How to Reduce Data Processing Costs

I found companies overpaying $50,000+ on Salesforce Data Cloud simply because they don't understand what truly drives costs. Everyone gets excited about Data Cloud's fancy features but ignores what's actually costing them money. If you don't understand the credit system, you're walking into a financial trap. The truth is simple: every action in Data Cloud costs credits. But some actions are budget killers. What's really emptying your wallet: -It's not just how much data you have—it's what you're doing with it -Sloppy data connections burn through credits like crazy -Poorly designed transformations are silent budget destroyers -Those "simple" activation tasks? They're often credit hogs The formula isn't complicated, just overlooked: (Records processed ÷ 1 Million) × Usage Type = What you're actually paying Smart teams do this first: start with the free version. You get 250,000 credits, one admin, five integration users, and 1TB storage without spending anything. But here's where most fail: they never track which specific operations eat the most credits. Your reports look great while your budget disappears. Want to slash your Data Cloud costs by 50%? Audit which operations are must-haves versus nice-to-haves. Then fix your biggest credit consumers first. Identify your three highest credit-consuming operations and share below. I'll help troubleshoot cost-efficient alternatives.

Cloud computing infrastructure costs represent a significant portion of expenditure for many tech companies, making it crucial to optimize efficiency to enhance the bottom line. This blog, written by the Data Team from HelloFresh, shares their journey toward optimizing their cloud computing services through a data-driven approach. The journey can be broken down into the following steps: -- Problem Identification: The team noticed a significant cost disparity, with one cluster incurring more than five times the expenses compared to the second-largest cost contributor. This discrepancy raised concerns about cost efficiency. -- In-Depth Analysis: The team delved deeper and pinpointed a specific service in Grafana (an operational dashboard) as the primary culprit. This service required frequent refreshes around the clock to support operational needs. Upon closer inspection, it became apparent that most of these queries were relatively small in size. -- Proposed Resolution: Recognizing the need to strike a balance between reducing warehouse size and minimizing the impact on business operations, the team developed a testing package in Python to simulate real-world scenarios to evaluate the business impact of varying warehouse sizes -- Outcome: Ultimately, insights suggested a clear action: downsizing the warehouse from "medium" to "small." This led to a 30% reduction in costs for the outlier warehouse, with minimal disruption to business operations. Quick Takeaway: In today's business landscape, decision-making often involves trade-offs.  By embracing a data-driven approach, organizations can navigate these trade-offs with greater efficiency and efficacy, ultimately fostering improved business outcomes. #analytics #insights #datadriven #decisionmaking #datascience #infrastructure #optimization https://lnkd.in/gubswv8k

How I Cut Cloud Costs by $300K+ Annually: 3 Real FinOps Wins When leadership asked me to “figure out why our cloud bill keeps growing Here’s how I turned cost chaos into controlled savings: Case #1: The $45K Monthly Reality Check The Problem: Inherited a runaway AWS environment - $45K/month with zero oversight My Approach: ✅ 30-day CloudWatch deep dive revealed 40% of instances at <20% utilization ✅ Right-sized over-provisioned resources ✅ Implemented auto-scaling for variable workloads ✅ Strategic Reserved Instance purchases for predictable loads ✅ Automated dev/test environment scheduling (nights/weekends off) Impact: 35% cost reduction = $16K monthly savings Case #2: Multi-Cloud Mayhem The Problem: AWS + Azure teams spending independently = duplicate everything My Strategy: ✅ Unified cost allocation tagging across both platforms ✅ Centralized dashboards showing spend by department/project ✅ Monthly stakeholder cost reviews ✅ Eliminated duplicate services (why run 2 databases for 1 app?) ✅ Negotiated enterprise discounts through consolidated commitments Impact: 28% overall reduction while improving DR capabilities Case 3: Storage Spiral Control The Problem: 20% quarterly storage growth, 60% of data untouched for 90+ days in expensive hot storage My Solution: 1, Comprehensive data lifecycle analysis 2, Automated tiering policies (hot → warm → cold → archive) 3, Business-aligned data retention policies 4, CloudFront optimization for frequent access 5, Geographic workload repositioning 6, Monthly department storage reporting for accountability Impact: $8K monthly storage savings + 45% bandwidth cost reduction ----- The Meta-Lesson: Total Annual Savings: $300K+ The real win wasn’t just the money - it was building a cost-conscious culture** where: - Teams understand their cloud spend impact - Automated policies prevent cost drift - Business stakeholders make informed decisions - Performance actually improved through better resource allocation My Go-To FinOps Stack: - Monitoring: CloudWatch, Azure Monitor - Optimization: AWS Cost Explorer, Trusted Advisor - Automation: Lambda functions for policy enforcement - Reporting: Custom dashboards + monthly business reviews - Culture: Showback reports that make costs visible The biggest insight? Most “cloud cost problems” are actually visibility and accountability problems in disguise. What’s your biggest cloud cost challenge right now? Drop it in the comments - happy to share specific strategies! 👇 FinOps #CloudCosts #AWS #Azure #CostOptimization #DevOps #CloudEngineering P.S. : If your monthly cloud bill makes you nervous, you’re not alone. These strategies work at any scale.

