Cost Management for Big Data

Imagine you have 5 TB of data stored in Azure Data Lake Storage Gen2 — this data includes 500 million records and 100 columns, stored in a CSV format. Now, your business use case is simple: ✅ Fetch data for 1 specific city out of 100 cities ✅ Retrieve only 10 columns out of the 100 Assuming data is evenly distributed, that means: 📉 You only need 1% of the rows and 10% of the columns, 📦 Which is ~0.1% of the entire dataset, or roughly 5 GB. Now let’s run a query using Azure Synapse Analytics - Serverless SQL Pool. 🧨 Worst Case: If you're querying the raw CSV file without compression or partitioning, Synapse will scan the entire 5 TB. 💸 The cost is $5 per TB scanned, so you pay $25 for this query. That’s expensive for such a small slice of data! 🔧 Now, let’s optimize: ✅ Convert the data into Parquet format – a columnar storage file type 📉 This reduces your storage size to ~2 TB (or even less with Snappy compression) ✅ Partition the data by city, so that each city has its own folder Now when you run the query: You're only scanning 1 partition (1 city) → ~20 GB You only need 10 columns out of 100 → 10% of 20 GB = 2 GB 💰 Query cost? Just $0.01 💡 What did we apply? Column Pruning by using Parquet Row Pruning via Partitioning Compression to save storage and scan cost That’s 2500x cheaper than the original query! 👉 This is how knowing the internals of Azure’s big data services can drastically reduce cost and improve performance. #Azure #DataLake #AzureSynapse #BigData #DataEngineering #CloudOptimization #Parquet #Partitioning #CostSaving #ServerlessSQL

How Shopify Reduced BigQuery Costs from $1,000,000 to $1,370 Per Month  Scaling systems often reveal inefficiencies, and Shopify’s experience with BigQuery is a masterclass in cost optimization. Here’s the story of how a small change made a big difference. In 2022, Shopify launched a new marketing tool powered by Apache Flink, which processed 1 billion rows of data for a select group of merchants.  To expand the tool for broader use, Shopify integrated Google BigQuery, a SQL-based external data warehouse, to handle queries on massive datasets and ensure scalability.  But there was a problem.  Each query scanned 75GB of data. At a rate of 60 queries per minute (their estimated demand), this would cost Shopify nearly $1,000,000 a month. Clearly, this wasn’t sustainable for general availability.  ► The Optimization: Clustering Shopify’s team explored clustering, a BigQuery feature that organizes data by frequently queried columns.  Why clustering works:   - It sorts data by specific columns (e.g., `DATE`, `REGION`) to limit the data scanned during queries.   - This ensures that queries only process relevant data blocks instead of the entire dataset.  By clustering their table around key columns used in their queries, Shopify reduced the data scanned per query from 75GB to just 0.1GB.  ► The Outcome: 99.9% Cost Reduction  After implementing clustering, Shopify achieved dramatic savings:  - Monthly costs dropped from $1,000,000 to $1,370.   - The query efficiency improved by over 150x, making the system scalable for general use.  ► The Process: What Shopify Did Right 1. Understanding Query Patterns: By analyzing the conditions in their WHERE clauses, they identified the best columns for clustering.  2. Iterative Testing: They ran multiple tests with clustered tables to confirm the reduction in scanned data.   3. Cost Awareness: By estimating query volumes and costs upfront, they pinpointed inefficiencies early.  Key Takeaways for Engineers  Optimizing data pipelines isn’t just about speed. it’s also about cost. Here’s how you can apply these lessons:  1. Clustering: Organize data by columns frequently used in queries to reduce scan costs.   2. Partitioning: Divide datasets into smaller segments (e.g., by time, geo, region) to minimize unnecessary processing.   3. Query Smarter: Avoid SELECT* statements; only fetch the columns you need.   4. Preview Data Wisely: Use table preview options instead of running expensive exploratory queries.  Reference blog: https://lnkd.in/gyt8ex-C

