Microsoft Fabric Tech
Microsoft Fabric is changing the way we think about modern data engineering. Instead of stitching together separate tools for ingestion, transformation, storage, orchestration, governance, and reporting, Fabric brings the entire workflow into one unified ecosystem. A typical Fabric data engineering journey can look like: 🔹 Ingest data using Data Pipelines, Dataflows Gen2, or streaming 🔹 Store and organize it in OneLake and Lakehouse 🔹 Transform at scale with Spark, SQL, and Notebooks 🔹 Orchestrate workflows with Data Pipelines 🔹 Serve trusted data through Power BI and semantic models 🔹 Monitor, govern, and deploy using Monitoring Hub, Purview, Git, and deployment pipelines The interesting part isn't any single feature — it's how naturally these components work together across the complete data lifecycle. OneLake underneath. Multiple workloads on top. One connected data platform.
Datacomplexity
Hello! I am new to Power BI and trying to do somethign quite complicated - or so I believe. I have two columns, one with let's say 'numbers X'. I have another column with let's say 'numbers Y'. I would like to return all X values with the same Y value for selected values in column 'numbers X', but not returning a repeat within a selected value from 'numbers X'. The issue I am running into is the lookup function is returning a repeat selected value for itself. See example below. '1' and '2' would be returned because it was found with same 'numbers Y'. '4' would not be returned because 'numbers Y' did not have a repeat value outside of itself.Understanding Telecom Customers Through Ontology
This ontology represents how telecom companies understand customer behavior and use that understanding to make smarter business decisions. The model connects customers, segments, devices, usage patterns, offers, campaigns, churn risk, and revenue outcomes to explain how customer activity influences business actions. For example, a customer’s usage pattern can indicate churn risk, which may trigger a targeted campaign or personalized offer. Device type can also influence usage behavior and revenue generation. The goal of this ontology is not to model raw data tables, but to create a shared business understanding that both humans and AI systems can use to reason about telecom customer journeys and customer-centric decision making. My Ontology Link : https://microsoft.github.io/Ontology-Playground/#/share/eJy1l82O2zYQx1_FYK4y4I_98PrW7CZATinSzSlYCDQ1komlSHU4cios9gXaQ5Cktx4KFEX7AHmxPEIgybYYm5Q3xu7Ntv4z_PvH4XB0x4wmo0xWsfkd0zwHNmdvdQJoietE6mxwDQqEyQeXpSWTA9rB9RJNmS0HrzehEUvACpQFSaPZnLGIgSZJ1XVVgGXzd3dMJmzONLwftg-GMxZtlruClRTgSyJF8-nr33_-zyImjDLI5uzZaDSbnE9YxAo0BSDJ9RrrfEmTL5YJixhVRf2TJZQ6qzPaV0ntIJWAbE5Ywn20G9jE7IY6Mg303uBtLHjBF1JJqvrUNudIxdJoiFPFs066MEYB1-z-plbv8jnv-LyBFeiyH9Dn311AL2bj8WQaBIRtwiMIbSJ5bkpNXXQCQuZcMY-UEPT-Oh5hASjNg5TWlCg8W-QFedaBvFyWqN9Ie9uL8tMXF-XLl88vRqMwSmlvj-FYhylYger9v7UKgVujD8qsMAieDfEyOXWY8LzgMtO9SP769P3x--niRRiJWGc8Ass2tPne85e3wszwXoRboVhyrftxW-JIccLJ5Vh_czSgE6_Ci_mkw_zW8gzinzkRYD_rz3-4rE8vJ1cX4yDrsklbtGmPAL6JPMQ74cTjdrGDVbsydRN9oLiVpQi_lqBFbyNtpYF24uU_7fi_TlPAXu4f_nG5X82mz0dh7qZOdwTvNu4Q7VZ16BpacSUTSVWc8Mp2QqkJMkBXWaAUvt7glLWSmWzvshhL9dDeOukA_wJZDs2VEEb88V8X8Xh0fjkOtxHbJjwC8ibyEOaNzjXcJy-gGYgOF3WB0mCNcke53RovzLHTlNejVn-j-O_7pnw-uzoJN-V1xmOa8ib0UEES6BIhzo2mZX9BKpmCqISC2BLPepMqU3FFVS2k0snqTE83EUNQvEZkl7Jw5k0ENWwegR2SGY6dhrz-OSbDIpaiyfdbN5n9kUxwTKTmzdA3Z0ZDnTfn2jMDb_d414VzaniagiDrtTDzWDh5JAtOrUmdqrr3gt_F9AlBjDoXxDGDAAifhcmehXrtH_dw8TALp09owXkTKtDkhgI74fMwfSQPztsGocwyQL-HM4-H08ephjO3JBMpeIiD72ieBTkY7XlxCnlw5uIMNGDQw_jpToXTo2y5qEMW4Tbl8_FYNeFMT_DbUi5k4Gz4POz3qeM8OH1qAcrozA5-gET4hPqr4uY-YgvZXPL1JXJz_w3ClcQa