Fixing Cluster Sprawl with Data Platform Planning

𝗠𝗼𝘀𝘁 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸𝘀 𝗯𝗿𝗲𝗮𝗸 𝗹𝗼𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗵𝗶𝘁 𝘁𝗵𝗶𝘀 𝘀𝗰𝗮𝗹𝗲. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗱𝗶𝗱𝗻'𝘁 𝘀𝗲𝘁𝘁𝗹𝗲 𝗳𝗼𝗿 𝗼𝗳𝗳-𝘁𝗵𝗲-𝘀𝗵𝗲𝗹𝗳. 𝗧𝗵𝗲𝘆 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻. → 10 trillion samples ingested daily → 5 billion active timeseries — in real time → 70 cloud regions across AWS, Azure & GCP → 50× cheaper storage using the Databricks Lakehouse → 5× reduction in monitoring downtime 𝗧𝗵𝗿𝗲𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀 𝗺𝗮𝗱𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲: Pantheon (custom Thanos TSDB) · Telegraf aggregation shield · Hydra on Delta Lake The same Lakehouse principles that power your data and AI workloads can power your observability stack too. At Meshynix, we help enterprises build production-grade Databricks platforms on Azure — Spark Streaming, Delta Lake pipelines, lakehouse architecture, and AI-ready infrastructure from day one. Swipe through the carousel to see exactly how it works 👇 💬 Comment 𝗜𝗡𝗙𝗥𝗔 below if your monitoring stack is becoming a bottleneck. 🔗 𝗙𝘂𝗹𝗹 𝗯𝗹𝗼𝗴: 𝗵𝘁𝘁𝗽𝘀://𝘄𝘄𝘄.𝗱𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀.𝗰𝗼𝗺/𝗯𝗹𝗼𝗴/𝟭𝟬-𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻-𝘀𝗮𝗺𝗽𝗹𝗲𝘀-𝗱𝗮𝘆-𝘀𝗰𝗮𝗹𝗶𝗻𝗴-𝗯𝗲𝘆𝗼𝗻𝗱-𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹-𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴-𝗶𝗻𝗳𝗿𝗮-𝗱𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 #Databricks #DataEngineering #AzureDatabricks #Lakehouse #SparkStreaming #DeltaLake #Meshynix #Infrastructure #Observability

One small Databricks mistake can quietly increase cloud costs every single day. Leaving clusters running longer than needed. Early in my learning, I focused mostly on making pipelines work. But in cloud data engineering, working is not enough. Efficiency matters too. Here are 3 things I now always check 👇 ✅ Auto-termination settings   Clusters should shut down automatically after inactivity. ✅ Cluster sizing   Over-sized clusters = unnecessary compute cost. ✅ Workload scheduling   Not every job needs a large high-performance cluster. One important lesson I learned: Good Data Engineers don’t just build pipelines. They build cost-efficient pipelines. Performance + Reliability + Cost optimization all matter together. What’s one optimization practice you follow in Databricks? #DataEngineering #Databricks #Azure #CloudComputing #Lakehouse

Getting a massive Azure bill at the end of the month is one thing. Not knowing exactly which Databricks workloads caused it is another. Standard cloud cost management is great for high-level infrastructure, but it often treats your Databricks spend as a black box. You see the total cost, but isolating the specific Spark jobs, untagged workloads, or inefficient pipelines driving that spend is a completely different challenge. We are currently rolling out the Databricks Cost Optimisation Dashboard (COTS) across our managed environments to fix exactly this. By configuring the default system tables inside Databricks, we are enabling detailed, workload-level visibility. With this, FinOps and engineering teams will be able to leverage: 📆 Week-Over-Week Trend Analysis  Instantly spot cost anomalies, like a sudden spike in SQL compute, and validate that recent pipeline optimisations are actually reducing costs period-over-period. 📊 Usage by Product Tier Visualise your total spend across specific Databricks services (SQL, All-Purpose, Jobs) to ensure your compute usage aligns with your intended architectural strategy. 🏷️ Tag-Based Workload Tracking  Separate your compute spend by specific business functions (e.g. analytics vs. exploration) and easily isolate untagged or orphaned workloads. It’s about moving away from reactive monthly billing discussions and actually empowering your engineers to target and optimise the specific pipelines burning the most compute. If you'd like to understand how you can leverage COTS, head to the comments to reach out. #IgniteYourData #CostOptimization

