Utilities: Smart Meter Data to Billing and Demand Forecasting 📋 Did You Know? The global roll-out of smart meters generates more data in a single day than most utility companies used to collect in an entire decade. While traditional meters were read once a month, or even once a quarter, smart meters transmit data at intervals […]
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Energy: SCADA Streams to Reliability Analytics 🔌 Did you know? The average modern wind turbine or smart substation generates roughly 1 to 2 terabytes of data every month. However, historically, less than 5% of that sensor data was actually used for decision-making. Most of it was simply discarded or “siloed” in SCADA systems, serving as a
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Logistics: Live ETA Prediction Pipelines from Fleet + Orders 🚚 Did you know? The “Last Mile” is famously the most expensive and inefficient part of the supply chain, often accounting for up to 53% of total shipping costs. The Evolution of Real-Time Logistics Live ETA (Estimated Time of Arrival) prediction pipelines represent the shift from reactive
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From DB2 to Lakehouse: Real-Time CDC Without Re-Platforming 💻 Did you know? Mainframe systems like DB2 still process approximately 30 billion business transactions every single day. Despite the rush toward modern cloud architectures, the world’s most critical financial and logistical data often resides in these “legacy” environments, making them the silent engines of the global economy.
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Streaming Upserts Done Right: Deduping and Idempotency at Scale 💻 Did you know? In many high-velocity streaming environments, the “same” event can be sent or processed multiple times due to network retries or distributed system failures. The Art of the Upsert At its core, a streaming upsert (a portmanteau of “update” and “insert”) is the process of synchronising incoming data with an existing
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Streaming Without the Sting: Data Quality Rules That Never Break the Flow 💻 Did you know? A single minute of downtime in a high-velocity streaming environment can result in the loss of millions of data points, potentially costing a business thousands of pounds in missed opportunities or regulatory fines. — Defining Resilient Streaming Quality Data quality in
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The Silent Pipeline Killer: Surviving Schema Drift in the Wild 📊 Did you know? In the early days of big data, a single column change in a source database could trigger a “data graveyard” effect, where downstream analytics remained broken for weeks. The silent pipeline killer Schema drift occurs when the structure of source data changes
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The Invisible Erosion: Detecting and Managing Data Drift in Modern Architectures 📊 Did you know? According to recent industry surveys, over 70% of organisations experience significant data drift within the first six months of deploying a production system. The Concept of Data Drift Data drift occurs when the statistical properties or the underlying structure of incoming data change
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