https%3A%2F%2Fmicrosoft.github.io%2FOntology-Playground%2F%23%2Fshare%2FeJy1l82O2zYQx1_FYK4y4I_98PrW7CZATinSzSlYCDQ1komlSHU4cios9gXaQ5Cktx4KFEX7AHmxPEIgybYYm5Q3xu7Ntv4z_PvH4XB0x4wmo0xWsfkd0zwHNmdvdQJoietE6mxwDQqEyQeXpSWTA9rB9RJNmS0HrzehEUvACpQFSaPZnLGIgSZJ1XVVgGXzd3dMJmzONLwftg-GMxZtlruClRTgSyJF8-nr33_-zyImjDLI5uzZaDSbnE9YxAo0BSDJ9RrrfEmTL5YJixhVRf2TJZQ6qzPaV0ntIJWAbE5Ywn20G9jE7IY6Mg303uBtLHjBF1JJqvrUNudIxdJoiFPFs066MEYB1-z-plbv8jnv-LyBFeiyH9Dn311AL2bj8WQaBIRtwiMIbSJ5bkpNXXQCQuZcMY-UEPT-Oh5hASjNg5TWlCg8W-QFedaBvFyWqN9Ie9uL8tMXF-XLl88vRqMwSmlvj-FYhylYger9v7UKgVujD8qsMAieDfEyOXWY8LzgMtO9SP769P3x--niRRiJWGc8Ass2tPne85e3wszwXoRboVhyrftxW-JIccLJ5Vh_czSgE6_Ci_mkw_zW8gzinzkRYD_rz3-4rE8vJ1cX4yDrsklbtGmPAL6JPMQ74cTjdrGDVbsydRN9oLiVpQi_lqBFbyNtpYF24uU_7fi_TlPAXu4f_nG5X82mz0dh7qZOdwTvNu4Q7VZ16BpacSUTSVWc8Mp2QqkJMkBXWaAUvt7glLWSmWzvshhL9dDeOukA_wJZDs2VEEb88V8X8Xh0fjkOtxHbJjwC8ibyEOaNzjXcJy-gGYgOF3WB0mCNcke53RovzLHTlNejVn-j-O_7pnw-uzoJN-V1xmOa8ib0UEES6BIhzo2mZX9BKpmCqISC2BLPepMqU3FFVS2k0snqTE83EUNQvEZkl7Jw5k0ENWwegR2SGY6dhrz-OSbDIpaiyfdbN5n9kUxwTKTmzdA3Z0ZDnTfn2jMDb_d414VzaniagiDrtTDzWDh5JAtOrUmdqrr3gt_F9AlBjDoXxDGDAAifhcmehXrtH_dw8TALp09owXkTKtDkhgI74fMwfSQPztsGocwyQL-HM4-H08ephjO3JBMpeIiD72ieBTkY7XlxCnlw5uIMNGDQw_jpToXTo2y5qEMW4Tbl8_FYNeFMT_DbUi5k4Gz4POz3qeM8OH1qAcrozA5-gET4hPqr4uY-YgvZXPL1JXJz_w3ClcQa
Solar Plant Ontology
Solar Plant Ontology — Description & Working Description This ontology is a structured digital model of a solar power plant that represents all major physical and digital component such as PV arrays, inverters, batteries, substation, grid, SCADA system, sensors, weather station, energy meters, and maintenance teams and defines how they are semantically connected. It converts a traditional solar plant into a connected knowledge graph, where every asset is described with attributes and linked through meaningful relationships like energy flow, monitoring, control, and maintenance. Working The ontology works by defining entities and relationships in a graph structure: Energy Flow: Solar energy is generated by PV arrays, converted by inverters, stored in batteries, and transmitted through substations to the grid. Monitoring & Control: Sensors and energy meters continuously capture performance data and send it to the SCADA system, which monitors and controls the entire plant. Environmental Impact: Weather conditions influence PV array efficiency, helping correlate environmental factors with energy output. Maintenance: Maintenance teams manage and service critical components like inverters to ensure smooth operation. Outcome This model enables end-to-end visibility, real-time monitoring, predictive analytics, and efficient asset management, making the solar plant a smart, interconnected digital system instead of isolated