Most Databricks teams can see their costs. Explaining them is a different problem entirely. Visibility is having the numbers — DBU reports, cloud invoices, usage dashboards. Most teams have all of that. Explainability is being able to answer the questions those numbers raise: → Which job drove last month's spike? → Which team owns the spend? → Did that cluster optimisation actually reduce total cost — or just shift it from the Databricks bill to the cloud bill? Neither billing system answers those questions on its own. And bridging them takes more than a notebook. Our latest article breaks down exactly why — the structural reason the two systems can't reconcile themselves, and why having the data is not the same as having the answer. Full article linked in the comments → #Databricks #FinOps #DataEngineering #CostObservability #CloudCost #DataPlatform #lumin8
Databricks Cost Explainability Beyond Visibility
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We cut cloud costs by 80%!! 💸📉 The surprising part? The biggest saving wasn't from better code. 🙅♀️ It was from reading the bill properly. 📑🔍 We found: → Clusters running all night with no jobs on them. 🌙💤 → Data stored in CSV (on a distributed system, in 2022!). 📁🚫 → Jobs running one by one that could run in parallel. 🐢➡️🐎 Migrated to Databricks . Converted to Delta Lake. Fixed the cluster config. 🛠️✨ The Result: ✅ 80% cost reduction. ✅ 85% faster processing. Sometimes the best engineering is just paying attention. 🧠💡 What's the most surprising inefficiency you've found in a data platform? 👇 #Databricks #DataEngineering #CloudCost #BigData #ApacheSpark #DeltaLake #CloudOptimization #TorontoTech #TorontoDataEngineer
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Most enterprise data platform builds start with the vendor question. AWS or Azure? Snowflake or Databricks? The cloud and the engine are variables. The architecture is the constant. Bronze. Silver. Gold. Source data preserved as-is. Cleaned and conformed across systems. Curated and ready for serving. Runs on any cloud, with any engine, swappable layer by layer. 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽. Inventory the top 3 use cases. Pick one. 𝗣𝗿𝗼𝘃𝗲. Land Bronze. Promote one Gold use case end to end. 𝗦𝗰𝗮𝗹𝗲. Decide the compute engine when scope is real, not before. 90 days to first Gold. Not 12 months to a half-finished warehouse. The architecture stays the same. The platform choices are the variables. None of these decisions are one-way doors. 👉 If your team is six months into a data platform build with no shipped use case, the constraint is usually less about budget and more about sequence. 🔗 More on how we approach it → https://lnkd.in/eX7m8jHP #EnterpriseData #DataEngineering #DataFoundation #SmartData
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Most data platform conversations start in the wrong place. This is a better way to sequence it. #Snowflake #Databricks #enterprisedataplatforms #SmartData
Most enterprise data platform builds start with the vendor question. AWS or Azure? Snowflake or Databricks? The cloud and the engine are variables. The architecture is the constant. Bronze. Silver. Gold. Source data preserved as-is. Cleaned and conformed across systems. Curated and ready for serving. Runs on any cloud, with any engine, swappable layer by layer. 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽. Inventory the top 3 use cases. Pick one. 𝗣𝗿𝗼𝘃𝗲. Land Bronze. Promote one Gold use case end to end. 𝗦𝗰𝗮��𝗲. Decide the compute engine when scope is real, not before. 90 days to first Gold. Not 12 months to a half-finished warehouse. The architecture stays the same. The platform choices are the variables. None of these decisions are one-way doors. 👉 If your team is six months into a data platform build with no shipped use case, the constraint is usually less about budget and more about sequence. 🔗 More on how we approach it → https://lnkd.in/eX7m8jHP #EnterpriseData #DataEngineering #DataFoundation #SmartData
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We’ve started putting together a tiny index of agentic data tools. Skills, MCPs, and CLIs organized across the data stack: Microsoft Fabric, Databricks, Snowflake, dbt, duckdb, and more. Cloud providers too. 👉 GitHub: https://lnkd.in/etHizZPi #datasystems #aisystems
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Snowflake is not a database. It is a cloud data platform with elastic compute, credit-based pricing, and an expanding ecosystem of dbt, Snowpipe, Tasks, and Streams. So why do most observability tools still monitor it like it is Postgres? #PRIZM was built from the metadata up to understand how Snowflake actually works. Criticality-aware profiling means your CFO's revenue table gets deep checks. The forgotten staging table from 2019 gets nothing. Your credit bill stays predictable even as your catalog grows into tens of thousands of assets. We broke down the 7 layers you actually need to observe and the 10 capabilities to demand before you sign with any vendor. https://lnkd.in/djYSuUpj #DataObservability #Snowflake #DataQuality #AIReadyData #DataEngineering #PRIZM