Excited to host today's Snowflake #Phoenix User Group Chapter Meeting where we’ll cover my top tips for optimizing Snowflake! Whether you’re new or experienced, these insights will help you ensure your platform stays efficient and ROI is maximized. Here’s a preview of the Top Tips we’ll discuss: 1. Auto-Scaling & Auto-Suspend: Automatically scale up/down and suspend warehouses when idle to avoid overprovisioning. 2. Query Result Caching: Speed up performance by using cached results, reducing the need to rerun queries. 3. Monitor Query Profiles: Regularly check query profiles to optimize slow-running or resource-heavy queries. 4. Right-Size Virtual Warehouses: Start small and scale up based on demand instead of over-allocating. 5. Clustering Keys: Use clustering to improve data retrieval speed for large datasets. 6. Minimize Data Movement: Avoid excessive data transfers between stages to reduce costs. 7. Zero-Copy Cloning: Efficiently create environments without data duplication. 8. Adjust Time Travel & Fail-Safe: Fine-tune these settings based on your data retention needs to lower storage costs. 9. Clean Up Unused Data: Regularly delete unused tables and objects to free up storage. 10. Resource Monitors: Set up resource monitors to cap usage and control runaway costs. The key is periodic monitoring and adjusting to meet your specific needs. Drop your favorite Snowflake optimization tip below 👇 #Optimization #CloudData #CostManagement #SnowflakeSuperhero #ROI #Datasuperhero #Snowflake_advocate

Enhancing SQL query efficiency is essential for improving database performance and ensuring swift data retrieval. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐨 𝐠𝐞𝐭 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭𝐞𝐝: 1. Use Appropriate Indexing 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Create indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. 𝐑𝐞𝐚𝐬𝐨𝐧: Indexes provide quick access paths to the data, significantly reducing query execution time. 2. Limit the Columns in SELECT Statements 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Specify only the necessary columns in your SELECT statements. 𝐑𝐞𝐚𝐬𝐨𝐧: Fetching only required columns reduces data transfer from the database to the application, speeding up the query and reducing network load. 3. Avoid Using SELECT 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Explicitly list the columns you need in your SELECT statement instead of using SELECT *. 𝐑𝐞𝐚𝐬𝐨𝐧: SELECT retrieves all columns, leading to unnecessary I/O operations and processing of unneeded data. 4. Use WHERE Clauses to Filter Data 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Filter data as early as possible using WHERE clauses. 𝐑𝐞𝐚𝐬𝐨𝐧: Early filtering reduces the number of rows processed in subsequent operations, enhancing query performance by minimizing dataset size. 5. Optimize JOIN Operations 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Use the most efficient type of JOIN for your scenario and ensure that JOIN columns are indexed. 𝐑𝐞𝐚𝐬𝐨𝐧: Properly indexed JOIN columns significantly reduce the time required to combine tables. 6. Use Subqueries and CTEs Wisely 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Analyze the execution plan of subqueries and Common Table Expressions (CTEs) and consider alternatives if performance issues arise. 𝐑𝐞𝐚𝐬𝐨𝐧: While simplifying complex queries, subqueries and CTEs can sometimes degrade performance if not used correctly. 7. Avoid Complex Calculations and Functions in WHERE Clauses 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Perform calculations or use functions outside the WHERE clause or use indexed columns for filtering. 𝐑𝐞𝐚𝐬𝐨𝐧: Calculations or functions in WHERE clauses can prevent the use of indexes, leading to full table scans. 8. Use EXPLAIN Plan to Analyze Queries 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Regularly use the EXPLAIN command to understand how the database executes your queries. 𝐑𝐞𝐚𝐬𝐨𝐧: The execution plan provides insights into potential bottlenecks, allowing you to optimize queries effectively. 9. Optimize Data Types 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨: Choose the most appropriate data types for your columns, such as using integer types for numeric data instead of strings. 𝐑𝐞𝐚𝐬𝐨𝐧: Proper data types reduce storage requirements and improve query processing speed. What other techniques would you suggest? If you found this helpful, feel free to... 👍 React 💬 Comment ♻️ Share #databases #sql #data #queryoptimization #dataanalytics