One of the hidden challenges with modern data platforms and AI enablement is cost. Cloud platforms and services have made it incredibly easy to activate capability, scale compute, experiment with AI and deliver value quickly. That’s a good thing. But it also means organisations can accumulate significant cost without really noticing until the monthly bill arrives. We often see organisations successfully building modern platforms and delivering valuable use cases, while at the same time unnecessary compute, idle workloads, inefficient pipelines or poorly optimised usage patterns slowly erode the value being created. That’s why as part of our Platform Manage offering we monitor both high-level cloud consumption as well as detailed Databricks usage and compute patterns. Having a platform that creates value is one thing. Ensuring that value is not consumed by unnecessary platform cost is another. The goal isn’t simply to build modern platforms. It’s to build platforms that are sustainable, scalable and commercially effective over the long term. #databricks #dataops #finops

Hi all, I’m planning to share some content around enterprise-scale Azure and Databricks architectures in upcoming posts. One of the most expensive problems in cloud data platforms is poor Spark optimization. And many teams don’t realize it until infrastructure costs explode. Here are 5 Spark optimization techniques that actually matter in production: 1️⃣ Avoid unnecessary shuffles Shuffles are often the biggest performance killer in Spark jobs. 2️⃣ Partition intelligently Good partitioning improves parallelism and query performance. 3️⃣ Use Delta Lake Delta improves reliability, schema evolution, and performance optimization. 4️⃣ Cache only when necessary Over-caching can waste cluster memory and increase costs. 5️⃣ Monitor skewed data A single overloaded partition can slow the entire job. The goal is not only fast execution. The goal is: ✔ stable pipelines ✔ predictable performance ✔ lower cloud costs That’s what matters in enterprise-scale data platforms. #DataEngineering #Azure #Databricks #PySpark #CloudComputing #BigData

Manifest files really help especially when migrating data across cloud environments. Recently, I leveraged this approach in a pipeline I built. Instead of directly copying datasets, I generated a manifest file from the source cloud containing metadata and file references. This manifest was then transferred to the target cloud and used to seamlessly register the data in Databricks. This approach changed a few things for me: • No need for heavy data copying or duplication • Faster data availability in the target system • Clear separation between data movement and metadata handling • Better scalability for large datasets In short, the manifest acted as a bridge making the migration lightweight, efficient, and reliable. Curious are others using manifest driven approaches for cloud migrations, or still relying on traditional copy methods? #DataEngineering #CloudMigration #BigData #DataPipelines #ETL #ELT #Databricks #ApacheSpark #DataArchitecture #DataEngineeringLife #DataOps #CloudComputing #DataPlatform #AnalyticsEngineering #ModernDataStack #DataLake #DataLakehouse #DeltaLake #Metadata #DataManagement #ScalableSystems #DistributedSystems #CloudData #DataIntegration #PipelineEngineering #TechInnovation #AIEngineering #MachineLearning #DataScience #EngineeringLife #Automation #DataStrategy #DataInfrastructure #CloudArchitecture #DataGovernance #TechCommunity #LinkedInTech #LearnInPublic #BuildInPublic #TechPost

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

🚀 Learning Azure Storage Accounts (Data Engineering) Hi everyone 👋 I am currently exploring Azure Storage Accounts and how they support data engineering solutions. 💡 Example use cases I am learning: Data Lake (ADLS Gen2): storing raw → processed → curated data Blob Storage: logs, backups, and large datasets Queue Storage: managing data pipeline events Table Storage: storing metadata Curious to learn from others 👉 How do you design your storage strategy for data pipelines in Azure? also trying to understand best practices around Azure subscriptions 👇 👉 Do you use separate subscriptions for dev/test/prod? 👉 How do you manage cost and security across environments? Would love to hear your approach! #Azure #DataEngineering #BigData #Cloud

One thing I learned in Cloud: You don't need a perfect data pipeline. You need a trustworthy one. You spend weeks trying to build a flawless Bronze -> Silver -> Gold Medallion Architecture on Azure. Every layer is clean. Every transformation is documented. Every case is handled. Then a stakeholder asks for a one-off report, and you realise the pipeline I'd overengineered can't answer a simple business question without me rewriting half of it. The lesson: Build for the question, not for the architecture diagram. A "messy" pipeline that business stakeholders trust and use beats a perfect one that only the engineer understands. Medallion Architecture isn't a trophy. It's a tool. #medallion #architecture #cloud #azure #pipeline #data