Imagine you’re filling a bucket from what seems like a free-flowing stream, only to discover that the water is metered and every drop comes with a price tag. That’s how unmanaged cloud spending can feel. Scaling operations is exciting, but it often comes with a hidden challenge of increased cloud costs. Without a solid approach, these expenses can spiral out of control. Here are important strategies to manage your cloud spending: ✅ Implement Resource Tagging → Resource tagging, or labeling, is important to organize and manage cloud costs. → Tags help identify which teams, projects, or features are driving expenses, simplify audits, and enable faster troubleshooting. → Adopt a tagging strategy from day 1, categorizing resources based on usage and accountability. ✅ Control Autoscaling → Autoscaling can optimize performance, but if unmanaged, it may generate excessive costs. For instance, unexpected traffic spikes or bugs can trigger excessive resource allocation, leading to huge bills. → Set hard limits on autoscaling to prevent runaway resource usage. ✅ Leverage Discount Programs (reserved, spot, preemptible) → For predictable workloads, reserve resources upfront. For less critical processes, explore spot or preemptible Instances. ✅ Terminate Idle Resources → Unused resources, such as inactive development and test environments or abandoned virtual machines (VMs), are a common source of unnecessary spending. → Schedule automatic shutdowns for non-essential systems during off-hours. ✅ Monitor Spending Regularly → Track your expenses daily with cloud monitoring tools. → Set up alerts for unusual spending patterns, such as sudden usage spikes or exceeding your budgets. ✅ Optimize Architecture for Cost Efficiency → Every architectural decision impacts your costs. → Prioritize services that offer the best balance between performance and cost, and avoid over-engineering. Cloud cost management isn’t just about cutting back, it’s about optimizing your spending to align with your goals. Start with small, actionable steps, like implementing resource tagging and shutting down idle resources, and gradually develop a comprehensive, automated cost-control strategy. How do you manage your cloud expenses?

Cloud costs are becoming the blind spot in digital transformation. A huge mistake is thinking cost control comes after deployment. Gartner, IDC, and regional surveys show the same thing: Cloud adoption is scaling, and so is waste. It raises hard questions for every delivery lead: How do we track value, not just spend? How do we forecast with accuracy? How do we stay cost-resilient across regions? It’s not about the cloud provider. It’s about the discipline behind it. And the reality: 94% of global organisations report cost overruns. Most common culprits? Idle compute. Unused storage. No tagging. No shutdown policies. Here’s why it keeps happening: → No unit cost ownership → No spend visibility at the service level → No roadmap alignment These aren’t random misses. They’re signs of a systemic problem: → Engineering owns infra ≫ not budgets → Finance owns totals ≫ not workloads → PMOs track milestones ≫ not consumption That’s why we use tools like: ⓘ AWS Cost Explorer to track EC2, S3, and Lambda usage ⓘ Azure Cost Management for daily anomaly alerts ⓘ GCP Billing for service-level granularity ⓘ CloudZero, Ternary, and nOps to push unit cost per job or user One UAE fintech cut idle compute by 37% in Q2 by tagging early, automating shutdowns, and publishing per-team cost scorecards. Cloud isn’t expensive. Lack of ownership is. الرؤية تسبق الوفورات. Savings follow visibility.

A startup just got hit with a $450,000 Google Cloud bill in just 45 days. Their normal monthly spend? $1,500. What happened? Their API key was compromised, resulting in 19 billion character translations. The worst part? They didn't know until the bill arrived. This isn't just about money - it's about survival. A $450K unexpected bill could sink most startups. Three critical lessons I've learned running cloud infrastructure: First, treat your API keys like your house keys. You wouldn't leave your front door unlocked, would you? Regular security audits, key rotation, and access reviews aren't optional anymore - they're essential hygiene. Second, cloud cost management isn't just about optimization - it's also about protection. Set up a layered budget and cost alert system. For a $1,500 monthly spend, you want alerts at: • 25% ($375) - Early warning • 50% ($750) - Mid-month check-in • 75% ($1,125) - Time to review usage • 100% ($1,500) - Monthly budget hit • Any sudden spike over 10% of daily average Third, and this is crucial for AI/ML workloads - implement usage quotas and rate limiting. AI services can rack up costs exponentially faster than traditional compute resources. One compromised endpoint can burn through your yearly budget in days. Quick checklist for everyone running cloud services: • Have you set up billing alerts? • When was your last security audit? • Are your API keys properly scoped and rotated? • Do you have rate limiting in place? • Is there a hard billing cap on your projects? Don't wait for a $450K surprise to start thinking about these. Prevention costs pennies compared to the cure. What's your take on cloud cost management? Have you had any close calls? Reddit post link: https://lnkd.in/diaSgC3B

