦ Manual refresh cycles → Automated, scheduled ELT pipeline ✦ Inconsistent schemas → Single Source of Truth ✦ Slow ad-hoc queries → 10× faster analytical performance via clustering keys on date and product dimensions. The forecast was trusted because the data was trusted. That trust was built here — before the model ever ran. #DataEngineering #Snowflake #Azure #MachineLearning #DataPipeline #DataWarehouse #MedallionArchitecture #DataScience #SQL #CloudComputing #SupplyChain #AI #ML

In a world obsessed with modern data platforms, it’s easy to ask: Does SAP BusinessObjects still matter in 2026? Short answer: Yes — more than people think. While tools like Fabric, Databricks, and Snowflake are redefining how we build data platforms, many enterprises still rely on SAP BusinessObjects to deliver the final, trusted layer of reporting. Here’s why it continues to hold its ground: • Mission-critical reporting lives here Financial, regulatory, and operational reports still depend on the stability and precision BusinessObjects provides. • The semantic layer is still a superpower The Universe layer enables consistent, governed metrics — something modern stacks are actively trying to replicate. • Deep SAP integration For organizations running SAP ecosystems, BusinessObjects remains a natural and efficient consumption layer. • Migration is not trivial Thousands of reports, years of embedded logic, and user adoption make full replacement a massive effort. • ROI from a licensing perspective is often underestimated BusinessObjects was traditionally sold as a perpetual license, meaning most organizations today only pay annual maintenance. Compared to subscription-based models like Power BI or Tableau, this significantly reduces ongoing costs — making a full replacement difficult to justify purely from a licensing ROI standpoint. • Hybrid is the new standard Modern data platforms + BusinessObjects = a pragmatic, enterprise-ready architecture. Let’s be clear: BusinessObjects is not leading innovation in AI or real-time analytics. But it doesn’t need to. 👉 It excels at what matters most in many organizations: Delivering consistent, trusted, and governed reporting at scale. In 2026, the question is no longer “replace or not?” It’s “how do we integrate it into a modern data strategy?” #DataAnalytics #BusinessIntelligence #SAP #DataStrategy #ModernDataStack #EnterpriseIT

At Wartsila, My Journey closely mirrored the organization’s analytics evolution — moving from Traditional, centrally managed Reporting to a modern, Cloud‑native, self‑service BI ecosystem that supports Strategic decision‑making.  I started by Strengthening the Enterprise BI foundation, working with business stakeholders to understand Reporting gaps and Operational challenges. Using the MSBI stack (SSIS, SSAS, SSRS), I delivered reliable ETL pipelines, semantic models, and Enterprise reports that supported day‑to‑day decision‑making and established Trust in data.  As the Business Matured, I took on Ownership of Program‑specific analytics initiatives, delivering tailored BI solutions for key programs such as the Spare Parts Development Program, Lead Management Program, and Energy Transition Lab. These Solutions enabled better supply chain visibility, sales funnel transparency, and sustainability insights, helping program teams move from static reporting to Actionable analytics.  With Increasing demand for scale and Agility, My role Expanded into Platform Modernization and cloud Enablement. I played a key role in cloudifying the Enterprise Data Warehouse, transitioning from legacy architectures to cloud platforms to improve performance, scalability, and cost efficiency. I also helped establish Azure Analysis Services and led a SAP HANA BI Proof of Concept, contributing to Wartsila’s long‑term Analytics Roadmap.  As Power BI Adoption Accelerated across the organization, I led the design and rollout of Power BI Service environments. This included defining governance models, workspace strategies, secure on‑premises data gateways, dataset refresh orchestration, application lifecycle management, and access controls — enabling a shift toward self‑service, business‑owned analytics while maintaining enterprise governance.  Alongside modernization efforts, I ensured continuity by leading the SQL Server 2016 SSRS upgrade, modernizing the reporting infrastructure and aligning legacy assets with evolving BI tools.  Across all Phases of this jo— from requirement discovery and data modelling (fact & dimension schemas) to Report development, Performance tuning, testing, and production rollout journey, I consistently owned end‑to‑end BI delivery— Ensuring solutions remained both Technically Robust and aligned with business outcomes.  IMPACT: Helped transition Wartsila from Cognos and legacy SSRS to Power BI and Azure‑based Analytics. Enabled Data‑driven decision‑making Across supply chain, sales, and sustainability Initiatives. Established a Scalable, Governed BI Ecosystem that Empowered business users. Positioned Business Intelligence as a Strategic Enabler of Wartsila’s digital Transformation Journey.

