3 Ways FinOps Optimizes Data Integration Costs and Performance

Cloud computing is an essential strategy for businesses seeking greater simplicity, flexibility and resilience in today’s digital landscape. However, according to research from McKinsey & Company, only 15% of organizations can establish a clear relationship between their cloud investments and business value delivery. With many cloud offerings available, the key challenge is to choose a configuration that meets performance needs while staying cost-effective.
 
Think of a typical data integration project that wants to meet its service level agreements (SLAs) while keeping costs down. It requires several key members — such as the admin, data engineer, cloud admin and finance team — to work together to optimize spending. These teams find themselves in an ongoing loop of planning, estimating, optimizing and monitoring. This iterative process, requiring continual adjustments, is both time-consuming and repetitive.

There’s a rising need for businesses to automate this process using the right FinOps tools to ensure that their teams have the bandwidth to focus on more pressing issues requiring their attention.

In this blog, you will hear about similar challenges facing businesses like yours and how Informatica FinOps can help you optimize performance and cost, followed by real-world use cases of customers leveraging FinOps to maximize efficiency.

You may face significant challenges when attempting to balance cloud costs with the need to meet SLAs. As you strive for optimal performance and financial efficiency, you may encounter a range of common issues, such as:

1. Rightsizing resources: Accurately sizing resources is critical to avoid over-provisioning, which results in wastage and unnecessarily increases costs, or under-provisioning, which can compromise SLAs.
2. Changing workload pattern: Cloud workloads are dynamic, with peak demand followed by low utilization. This requires ongoing adjustments to maintain performance and cost efficiency.
3. Complex pricing models: Cloud providers offer complex pricing models, such as on-demand, reserved and spot instances. This makes it challenging to predict expenses while maintaining performance without expert knowledge.

You need to be able to navigate these intricacies of scaling your cloud infrastructure to meet demand without overspending while maintaining the required SLAs. This delicate balancing act requires careful planning, monitoring and strategic decision-making, which can be time-consuming without the right FinOps tool.

Informatica FinOps is an advanced data integration tool designed to optimize resource management for cloud data integration. The primary objective of Informatica FinOps is to achieve financial accountability and cost efficiency with:

1. Cost allocation and optimization: Accurate and efficient allocation of cloud expenses to stay within budget while maximizing performance with recommendations for added efficiency and optimal times for executing workloads.
2. Cost transparency: Clear visibility into cloud costs by tracking, analyzing and reporting expenditures at a granular level, allowing businesses to set budgets and predict future cloud costs based on historical data and expected usage patterns.
3. Simplicity: Select cost/performance optimization and add budget details with a single click without the complexity of understanding cloud ecosystems and job compute requirements.

Informatica FinOps offers several key functionalities aimed at optimizing cloud resource management, including:

1. Automated configuration: Informatica FinOps automatically configures many cluster properties, allowing admins to have more control over resource costs with:
   • Cluster tuner - Dynamically updates job configurations for optimal operation using values provided by the admin
   • Cluster metric analyzer – Generates signals used by the Cluster Configuration Generator to decide optimal values for runtime properties such as the cluster node count and node types.
   • Cluster configuration generator - Decides on the best configurations dynamically based on collected metrics.
     
2. User recommendations: Generates recommendations to provide users with actionable items using:
   • Insight handler - Analyzes metrics and generates resource usage insights to help you make relevant changes.
   • Engine recommender - Provides information to help you choose the correct engine for a job by generating predictions about future runtime behavior.
3. Metrics provider: Collects and stores metrics from the cluster and the job at regular intervals. 
   

Figure 1: Informatica FinOps is powered by CLAIRE.

Businesses across industries leverage Informatica FinOps to tackle complex data and resource management challenges. These use cases highlight the role of FinOps in maximizing operational efficiency across verticals:

1. Large U.S. healthcare provider - Patient data and doctor’s notes are processed in near real-time, with strict SLAs for timely updates. Instead of manual intervention, cluster sizing to achieve target SLAs is now handled by the Informatica FinOps engine.
   
2. Major U.S. life insurance – Insurance files are processed with high concurrency needs as part of data platform modernization. Manually determining cluster size and node types to handle the concurrency is challenging due to the required expertise in Spark, Kubernetes, and cloud providers. Informatica FinOps simplifies this with a one-click solution, reducing the complexity.
   
3. Multinational bank - Hierarchical data is processed and transferred from one table in a Databricks Delta Lake to another data store. Based on data-dependent factors, pushing the processing to Delta Lake might be better than processing it on a cluster to avoid ingress and egress charges to the customer. Informatica FinOps automatically identifies the most cost-effective option.
   
   

Discover how Informatica FinOps can revolutionize your cloud resource management by providing cost-efficiency while maintaining peak performance with its advanced features.

Based on the paper Informatica FinOps for Cloud Data Integration by Atam Prakash Agrawal, Anant Mittal, Shivangi Srivastava, Michael Brevard, Valentin Moskovich and Mosharaf Chowdhury.