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"Started exploring Snowflake today — a cloud data warehouse that's widely used in modern data engineering! Key thing I learned — unlike traditional databases, Snowflake separates storage and compute. This means teams can query data independently without slowing each other down! #DataEngineering #Snowflake #LearningInPublic #CloudData"
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If your team is dealing with fragmented data environments, runaway BigQuery costs, or pipelines that underperform at scale, this session is worth your time. On May 29, Jellyfish Training (Google's North American Cloud Training Partner of the Year) is running a full-day virtual session: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 Google Cloud. Built for data architects and engineers, the day covers Knowledge Catalog (formerly Dataplex) and Data Mesh architecture, BigQuery workload management and pricing, Dataflow and batch pipeline tuning, and a FinOps module on budgets and alerting. Two hands-on labs are built into the schedule. Participants who complete the training earn a digital Credly "Enterprise Data Efficiency" badge. 📅 May 29, 2026 | 9:00 AM – 5:00 PM CDT | Virtual Participation is limited to keep the experience hands-on and high-impact. 🔗 Register here ➡ https://lnkd.in/e_H6UAJu #GoogleCloud #DataEngineering
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If your team is dealing with fragmented data environments, runaway BigQuery costs, or pipelines that underperform at scale, this session is worth your time. On May 29, Jellyfish Training (Google's North American Cloud Training Partner of the Year) is running a full-day virtual session: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 Google Cloud. Built for data architects and engineers, the day covers Knowledge Catalog (formerly Dataplex) and Data Mesh architecture, BigQuery workload management and pricing, Dataflow and batch pipeline tuning, and a FinOps module on budgets and alerting. Two hands-on labs are built into the schedule. Participants who complete the training earn a digital Credly "Enterprise Data Efficiency" badge. 📅 May 29, 2026 | 9:00 AM – 5:00 PM CDT | Virtual Participation is limited to keep the experience hands-on and high-impact. 🔗 Register here ➡ https://lnkd.in/dUsEpXcW #GoogleCloud #DataEngineering
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If your team is dealing with fragmented data environments, runaway BigQuery costs, or pipelines that underperform at scale, this session is worth your time. On May 29, Jellyfish Training (Google's North American Cloud Training Partner of the Year) is running a full-day virtual session: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 Google Cloud. Built for data architects and engineers, the day covers Knowledge Catalog (formerly Dataplex) and Data Mesh architecture, BigQuery workload management and pricing, Dataflow and batch pipeline tuning, and a FinOps module on budgets and alerting. Two hands-on labs are built into the schedule. Participants who complete the training earn a digital Credly "Enterprise Data Efficiency" badge. 📅 May 29, 2026 | 9:00 AM – 5:00 PM CDT | Virtual Participation is limited to keep the experience hands-on and high-impact. 🔗 Register here ➡ https://lnkd.in/eDBaZijz #GoogleCloud #DataEngineering
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If your team is dealing with fragmented data environments, runaway BigQuery costs, or pipelines that underperform at scale, this session is worth your time. On May 29, Jellyfish Training (Google's North American Cloud Training Partner of the Year) is running a full-day virtual session: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 Google Cloud. Built for data architects and engineers, the day covers Knowledge Catalog (formerly Dataplex) and Data Mesh architecture, BigQuery workload management and pricing, Dataflow and batch pipeline tuning, and a FinOps module on budgets and alerting. Two hands-on labs are built into the schedule. Participants who complete the training earn a digital Credly "Enterprise Data Efficiency" badge. 📅 May 29, 2026 | 9:00 AM – 5:00 PM CDT | Virtual Participation is limited to keep the experience hands-on and high-impact. 🔗 Register here ➡ https://lnkd.in/du5PBW5X #GoogleCloud #DataEngineering
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