How to Lower LLM Costs for Scalable GenAI Applications Knowing how to optimize LLM costs is becoming a critical skill for deploying GenAI at scale. While many focus on raw model performance, the real game-changer lies in making tradeoffs that align with both technical feasibility and business objectives. The best developers don’t just fine-tune models—they drive leadership alignment by balancing cost, latency, and accuracy for their specific use cases. Here’s a quick overview of key techniques to optimize LLM costs: ✅ Model Selection & Optimization • Choose smaller, domain-specific models over general-purpose ones. • Use distillation, quantization, and pruning to reduce inference costs. ✅ Efficient Prompt Engineering • Trim unnecessary tokens to reduce token-based costs. • Use retrieval-augmented generation (RAG) to minimize context length. ✅ Hybrid Architectures • Use open-source LLMs for internal queries and API-based LLMs for complex cases. • Deploy caching strategies to avoid redundant requests. ✅ Fine-Tuning vs. Embeddings • Instead of expensive fine-tuning, leverage embeddings + vector databases for contextual responses. • Explore LoRA (Low-Rank Adaptation) to fine-tune efficiently. ✅ Cost-Aware API Usage • Optimize API calls with batch processing and rate limits. • Experiment with different temperature settings to balance creativity and cost. Which of these techniques (or a combination) have you successfully deployed to production? Let’s discuss! CC: Bhavishya Pandit #GenAI #Technology #ArtificialIntelligence

Why My Splunk Engagements Pay for Themselves In the world of data analytics, efficiency is key. When it comes to leveraging Splunk, ensuring your system is optimized can lead to significant cost savings and enhanced performance. Here’s why my Splunk engagements consistently pay for themselves: 1. Identifying Over-Allocation One of the common issues I encounter during Splunk engagements is over-allocation of resources. Companies often allocate more resources than necessary, leading to inflated costs without corresponding benefits. Through detailed analysis and fine-tuning, I help right-size these allocations, ensuring you’re only paying for what you truly need. 2. Correcting Improper Builds Improper builds can cause a multitude of issues, from slow performance to increased storage costs. By auditing your current setup, I identify and rectify these problems. This not only improves system performance but also reduces unnecessary expenses. 3. Addressing Bad Architecture A poorly designed Splunk architecture can be a drain on resources and finances. My engagements involve a thorough review of your architecture, pinpointing inefficiencies and implementing best practices. This results in a more streamlined, cost-effective system. 4. Reducing Data Volume Without Losing Fidelity One of the most impactful ways I help clients save money is by reducing the volume of data sent to Splunk. This doesn’t mean sacrificing data quality or fidelity. Instead, I utilize techniques to filter out unnecessary data and optimize the data ingestion process. The result is a leaner, more efficient data flow that maintains its integrity and usefulness while cutting down on costs. Real Results Here are a few examples of how my engagements have paid off: Case Study 1: A financial services company was able to reduce its Splunk licensing costs by 30% after addressing over-allocation and optimizing data flows. A $100,000 spend with me saved them 2 million USD/year. Case Study 2: An e-commerce firm saw a 40% improvement in system performance and a significant reduction in storage costs after correcting improper builds and redesigning its architecture. A $40,000 spend with me saved them $250,000 USD/year on storage alone. Case Study 3: A healthcare provider saved over $500,000 annually by implementing data filtering techniques that reduced their Splunk data volume by 25% without losing critical insights. Final price tag with me? One time fee of $50k. 27.5M saved over 10 years: Investing in my Splunk optimization services leads to tangible savings and performance improvements. By addressing over-allocation, improper builds, bad architecture, and optimizing data volume, my engagements ensure that your Splunk setup not only pays for itself but also delivers enhanced value and efficiency. If you're looking to maximize your Splunk investment, let’s connect and discuss how we can achieve these results for your organization. #splunk #savings #reduction #optimization #data

