Data Lake Cost Optimization

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February 2, 2024 by Updated April 16th, 2024

Conducting ad-hoc analytics, streaming analytics, and ML workloads in the cloud offers unique cost, performance, speed, time to value, and accessibility advantages. However, the amount of data in the cloud can be humongous, resulting in greater costs. Controlling the spiraling costs and applying specific business policies is critical for cloud-based data lake platform users.

Read on to discover the data lake feature that has saved our customers millions. It has stood the test of time and continues to help our customers discover clever cost savings, even when they thought there were none to be made.

With the evolving technologies and industry disruptions in a global world, businesses face the major challenge of streamlining their operations, so that they can move ahead of their competitors with great efficiency and effectiveness towards achieving cost excellence. This very requirement gives rise to cost optimization.

Want to save up to 42% on your data lake costs? Learn about Qubole Cost Explorer.

Why do Organizations need Data Lake Cost Optimization?

Organizations need cost optimization for various reasons. Some of the major aspects are:

  • Minimize Risks: To avoid any unnecessary expenses and mishaps, organizations need a cost-optimization strategy.
  • Minimize Costs: Cost minimization helps teams in reducing costs which may directly impact profits.
  • Maximize Business Value: It helps organizations stay one step ahead of their competitors by decreasing time to market & promoting innovation.

Cost optimization is the continuous process of maximizing business value through a business-focused drive to fully utilize resources and minimize costs by reducing sources of wasteful expenditure, underutilization of resources, or low return in the company’s IT budget both in the cloud and data centers. The practice aims to invest in new technology to speed up business growth or improve profit margins by maximizing savings and meeting business requirements. It includes:

  • Obtaining the best pricing and terms across business purchases to procure more resources for less.
  • Standardizing, simplifying, and rationalizing platforms, applications, processes, and services.
  • Automating and digitizing IT and business operations to reduce mismanaged or excess resources.
  • Aligning delivery of service to specific workloads and applications with the best customer experience.

Qubole Cost Explorer – Cloud Compute Cost Optimization, The Easy Way

Qubole Cost Explorer (QCE) allows you to monitor, manage, and control costs by providing granular visibility of your infrastructure spending at a job, cluster, or cluster instance level.

  • Account Level: Aggregate the dollar spend at an individual account level.
  • Pre-existing Tag Level: Attribute the dollar spend at the public cloud compute tag level.
  • User Level: Attribute the dollar spend at an individual user level.
  • Cluster Instance Level: Attribute the dollar spend at the cluster instance level.
  • Job Level: Attribute the dollar spend at an individual command level.
  • Cluster Level: Attribute the dollar spend at an individual cluster level.

Data Lake Cost Analysis with Qubole Cost Explorer

Qubole’s powerful automation empowers administrators to control their spending by optimizing resource consumption, deploying lower-priced resources, eliminating redundant resource consumption, and throttling queries based on monetary limits. It also provides governance through intelligent automation capabilities such as workload-aware autoscaling, intelligent spot management, heterogeneous cluster management, Qubole Cost Explorer, and automated cluster lifecycle management.

Qubole Cost Explorer helps organizations monitor, manage, and optimize big data costs by providing visibility of workloads at the job, cluster, and cluster instance levels. It also helps enterprises achieve a financial governance solution to monitor their workload expenditures using pre-built and customizable reports and visualizations.

Qubole Cost Explorer can help you avoid visiting the cloud optimization circus frequently. With Cost Explorer, you can track costs, monitor showbacks, justify business plans, prepare budgets, and build ROI analyses. Let’s see how.

  • Build ROI Analysis: Before being consumed by downstream applications such as reporting, dashboarding, or ML models, data goes through several stages of the pipeline. Each stage of the pipeline consumes a portion of the overall investment. Through job-level cost attribution, QCE can provide insights into the “investment” part of the ROI calculation.
  • Plan and Budget: With the job and user level cost attribution through QCE, customers can now go beyond the cluster, and actually, look into the workloads that are driving the consumption to project and track spending.
  • Monitor Showback: QCE provides cost attribution at the user level across multiple dimensions: aggregate, by command source, command type, etc. With this information, you can then map the users to their teams of business units to monitor the show back.
  • Justifying Business Cases: A sound financial governance practice requires frequent operational reviews on the steps taken to control cost. QCE makes this data readily available, such as savings from using AWS Spot Instances as part of Qubole clusters.

How Is Qubole Cost Explorer Delivered?

Qubole Cost Explorer provides flexibility and self-service querying capabilities, allowing users to generate custom insights above and beyond the packaged metrics delivered in QCE. Let’s understand the different stages in detail:

  • QCE data ingested into Data Lake: The data is directly ingested into a customer’s data lake. The QCE Data ownership resides with organizations for retention, custom applications, etc.
  • Materialized tables for self-service analytics: QCE data from the data lake is materialized as data tables for self-service analytics.
  • Data Access Policies: Customers can configure their own lifecycle policy on QCE data, and transfer it to cheaper storage for archival. They can determine which users have access to sensitive cost data. In addition to this, sensitive data such as cost and other billing information requires appropriate data access policies.
  • Data Retention: QCE data retention and storage policies are determined by the customers who can leverage the comprehensive suite of data governance, security, and privacy-related capabilities and configure their own data access policies.

Data Lake Cost Optimization with QCE

Qubole Cost Explorer provides multiple interfaces to access the data and make it readily available.

  • Out-of-Box Qubole Notebooks/Dashboards: Pre-built dashboards are uploaded as Notebooks to a customer’s Qubole account to allow users to interact with the dashboards and further analyze the data based on multiple dimensions.
  • Interactive SQL Queries through Qubole SQL Workbench: With QCE data tables and columns available right next to the SQL Composer, users can perform any ad-hoc data exploration by leveraging Qubole SQL Workbench to query this data using Hive, Spark, or Trino on Qubole.
  • BI Tools: With materialized tables, creating custom QCE dashboards with (Tableau, Looker, and other popular BI tools) for business users, is only a fingertip away.
  • Qubole SDK for Custom Applications: Organizations have their own needs and requirements with a cost analytics service, giving rise to several custom applications that need to be built on cost data. QCE activates all of these custom application use cases, by transferring the data ownership and storage to Qubole customers.

TCO Best Practices with Qubole Cost Explorer

Cloud data lakes are cost-effective, agile, and scalable, making Proof of Concepts easy to implement. However, over time, ad-hoc queries take longer, model iteration cycles increase, and resource usage can lead to wasteful expenditures.

This negatively impacts TCO and cost predictability. Qubole’s Cost Explorer provides pre-built and customizable reports and visualizations to help enterprises monitor their big data workloads and prevent wasteful spending. Teams can fully leverage the platform to realize business objectives and value with Cost Explorer.

Want to save up to 42% on your data lake costs? Learn about Qubole Cost Explorer.

Qubole is helping organizations regain control of costs for Big data processing in the cloud and succeed at their goals and business initiatives without overpaying. Qubole Cost Explorer provides enterprises with a financial governance solution to monitor their workload spending with pre-built and customizable reports and visualizations. With clarity in their cloud infrastructure costs, Data teams can effectively manage, control, and reduce them—regardless of the lifecycle stage or bursty nature of typical analytics and machine learning use cases.

To summarize, Qubole helps organizations save huge amounts of money with built-in platform capabilities and sustainable economics that allow your infrastructure to automatically scale up or down as per one’s requirement. To learn more about Qubole Cost Explorer, set up a time for the Qubole Cost Explorer demo with us.

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