Thank you to Jean-Francois Monteil and Sigal Gelbart for a session that moved fast and delivered. Seeing AI agents and decision steps embedded right inside a Talend pipeline is one of those moments where the concept stops being theoretical. Data flowing from sources like SAP and Kafka, with context-aware routing deciding in real time where it goes and what happens next. No manual intervention. No waiting. The pipeline thinks. For anyone managing complex data flows across multiple source systems, this is worth paying close attention to. The gap between data arriving and action being taken just got a lot smaller. How many of your current pipelines are still routing data manually? Join our webinar on the 21st April to dive into this topic more: https://lnkd.in/eTQW4nYq #QlikConnect #QlikAmbassador Qlik Catalyst BI

SAP Datasphere and SAC Together: Here's What the Real-World Data Flow Actually Looks Like 🚀 On paper, Datasphere feeds SAC. In practice, the integration has layers — and if you don't understand each one, you'll hit performance issues, data mismatches, and frustrated business users. 💡 Here's the actual flow, layer by layer: 1️⃣ Data Ingestion into Datasphere: Source systems — S/4HANA, BW, third-party platforms — push or are pulled into Datasphere via Connections. Data lands in the Persistence Layer (replicated tables) or stays virtual (remote tables). Transformations happen in Data Flows and Views within the relevant Space. 📊 2️⃣ Business Layer Modeling: Once the data is clean and modeled in the Analytical Dataset or Fact/Dimension structure, the Business Builder creates Consumption Models — entities designed specifically for SAC to query. This layer is the handshake between technical data and business meaning. 🛠️ 3️⃣ SAC Consumption via Live Connection: SAC connects to Datasphere via a Live Data Connection. When a business user opens a story or planning model, SAC fires a query to Datasphere in real-time. There is no data duplication in SAC — the numbers always reflect the latest state of your Datasphere model. ✅ What makes this integration work well in practice: • Model optimization: Analytical Datasets in Datasphere should be pre-aggregated where possible — raw transactional data at the SAC query layer creates performance problems. 🧩 • Space governance: Each business domain should own its Space — this prevents cross-domain data pollution and keeps responsibilities clear. 🔒 • Consistent semantics: KPIs defined in the Business Builder flow directly into SAC measures — eliminating the risk of different teams calculating the same metric differently. ⏱️ Final Thought: The Datasphere-SAC integration is powerful precisely because the responsibilities are clean — Datasphere owns the data, SAC owns the experience. The teams that respect that boundary build solutions that scale. The teams that don't end up with duplicated models and reconciliation headaches. 🏗️ What's the most complex data flow challenge you've solved in a Datasphere-SAC implementation? #SAP #SAPDatasphere #SAPAnalyticsCloud #DataIntegration #DataArchitecture #BusinessIntelligence #SurbhiVerma