🚀 Databricks Cost Reduction Strategies – Real Savings with Smart Optimization! 💰 💡 Interview Insight: Q: "Can you share some advanced strategies you've used to reduce costs, with examples and figures?" A: "Of course! Let’s explore some lesser-known yet highly effective cost optimization techniques." 🔥 Advanced Strategies That Delivered Real Savings 🔹 1️⃣ Optimizing Job Scheduling & Cluster Management ✅ Approach: Grouped jobs with similar resource needs and execution times, running them sequentially on the same cluster to minimize spin-ups and terminations. 📉 Impact: Before: Frequent cluster starts → $8,000/month After: Grouping reduced initialization by 50% → $5,000/month 💰 Savings: $3,000/month (37.5% reduction) 🔹 2️⃣ Dynamic Resource Allocation Based on Workload Patterns ✅ Approach: Analyzed workload trends to predict peak usage and dynamically adjusted cluster sizes, reducing over-provisioning during non-peak hours. 📉 Impact: Before: Over-provisioned clusters → $10,000/month After: Dynamic scaling → $6,000/month 💰 Savings: $4,000/month (40% reduction) 🔹 3️⃣ Optimized Job Execution Using Notebooks ✅ Approach: Modularized notebooks to avoid unnecessary execution, ran only essential parts, and reused cached results. 📉 Impact: Before: Full notebook execution → $7,000/month After: Selective execution → $4,500/month 💰 Savings: $2,500/month (35.7% reduction) 🔹 4️⃣ Incremental Data Processing to Cut Ingestion Costs ✅ Approach: Instead of processing full datasets, switched to incremental processing with Delta Lake to handle only data changes. 📉 Impact: Before: Full dataset processing → $12,000/month After: Incremental processing → $6,000/month 💰 Savings: $6,000/month (50% reduction) 🎯 Bonus: Storage Optimization 📦 By storing fewer interim datasets, storage costs dropped from $3,000/month to $1,800/month—a 40% reduction! 💭 Your Take? Which of these strategies have you tried? Any unique cost-saving techniques you’ve implemented? Let’s discuss in the comments! 👇 Follow Dattatraya shinde Connect 1:1 ? https://lnkd.in/egRCnmuR #Databricks #CostOptimization #CloudSavings #DataEngineering #FinOps #CloudCostManagement