Let’s talk about optimizing log analysis and reducing costs associated with log processing. Here are 2 techniques I use: 1. Metricizing logs This technique involves turning logs into numbers. Instead of sifting through and storing voluminous log records, we capture the essence of our log data by preserving just the counts for various response codes, like the HTTP 400s and 500s. Think about it. Isn’t it easier to comprehend that we had a 1% error rate today compared to a mere 0.1% yesterday than rummaging through 100,000 log records from today versus the same number from yesterday? This not only saves us storage space but also enhances our ability to analyze and compare error rates effortlessly. But this technique does not account for the problems associated with latency distributions, which brings us to our 2nd technique. 2. Bucketing latency This approach allows us to better understand response times and system performance. To implement this technique, we create distinct buckets based on response time ranges. For instance, we might define buckets as less than a second, 1 to 10 seconds, 10 to 100 seconds, 100 to 1000 seconds, and more than 1000 seconds. Generally, it’s done in a log way rather than the linear way. However, I personally don't like this approach because the buckets are arbitrary. Instead, I prefer an approach that ensures the product of the number of requests and the corresponding time they took remains consistent across each bucket. This methodology draws inspiration from my experience in databases, where both short and long queries hold value in reduction efforts. If we have such buckets, suddenly, we know that shifting things one bucket to the left saves us about the same amount of time, whether it's something like: - introducing a result set cache for fast queries, or - introducing an algorithmic change for slow queries. This powerful approach extends not only to databases but various other systems, allowing us to understand what the system is actually doing and where the costs are. For instance, you can use it to address the spikes caused by network issues leading to retries. I’d love to engage with you in the comments. Let me know if you have any questions. #devops #reliability #logs

What'd happen with 2.1 B unnecessary API calls? It will be a STRUGGLE to manage it. Cloud costs will break the roof. Even then, Duolingo solved it. Here's what they did 👇 Background: Duolingo was wasting millions in unnecessary API calls. It was happening as features like stories, adventures, and DuoRadio scaled. Instead of patching up the problem, they reimagined their cost management strategy entirely, turning a challenge into an opportunity for efficiency. But the journey wasn’t without hurdles; they faced significant challenges: 1️⃣ Legacy systems waste resources. 2️⃣ Overprovisioning caused by poor defaults. 3️⃣ Staging environments costlier than production. So, what did Duolingo do? 🔹 Decommissioned unused resources: They eliminated outdated clusters, unused databases, and redundant microservices from deprecated features, reducing waste and reallocating resources to active workloads. 🔹 Enabled full cost visibility with CloudZero: Duolingo broke down cloud costs into queryable components, uncovering inefficiencies like staging environments costing more than production and identifying critical optimization opportunities. 🔹 Right-sized and optimized performance: They fine-tuned configurations for 90-95% memory utilization, migrated databases to a cloud-native, serverless data platform, and leveraged on-demand resources to maximize efficiency. The results? ✅ Service-to-service traffic dropped by 60% ✅ 20% reduction in cloud costs within months. ✅ Hundreds of thousands saved annually from optimizing a single service. The bottom line: When optimizing cloud infrastructure, focus on building visibility, cleaning up tech debt, and right-sizing resources. #AI #Duolingo #CaseStudy #Simform #GenAI P.S. 💡 In yesterday’s newsletter, I covered how Duolingo reimagined its cost management strategy. Subscribers get access to: - Product engineering insights. - Proven development strategies. - Latest Azure & Gen AI trends. Check it out! Link in comments ⬇️

A friend recently asked me what can you do to reduce your Snowflake bill? There are a lot of small tips and tricks but pattern wise I see just 3: 1. Make queries run faster. The faster they run, the fewer compute credits they consume. To make them run faster read up on Snowflake's micro-partitions and get very familiar with the Query Profiler. 2. Make queries run less frequently. If a dashboard only shows daily data there’s no need for it to run multiple times during the day. At a previous company 99.9% of our data was daily. We refreshed it over night. At another they needed data much more frequently so we ran stuff hourly or more frequently. We put a threshold in place for every query to finish in less than 25% the allotted time else we could put it in a slower cadence schedule 3. Use a smaller warehouse size. In SF warehouse size (aka compute resources) go from XS (1 credit/hr) to 6XL (512 credits/hr) in powers of 2 (1, 2, 4, 8, 16) There are a few other smaller factors involved like spin-up time. So if your query can run in a reasonable amount of time in a smaller warehouse, definitely use that.