How to Connect Datasphere to Snowflake via Zero-Copy Sharing One of the most powerful features in SAP Business Data Cloud that most teams haven't explored yet: zero-copy data sharing with Snowflake. Here's how it works and why it matters. WHAT IS ZERO-COPY SHARING? Zero-copy means Snowflake can read your Datasphere data products directly, without replicating the data. No ETL pipelines. No data duplication. No synchronisation jobs. The data stays in Datasphere, governed and managed. Snowflake queries it in place. HOW TO SET IT UP: Create your data product in Datasphere (Spaces, models, semantic labels, published to Data Marketplace) Enable BDC Connect for Snowflake in your Datasphere tenant Configure the sharing endpoint and authenticate the Snowflake account From Snowflake, access the shared data product as if it were a native Snowflake table Query, transform, and feed into your Snowflake analytics and AI/ML workloads WHEN TO USE THIS: → Your data science team works in Snowflake but needs governed SAP data → You want to feed SAP actuals into Snowflake-based ML models without building ETL → Cross-platform analytics: combine SAP data with non-SAP data already in Snowflake → Reduce data sprawl: stop replicating SAP data into every analytics platform WHAT TO WATCH OUT FOR: → Performance depends on the Datasphere model design. Pre-aggregate where possible. → Not all Datasphere objects are eligible for sharing. Only published data products work. → Security and governance travel with the data. Snowflake users see what the data product exposes, nothing more. → Snowflake BDC Connect is in controlled availability. Check your Datasphere version for GA timeline. WHY THIS MATTERS: This is the architecture pattern that makes BDC a genuine enterprise data platform, not just an SAP data warehouse. SAP data stays governed in Datasphere. External platforms consume it without duplication. One source of truth. What's your current approach to getting SAP data into Snowflake?

𝗔𝗜-𝗮𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗱𝗮𝘁𝗮 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲 𝘁𝗵𝗶𝗻𝗴 — 𝗶𝘁'𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗶𝗻𝘀𝗶𝗱𝗲 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲. Snowflake's Cortex Code is changing how data engineering teams build and deploy dbt projects. Instead of manually writing models, configuring tests, and tuning SQL performance across multiple tools, teams can now do it from a single prompt or CLI command. This matters for organizations managing complex data pipelines — particularly those migrating from legacy platforms like SAP or on-premise warehouses. The ability to generate dbt models automatically, run tests, and optimize SQL in one unified environment dramatically reduces development time and human error. For data engineers, it means less time on boilerplate and more time delivering insights that actually move the business forward. For data leaders, it's a compelling case for accelerating your Snowflake adoption roadmap. If your team is still building dbt workflows the hard way, this is worth your attention. Read the full article here: https://lnkd.in/gAfc7Dmb #Snowflake #dbt #DataEngineering #CortexAI #DataTransformation

SAP Datasphere Architecture — Data Fabric Explained Simply SAP Datasphere Isn't Just a Database: It's a Data Fabric — Here's What That Actually Means 🚀 The term "Data Fabric" gets thrown around a lot. But when you're sitting in front of SAP Datasphere, what does it actually look like in practice? Let me break it down simply. 💡 Here's how the architecture comes together: 1️⃣ The Data Layer (Spaces): Datasphere organizes everything into Spaces — isolated environments for different business domains like Finance, Supply Chain, or HR. Each Space has its own data, users, and connections. This is your governed data foundation. 📊 2️⃣ The Federation Layer (Remote Tables & Data Access Controls): DSP doesn't always copy data. It can query data in-place from S/4HANA, BW, or even non-SAP sources using Virtual Tables. Data stays at the source, but you access it as if it's local. This is the fabric in action — weaving sources together without full replication. 🛠️ 3️⃣ The Business Layer (Business Builder): This is where technical models become business-friendly. You define Entities, Relationships, and Measures in plain business language. SAC then consumes this layer, giving business users clean, consistent metrics — no raw SQL in sight. ✅ Why this architecture wins: 🔹Flexibility: You choose to replicate or federate based on performance needs and data volume. 🧩 🔹Governance: Spaces enforce clear data ownership and access boundaries across the enterprise. 🔒 🔹Simplicity for Consumers: The Business Layer abstracts complexity so analysts get trustworthy data, not raw tables. ⏱️ 👉Final Thought: A Data Fabric isn't about storing everything in one place — it's about making every data source accessible, governed, and meaningful from one place. Datasphere is SAP's answer to that challenge, and understanding its layers is the first step to using it right. 🏗️ 💬How are you currently managing data federation vs. replication in your Datasphere landscape? #SAP #SAPDatasphere #DataFabric #DataArchitecture #DataGovernance #S4HANA #SurbhiVerma