Product Update Join Us Feb 29

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Data Observability and FinOps for Google Cloud BigQuery

Cloud Data Cost Observability Meets Optimization

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AI-DRIVEN CLOUD COST MANAGEMENT AND PERFORMANCE OPTIMIZATION FOR BIGQUERY

Unlocking BigQuery: Achieving Speed and Scale with Data Observability and FinOps

Unravel’s AI-driven cloud cost optimization for BigQuery delivers insights based on Unravel’s deep observability at the job, user, and code level to supply AI-driven cost optimization recommendations for slots and SQL queries, including slot provisioning, query duration, autoscaling efficiencies, and more. With Unravel, BigQuery users can speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads.

 

View Plans & Pricing as well as Get Started for Free

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Built for BigQuery

Increase data pipeline reliability and cost efficiency

Unravel’s AI-powered Insights Engine understands all the intricacies and complexities of BigQuery and the supporting infrastructure to optimize efficiency and performance.

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Visibility into BigQuery compute and storage spend

  • Visualize the breakdown of costs, including the average cost per project, job, user over time for compute and storage.
  • See both on-demand and reserved capacity pricing views.
  • Understand infrastructure as well as pricing structure usage and efficiencies by query, user, department, project.
  • Automatically get accurate, granular cost allocation, trend visualization, and forecasting.
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AI-driven cloud cost optimization for BigQuery

  • Receive automated cost optimization recommendations for slots and SQL:
    • Slot provisioning
    • Query duration
    • Autoscaling efficiencies
    • And more
  • Get predictive cost and performance insights of existing workloads prior to moving them to the cloud
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Visualization dashboards and unit costs

  • Get dozens of dashboards and alerts out-of-the-box
  • Customize dashboards and alerts with easy-to-use widgets to enable at-a-glance and drill down dashboards on
    • Spend
    • Performance
    • Unit economics
  • Build dashboards to show unit costs in terms of average cost per user, per project, and per job.
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Explore Unravel features

Try our self-guided tours and experience Unravel today.

Self-Guided Tour
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Specific Situations

Unravel for BigQuery use cases

Unravel for BigQuery provides a single source of truth to improve collaboration across functional teams and accelerates workflows for common use cases. Below are just a few examples of how Unravel helps BigQuery users for specific situations.

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Scenarios by Role

  • FinOps Practitioner

    Scenario: Understand what we pay for BigQuery down to the user/app level in real time, accurately forecast future spend with confidence.

    Benefits: Granular visibility at the project, job, and user level enables FinOps practitioners to perform cost allocation, estimate annual cloud data application costs, cost drivers, break-even, and ROI analysis.

  • FinOps Practitioner / Engineering / Operations

    Scenario: Identify the most impactful recommendations to optimize overall cost and performance.

    Benefits: AI-powered performance and cost optimization recommendations enable FinOps and data teams to rapidly upskill team members, implement cost efficiency SLAs, and optimize BigQuery pricing tier usage to maximize the company’s cloud data ROI.

  • Engineering Lead / Product Owner

    Scenario: Identify the most impactful recommendations to optimize the cost and performance of a project.

    Benefits: AI-driven insights and recommendations enable product and data teams to improve slot utilization, boost SQL query performance, leverage table partitioning and column clustering to achieve cost efficiency SLAs and launch more data jobs within the same project budget.

  • Engineering / Operations

    Scenario: Live monitoring with alerts.

    Benefits: Live monitoring with alerts speed MTTR and prevent outages before they happen.

  • Data Engineer

    Scenario: Debugging a job and comparing jobs.

    Benefits: Automatic troubleshooting guides data teams directly to the pinpoint the source of job failures down to the line of code or SQL query along with AI recommendations to fix it and prevent future issues.

  • Data Engineer

    Scenario: Identify expensive, inefficient, or failed jobs.

    Benefits: Proactively improve cost efficiency, performance, and reliability before deploying jobs into production. Compare two jobs side-by-side to find any metrics that are different between the two runs, even if the queries are different.

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Commonly asked questions

Are cloud compute and storage costs included in BigQuery?

Yes. BigQuery is serverless, meaning that you do not need to deploy compute or storage. BigQuery pricing includes compute and storage costs and depends on the type of analysis, type and amount of storage, data ingestion and other factors. You can also reserve compute capacity to reduce processing costs. Estimate BigQuery costs online with the Google Cloud pricing calculator.

 

Learn more about FinOps for data teams

Does Unravel support the new BigQuery Editions pricing?

Yes. Unravel supports both on-demand compute pricing and BigQuery Editions. For example, Unravel can help you improve cost efficiency by recommending a different pricing plan. Unravel can also recommend the minimum and maximum slot settings depending on your workload.

Can Unravel notify me about long running BigQuery SQL queries?

Yes. Unravel alerts can be generated by setting AutoActions. For example, you can use an AutoAction to notify you about a situation that requires manual intervention, such as resource contention or stuck jobs.

Learn more about automated troubleshooting

Does Unravel help me optimize SQL queries?

Yes. Unravel provides AI-powered recommendations to help you improve query efficiency. Optimal SQL queries enable you to accelerate results and serve more user requests with your existing BigQuery resources. For example, Unravel helps you make better use of partition and cluster keys, improve performance with partition pruning, and avoid common anti-patterns such as SELECT * on “wide” tables that contain a large number of infrequently-used columns.

Can Unravel receive BigQuery metadata automatically?

Yes. Unravel can be set up to automatically create and configure resources in more than 100 projects at a time. You can either add single projects or multiple projects at a time for Unravel monitoring. These projects can be added either with customer-supplied credentials or with Unravel-generated credentials.

Learn more about cloud cost management

Does Unravel include the ability to create new dashboards beyond the out-of-the-box dashboards?

Yes. You can create custom dashboards using a collection of pre-built components. Custom dashboards help you improve communication and collaboration between teams and give leaders visibility to key metrics.

Can I create custom alerts beyond the built-in alerts provided by Unravel?

Yes. You can create new alerts using a flexible set of criteria and communications platforms integrations. Custom alerts effectively provides an early warning system to alert you of resource usage spikes before it hits the cloud bill.

How does Unravel help when migrating to Google Cloud BigQuery?

Unravel provides granular Insights, recommendations, and automation for before, during and after your Spark, Hadoop and data migration to BigQuery. Unravel for BigQuery is a complete data observability platform to help you tune, troubleshoot, cost-optimize, and ensure data quality on BigQuery. Unravel provides AI-powered recommendations and automated actions to enable intelligent optimization of data pipelines and data applications.

Learn more about cloud migration

Can Unravel produce FinOps showback or chargeback reports for Google Cloud BigQuery?

Yes, Unravel includes showback/chargeback reports for BigQuery compute and storage costs by projects and users. You can specify the reporting time window to match your organization’s budgeting cycle.

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AI-POWERED DATA OBSERVABILITY & FINOPS

Accelerate lakehouse performance and efficiency.

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