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Fuel AI & ML on a Budget: A FinOps Data Strategy

Power your AI and ML initiatives without breaking the bank. Discover how to build a cost-effective data analytics foundation that accelerates model training and deployment

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Fuel AI & ML on a Budget: A FinOps Data Strategy

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  1. The FinOps Guide to Data Analytics How to Decouple Storage and Compute to Drastically Reduce Cloud Costs Starburst Consulting DataCouch

  2. Why Are Your Analytics Cloud Bills Escalating? The High Cost of Inefficiency The Architectural Trap Traditional cloud data warehouses tightly couple storage and compute. This means you can't scale one without paying for the other, leading to significant overprovisioning and wasted spend. Bad data and inefficiencies cost companies an average of $12.9 million annually. Knowledge workers spend an average of 12 hours a week just "chasing data" due to data silos and fragmented systems. The Hidden Cost of Data Movement Constant data movement, duplication, and ETL processes are a major bottleneck and a significant cost driver, both in terms of infrastructure and personnel. These hidden costs often go unnoticed until it's too late.

  3. Stop Moving Data. Start Querying It. Core Concept: Decouple Compute from Storage How It Works: The Federated Query Engine A modern architecture separates your query engine (compute) from your data storage (like a data lake). This allows you to scale compute resources up or down based on query demand, without altering your storage footprint, leading to massive cost efficiencies. A federated engine provides a single point of access to query all your data4wherever it lives4 across clouds, on-prem, and in different formats. This eliminates the need for costly and time-consuming data duplication and ETL pipelines, centralizing your data strategy.

  4. From Cost Center to Value Driver 12.7x 41% 10x Cost Savings SQL Analytics Savings Faster Queries Achieve up to 12.7x cost savings compared to traditional cloud data warehouses by eliminating redundant infrastructure and optimizing resource usage. Deliver faster insights with up to 10x faster queries, empowering data teams to focus on analysis instead of data wrangling. Realize 41% savings on SQL analytics and 80% savings on near real-time analytics by running queries more efficiently on open data lakehouse formats.

  5. Your 3-Step FinOps Framework for Data Analytics 01 02 Assess and Centralize Access Optimize and Govern Conduct an architecture audit to identify cost drivers in your current warehouse. Implement a federated query engine to create a single, unified data layer without moving data. Leverage open table formats like Apache Iceberg or Delta Lake to improve performance and reduce storage costs. Establish federated data governance to manage access and ensure compliance across all data sources. 03 Monitor and Scale Efficiently Use performance optimization services to fine-tune your environment for your specific workloads, ensuring maximum efficiency. Scale compute resources elastically to meet demand without over-provisioning.

  6. Start Your Journey to a Cost-Effective Data Strategy DataCouch Schedule a complimentary Architecture Audit to identify your key areas for cost savings. Website:https://datacouch.io/ Email:hello@datacouch.io Phone:+1 (518) 861-4949

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