Live from ICC 2025 in Sacramento: Greg Sloyer (Industry Principal, Manufacturing at Snowflake) joins the Energy Tech Podcast to break down how OT data from Ignition can flow—with context—into Snowflake using IoT Bridge, MQTT/Sparkplug, and Snowpipe Streaming, then combine with ERP/CRM data for real business outcomes. We dig into Cortex (semantic layer, NLQ, search over manuals/service records), DataOps/DevOps realities, and pragmatic analytics for energy: compression fleets, maintenance, and production trends.GuestGreg Sloyer, Industry Principal—Manufacturing, Snowflake.What you’ll learn- Why ICC 2025 is a “level up”: new venue, Ignition 8.3, PROVE IT- Edge → Cloud: UDT context, MQTT/Sparkplug, IoT Bridge & Snowpipe Streaming- OT + IT together: time series + ERP/CRM for bigger, multi-site problems- PoC fast paths for regional operators (free trials; zero-code landing)- Democratized analytics: run anomaly detection via SQL- Cortex: semantic layer, natural-language questions, and search over manuals & service history- Energy-specific cadence: hourly/daily/7-day moving averages vs sub-ms myths- Fleet view of compressors: MTTR/MTBF, asset health, and market-aware maintenance- When to use Snowpark/Streamlit vs NLQ for quick operator answersLinks & resources• Snowflake for Manufacturing & Energy: https://www.snowflake.com• IoT Bridge (with Cirrus Link): https://cirrus-link.com/• Ignition 8.3 (Inductive Automation): https://inductiveautomation.com/news/inductive-automation-releases-ignition-83• Opsite Energy – Insight-Driven Remote Operations: www.opsiteenergy.com00:10 Welcome to Energy Tech Podcast @ ICC 202500:26 Guest intro: Greg Slayer (Snowflake)00:49 Greg’s role: Industry Principal, Manufacturing01:44 ICC “level up”: new venue & more room for real OT/IT talks02:46 Why ICC 2025 matters: Ignition 8.3 + PROVE IT track03:50 Where Snowflake fits with Ignition & IA04:21 Getting shop-floor OT data into Snowflake (since 2023)05:03 IoT Bridge + MQTT/Sparkplug → OT+IT data together06:01 Edge-first: UDT context, Snowpipe Streaming, zero-code landing07:24 Context that business users understand (units, semantics)08:33 Add sensors/tags → auto-populate in Snowflake for data science09:05 Fast start for regional energy players (trials & PoCs)10:51 Democratized analytics: SQL anomaly detection on time series11:51 Modeling options, right cadence (hourly/shift vs sub-ms)12:42 Snowflake Cortex: semantic layer, NLQ, medallion patterns13:28 Energy cadence: 7-day moving averages, hourly/daily ops15:44 Maintenance reality: dispatch latency & practical “near real-time”16:28 Cortex Analyst & Search: manuals, service records, tech assist19:16 Compression fleet use case: asset health + market signals20:26 Fleet-level KPIs: MTTR/MTBF across sites & vendors21:01 Snowpark & Streamlit vs NLQ answers for ops22:32 “Advanced analytics,” not hype: optimization + ML in Snowpark24:26 Wrap-up, resources & how to try it; Like & SubscribeMusic: Uygar Duzgun / “Fast Life” / courtesy of www.epidemicsound.com