Unlocking the Secrets of Cloud Costs: Small Tweaks, Big Savings! Three fundamental drivers of cost: compute, storage, and outbound data transfer. 𝐂𝐨𝐬𝐭 𝐎𝐩𝐬 refer to the strategies and practices for managing, monitoring, and optimizing costs associated with running workloads and hosting applications on provider’s infrastructure. 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐂𝐥𝐨𝐮𝐝 𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐂𝐨𝐬𝐭𝐬: 💡𝐑𝐢𝐠𝐡𝐭-𝐒𝐢𝐳𝐢𝐧𝐠 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: 📌 Ensure you're using the right instance type and size. Cloud providers offer tools like Compute Optimizer to recommend the right instance size. 📌 Implement auto-scaling to automatically adjust your compute resources based on demand, ensuring you're only paying for the resources you need at any given time. 💡𝐔𝐬𝐞 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬: 📌 Serverless solutions like AWS Lambda, Azure Functions, or Google Cloud Functions allow you to pay only for the execution time of your code, rather than paying for idle resources. 📌 Serverless APIs combined with functions can help minimize the need for expensive always-on infrastructure. 💡𝐔𝐭𝐢𝐥𝐢𝐳𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: 📌 If you're running containerized applications, services like AWS Fargate, Azure Container Instances, or Google Cloud Run abstract away the management of servers and allow you to pay for the exact resources your containers use. 📌 Use managed services like Amazon RDS, Azure SQL Database, or Google Cloud SQL to lower costs and reduce database management overhead. 💡𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 📌 Use the appropriate storage tiers (Standard, Infrequent Access, Glacier, etc.) based on access patterns. For infrequently accessed data, consider cheaper options to save costs. 📌 Implement lifecycle policies to transition data to more cost-effective storage as it ages. 💡𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐂𝐃𝐍𝐬): Using CDNs like Amazon CloudFront, Azure CDN, or Google Cloud CDN can reduce the load on your backend infrastructure and minimize data transfer costs by caching content closer to users. 💡𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐞𝐫𝐭𝐬: Set up monitoring tools such as CloudWatch, Azure Monitor etc. to track resource usage and set up alerts when thresholds are exceeded. This can help you avoid unnecessary expenditures on over-provisioned resources. 💡𝐑𝐞𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫 𝐌𝐮𝐥𝐭𝐢-𝐑𝐞𝐠𝐢𝐨𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬: Deploying applications across multiple regions increases data transfer costs. Evaluate if global deployment is necessary or if regional deployments will suffice, which can help save costs. 💡𝐓𝐚𝐤𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐨𝐟 𝐅𝐫𝐞𝐞 𝐓𝐢𝐞𝐫𝐬: Most cloud providers offer free-tier services for limited use. Amazon EC2, Azure Virtual Machines, and Google Compute Engine offer limited free usage each month. This is ideal for testing or running lightweight applications. #cloud #cloudproviders #cloudmanagement #costops #tech #costsavings

The mandate to deliver value comes with the responsibility of acting like a fiduciary. The capex-oriented financial budgeting practice — beloved by many Chief Financial Officers (CFOs) because of the ability to amortize physical resources to tax advantage — is no longer viable in a cloud and FinOps world. CFOs will need to adapt to an opex-oriented model and explore new optimization and budgetary best practices. This represents a fundamental and foundational shift from the last four decades of IT cost management and budgeting. Cloud is a massive budget item and leaders responsible for data, analytics and AI have to act accordingly. 🔮 Gartner predicts that by 2027, generative-AI-enhanced cost optimization will automate 40% of data and analytics spending in cloud-based data ecosystems. So data and analytics leaders need to: 1️⃣ Actively track and report cloud spending at the workload* level by acquiring appropriate tools and implementing best practices to use them across the financial and line-of-business organizations. 2️⃣ Introduce greater granularity to D&A budgets by linking specific workloads or projects to budget line items, and tracking cloud spend. 3️⃣ Introduce FinOps as an interactive discipline through a phased approach by continually evaluating workloads for their price/performance and value over time, and eliminating or optimizing those workloads that do not provide sufficient value for cost. 4️⃣ Establish explicit lines of communication between the offices of the CFO, CDAO and CIO by formalizing regular assessments of cloud spend and its business value. * "Workload" = a cohesive body of work that meets a specific business requirement. A workload may require a single cloud resource, or a set of cloud resources all working in tandem. For example, a business intelligence (BI) team may need to produce a set of reports each week that provide a snapshot view of the health of the business. These reports rely on multiple resources to produce their end-user-facing content: the data warehouse, the BI reporting tool and the data integration processes that load the data warehouse. Different workloads will require different sets of resources. The forward-thinking cloud practitioner will logically tag these resources for budget and alerting capabilities in the cloud. If #FinOps #Analytics #Data #AI #Cloud interests you as a Gartner client subscribe to our D&A research, check out our brand new research from my colleagues Adam Ronthal and Michael Gabbard: "Cloud Transition Requires CDAOs to Collaborate With CFOs" https://lnkd.in/ema98knP (requires client login)