components. https://microsoft.github.io/Ontology-Playground/#/share/eJy9V01v20YQ_SvB9soAktPCtm76sBMDcWJEjnMoDGJMjqQFyF1md5mENXwv0ABt0VsvRYsiPyy_ID8h4JrULkntkkqC3MTh6D3N25m3o1vCmeIJXxdkcksYpEgmZMkTEA8uEmDqwfP6dUBilJGgmaKckQkhAUGmqCouiwwlmfx8S2hMJkSWX9bfJUEDsI51YGikP3365_f_SEAinnBBJuSH09PZ8WhEApIJnqFQtGKpMDXcWUwCooqsDEglKFuTu6CZEj4rH3qznvII9E_yZM4hg4iq4vxVN-u6zNMCZFdTIaAw1V9sA67SP_79r1354ujRbDR2Vq7R_JXrlN7KffWYrJPVikYUWVT4qqbsDQqFwpR9ZiLuI__rg134aHR0cHjgLLxG9NdeZ32H8k3WObB8BZHKhS7YKdMNKIXCao7ZNjBcpOnxiXsuKkC_RsOl2XjTNiDWGD7FN5j48i4x2jBtI2HpFj6BZH4jVT2JtX3YsW9kHwbTr5TJ6-2nK54oGCLHsMazmN3mZIRbCxobyR7fP7nFev_HHo5Ton1dQ5UI4Qtc9zisTnueoQDFd8yRSTwV-Dovh_LJL95umk8XU6uRqsehsvT4kYb7Ol00RHiFQvYoc4Us9kuywCzhRYrey8ySBpnkll0v62ePD_1qi3NyNB4fPHKLo_F6hkvnODzBThMUkvBZnt7sclfr2ouiXIDfqZcKVC59yrxFUBsUy7YNvWrHh1rRaHR4tPjRrdQgHxpoQstey7BvSqlErjum7wheZjEoDLdj59MvBcoUMmARXiKkRsDzzgu3gn_-byv40_xgcez2pxLNL1-Z0a_dhq6UL2GIqE8Q4nDOc7YDyWiEDMW6OMfGxnTSCHrm8Ddbm_HocD52X3Qarm8M95yvcJ6AtGYoxoimkLR7C5LkvhkXoCzhy076onvTPbrXARF4TyY3NLP-jmxAXrQX8kZsJXja_tuieGOPj0DElEFCVUEmhDN8qPjDFFhBtue5QozlWWcHbocrMgOtmazdeQBVpJcuOWtvkp14RWahazazgg6ta7ljJ-u-cNA1FroBjPgu40LJS_64sdC0w_Wx2eiar9qDhgjJmRI8STCe2TI2o872qFeLAUQpZ1Rx0eRpBisaczb7cmxALls3uh3a3XjbHWAAgcDqAAyBHap1qhH3_f3VpTtdrTBS0nB04hVR55Lef2b1PQWUWWx2qCLq3mZfMLMpgswF2kwmUhE1r4Q9SK7vAnJDWUzZujS-67vPP_r3bQHealthCare Ecosystem
This ontology represents a comprehensive Patient Care Lifecycle, modeling the journey from clinical diagnosis to financial resolution. It acts as a semantic bridge between siloed healthcare data, connecting the Clinical World (Vitals, Diagnoses, and Procedures) with the Operational World (Facilities and Pharmacies) and the Financial World (Insurance Claims). By centering the model around the Encounter as a hub, the ontology explains how a single medical event triggers a chain of specialized actions, treatments, and billing requirements. The entities and relationships chosen are vital because they provide the necessary context for both humans and AI to reason about healthcare efficiency. Instead of looking at a Medication in isolation, this ontology links it back to a specific Provider's expertise and a Patient's diagnostic need, occurring at a particular Facility. This structure is critical for identifying real-world patterns, such as the correlation between specialty-driven procedures and insurance claim approval rates, ultimately transforming raw medical records into a coherent story of patient care and system performance. My Ontology
Sales Acquisition & Performance Ontology: Multi-Channel Funnel with Advisor Attribution
This ontology models a multi-channel sales acquisition system, capturing the full client lifecycle from the moment a prospect is generated by a marketing channel, through an advisor handling their inbound inquiry, to a signed contract and first confirmed payment. It represents a real operational environment where advisors across multiple divisions handle hundreds of inbound calls daily, and where understanding which channels generate quality prospects, not just volume, is critical to business decisions. The relationships tell story: a Channel generates a Prospect, a Prospect initiates an Inquiry, an Advisor handles that Inquiry, and a successful conversion produces a Contract fulfilled by a Payment. The Channel entity carries a composite health score weighting enrollment volume, client retention, and call quality — reflecting how the business actually evaluates acquisition sources. This ontology enables an AI agent to reason about sales performance, advisor attribution, and channel ROI from a single shared conceptual model. Sales Acquisition & Performance Ontology
